Oil and fat deterioration detection device, oil and fat deterioration detection system, oil and fat deterioration detection method, and oil and fat deterioration detection program

Abstract

Provided are a fat and oil deterioration degree detection device, a fat and oil deterioration degree detection system, a fat and oil deterioration degree detection method, and a fat and oil deterioration degree detection program that are capable of highly accurately and easily detecting various deterioration indicators of fat and oil.　A cloud 8 serves as this fat and oil deterioration degree detection device that detects the degree of deterioration of deep-frying oil P on the basis of a polar compound amount of the deep-frying oil P. The cloud 8 comprises: a storage unit 82 that stores a correlation between the polar compound amount PCn of the deep-frying oil P and a prescribed deterioration indicator DIn thereof other than the polar compound amount; a data acquisition unit 81 that acquires a measurement of the polar compound amount; a deterioration indicator calculation unit 83 that calculates the prescribed deterioration indicator DIn on the basis of the correlation stored in the storage unit 82 and the measurement of the polar compound amount acquired by the data acquisition unit 81; and a detection result output unit 84 that outputs the prescribed deterioration indicator DIn calculated by the deterioration indicator calculation unit 83 as a detection result for the deterioration degree of the deep-frying oil P.

Description

[Technical Field]

[0001] The present invention relates to an oil and fat deterioration degree detection device, an oil and fat deterioration degree detection system, an oil and fat deterioration degree detection method, and an oil and fat deterioration degree detection program for detecting the degree of deterioration of oils and fats. [Background technology]

[0002] Deep-frying, a cooking method that uses edible oil, a type of fat, to fry food is widely known as one of many cooking methods. However, in order to maintain the quality of fried food produced by deep-frying, it is necessary to properly manage the quality of the edible oil used for deep-frying (hereinafter referred to as "deep-frying oil"). Deep-frying oil deteriorates as oxidation progresses with increasing usage time and frequency of use for deep-frying. Therefore, restaurants and retail stores that serve deep-fried food, in particular, use appropriate indicators to understand the degree of deterioration of deep-frying oil (hereinafter simply referred to as "deterioration level"), and deep-frying oil that has reached the discard standard is discarded and replaced with new deep-frying oil.

[0003] Indicators of the degree of deterioration of oils and fats (hereinafter referred to as "deterioration indicators") include, for example, color, acid value (AV), total polar compound content (TPM), viscosity increase rate, anisidine value, carbonyl value, smoke point, tocopherol content, iodine value, refractive index, volatile component content, and volatile component composition. These deterioration indicators can be measured using various sensors and imaging devices.

[0004] For example, Patent Document 1 discloses a method for measuring the acid value of a target oil by simultaneously photographing a color test piece immersed in a target oil and a color bar composed of multiple colors corresponding to the acid value with a camera, calculating the RGB color information of the color test piece and the RGB color information of the color bar from the captured images, and referring to the acid value corresponding to the calculated RGB color information of the color bar to determine the RGB color information of the target oil.

[0005] However, when measuring the "acid value" of oils and fats using the method described in Patent Document 1, the measurement procedure is complicated. For example, the person measuring must first immerse a color test piece in the target oil or fat, then leave the immersed test piece undisturbed for about 30 seconds, and then photograph it with a camera so that the color test piece and the color bar are in the same field of view. Furthermore, since the measurement method (for example, how to photograph with a camera) varies from person to person, errors are likely to occur in the measurement results.

[0006] Therefore, as an indicator of oil and fat deterioration that can be measured more simply and accurately than measuring the "acid value," for example, "amount of polar compounds" can be mentioned. Patent Document 2 discloses a method for measuring the capacitance in oil and fat by immersing a sensor having an electrode part in the oil and fat, and detecting the TPM value, which indicates the amount of polar compounds (polar molecular weight) contained in the oil and fat, from the measurement result. When measuring the amount of polar compounds in oil and fat using the method described in Patent Document 2, the person performing the measurement only needs to immerse the sensor in the oil and fat, making the measurement operation very simple, and the measurement result is highly accurate as it is less prone to errors by the person performing the measurement. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2020-38207 [Patent Document 2] Patent No. 6395243 [Overview of the project] [Problems that the invention aims to solve]

[0008] In Japan, "acid value" has conventionally been adopted as an indicator showing waste oil standards. For example, in the case of edible oil for business use such as general cooking oil and school lunches, guidance has been given to replace the edible oil at a reference value of "acid value 2.5" according to the hygiene norms of the Ministry of Health, Labour and Welfare. Therefore, in restaurants, retail stores, etc., it is difficult to adopt a method for measuring the "amount of polar compounds" as described in Patent Document 2, and currently, mainly a method for measuring the "acid value" as described in Patent Document 1 is adopted. Also, "color", which is easy to judge visually, is often used as a deterioration indicator, and in other cases, various deterioration indicators such as "viscosity increase rate" may be adopted.

[0009] When a deterioration indicator that is likely to cause an error in the measurement result is adopted as an edible oil deterioration indicator, the store side often sets a value with a margin from the reference value (for example, "acid value 2.0" in the case of "acid value") as the threshold at the time of waste oil, considering that the measured value contains an error. As a result, there is a possibility that edible oil that could originally still be used is wasted. Also, when a deterioration indicator with a complicated measurement operation is adopted as an edible oil deterioration indicator, the store side will require a certain amount of time to detect the degree of deterioration of the edible oil, and there is a possibility that the work efficiency will decrease.

[0010] Therefore, an object of the present invention is to provide an oil and fat deterioration degree detection device, an oil and fat deterioration degree detection system, an oil and fat deterioration degree detection method, and an oil and fat deterioration degree detection program capable of simply and highly accurately detecting various deterioration indicators of oils and fats.

Means for Solving the Problems

[0011] [1] The present invention relates to an oil and fat deterioration detection device for detecting the degree of deterioration of an oil and fat based on the amount of polar compounds in the oil and fat, which is one of the deterioration indicators of an oil and fat, and is characterized by comprising: a storage unit that stores a correlation between the amount of polar compounds and a predetermined deterioration indicator other than the amount of polar compounds; a data acquisition unit that acquires a measured value of the amount of polar compounds; a deterioration indicator calculation unit that calculates the predetermined deterioration indicator based on the measured value of the amount of polar compounds acquired by the data acquisition unit and the correlation stored in the storage unit; and a detection result output unit that outputs the predetermined deterioration indicator calculated by the deterioration indicator calculation unit as the detection result of the degree of deterioration.

[0012] [2] Preferably, the oil and fat deterioration detection device described in [1] above, wherein the correlation is a correlation formula in which the predetermined deterioration index is expressed as a polynomial of the amount of polar compound.

[0013] [3] Preferably, the oil and fat deterioration degree detection device described in [2] above, wherein the correlation formula is a linear formula represented by the following formula (1) or a quadratic formula represented by the following formula (2), where PC is the amount of polar compound, DI is the predetermined deterioration index, and n is an arbitrary heating time of the oil and fat. DIn = α × (PCn) + β···(1) α: Linear coefficient of PCn β: constant DIn = γ × (PCn) 2 +δ×(PCn)+ε···(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant

[0014] [4] Preferably, the oil deterioration detection device described in [3] above, wherein the oil is edible oil for cooking food ingredients, and the linear coefficient α and constant β included in formula (1), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (2) are each set to values ​​corresponding to the amount of fried food per unit time that is fried using the edible oil.

[0015] [5] Preferably, the oil deterioration detection device described in [3] above, wherein the oil is edible oil for cooking food ingredients, and the storage unit stores as the correlation formula a linear formula represented by the following formula (3) obtained by subtracting an air heating variable EH1, which is set considering air heating where only the oil is heated without cooking the food ingredients, from formula (1), or a quadratic formula represented by the following formula (4), which is obtained by subtracting an air heating variable EH2, which is set considering air heating, from formula (2). DIn = α × (PCn) + β - EH1···(3) α: Linear coefficient of PCn β: constant EH1: Air heating variable DIn = γ × (PCn) 2 +δ×(PCn)+ε-EH2···(4) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant EH2: Air heating variable The deterioration index calculation unit is characterized in that, when the oil is subjected to the air heating, it calculates the predetermined deterioration index using formula (3) or formula (4) stored in the memory unit.

[0016] [6] Preferably, the oil deterioration degree detection device described in [3] above, characterized in that the linear coefficient α and constant β of formula (1), and the quadratic coefficient γ, linear coefficient δ, and constant ε of formula (2) are each set to values ​​corresponding to the type of oil.

[0017] [7] Preferably, the oil deterioration detection device described in [6] above, wherein the oil is classified into a first type and a second type according to the fatty acid composition constituting the oil, the first type is an oil having a composition in which the oleic acid content is greater than the linoleic acid content, and the second type is an oil having a composition in which the oleic acid content is less than or equal to the linoleic acid content, and the memory unit is set to a value corresponding to the first type as the linear coefficient α of formula (1). A linear equation represented by the following formula (5), in which α1 is set to a value corresponding to the first oil type as the constant β in formula (1), or a quadratic equation represented by the following formula (6), in which γ1 is set to a value corresponding to the first oil type as the quadratic coefficient γ in formula (2), δ1 is set to a value corresponding to the first oil type as the linear coefficient δ in formula (2), and ε1 is set to a value corresponding to the first oil type as the constant ε in formula (2), DIn = α1 × (PCn) + β1 ... (5) α1: Linear coefficient of PCn β1: Constant DIn = γ1 × (PCn) 2 +δ1×(PCn)+ε1···(6) γ1: Quadratic coefficient of PCn δ1: Linear coefficient of PCn ε1: constant A linear equation represented by the following formula (7) containing α2, which is set to a value corresponding to the second oil type as the linear coefficient α of formula (1), and β2, which is set to a value corresponding to the second oil type as the constant β of formula (1), is stored as the correlation formula. Alternatively, a quadratic equation represented by the following formula (8) containing γ2, which is set to a value corresponding to the second oil type as the quadratic coefficient γ of formula (2), δ2, which is set to a value corresponding to the second oil type as the linear coefficient δ of formula (2), and ε2, which is set to a value corresponding to the second oil type as the constant ε of formula (2), is stored as the correlation formula. DIn = α² × (PCn) + β²···(7) α2: Linear coefficient of PCn β2: Constant DIn = γ² × (PCn) 2+δ²×(PCn)+ε²···(8) γ2: Quadratic coefficient of PCn δ²: Linear coefficient of PCn ε²: constant The deterioration index calculation unit is characterized in that, when the oil is a first type of oil, it calculates a predetermined deterioration index using formula (5) or formula (6) stored in the storage unit, and when the oil is a second type of oil, it calculates a predetermined deterioration index using formula (7) or formula (8) stored in the storage unit.

[0018] [8] Preferably, the oil deterioration detection device described in [6] above, wherein the oil is classified into a third type and a fourth type according to the iodine value of the oil, the third type is an oil type in which the iodine value of the oil is less than a predetermined iodine value threshold, and the fourth type is an oil type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, and the storage unit sets α3, which is set to a value corresponding to the third type as the linear coefficient α of formula (1), in formula (1) A linear equation represented by the following formula (9), each including β3, which is set as the constant β according to the third type of oil, or a quadratic equation represented by the following formula (10), each including γ3, which is set as the quadratic coefficient γ of formula (2) according to the third type of oil, δ3, which is set as the linear coefficient δ of formula (2) according to the third type of oil, and ε3, which is set as the constant ε of formula (2) according to the third type of oil, DIn = α³ × (PCn) + β³···(9) α3: Linear coefficient of PCn β3: Constant DIn = γ³ × (PCn) 2 +δ3×(PCn)+ε3···(10) γ3: Quadratic coefficient of PCn δ3: Linear coefficient of PCn ε3: constant The following linear equation, represented by formula (11), includes α4, set as the linear coefficient α of formula (1) to a value corresponding to the fourth oil type, and β4, set as the constant β of formula (1) to a value corresponding to the fourth oil type, or the following quadratic equation, represented by formula (12), includes γ4, set as the quadratic coefficient γ of formula (2) to a value corresponding to the fourth oil type, δ4, set as the linear coefficient δ of formula (2) to a value corresponding to the fourth oil type, and ε4, set as the constant ε of formula (2) to a value corresponding to the fourth oil type, and is stored as the correlation formula. DIn = α⁴ × (PCn) + β⁴···(11) α4: Linear coefficient of PCn β4: Constant DIn = γ₄ × (PCn) 2 +δ₄×(PCn)+ε₄···(12) γ4: Quadratic coefficient of PCn δ4: Linear coefficient of PCn ε4: constant The deterioration index calculation unit is characterized in that, when the oil is the third type of oil, it calculates the predetermined deterioration index using formula (9) or formula (10), and when the oil is the fourth type of oil, it calculates the predetermined deterioration index using formula (11) or formula (12).

[0019] [9] Preferably, the oil degradation detection device described in [6] above, wherein the oil is classified into a fifth oil type and a sixth oil type according to the CDM value of the oil, the fifth oil type is an oil type in which the CDM value of the oil is equal to or greater than a predetermined CDM threshold, and the sixth oil type is an oil type in which the CDM value of the oil is less than the predetermined CDM threshold, and the storage unit contains a linear formula represented by the following formula (13) which includes α5 set as the linear coefficient α of formula (1) to a value corresponding to the fifth oil type, and β5 set as the constant β of formula (1) to a value corresponding to the fifth oil type, or a quadratic formula represented by the following formula (14) which includes γ5 set as the quadratic coefficient γ of formula (2) to a value corresponding to the fifth oil type, δ5 set as the linear coefficient δ of formula (2) to a value corresponding to the fifth oil type, and ε5 set as the constant ε of formula (2) to a value corresponding to the fifth oil type, DIn = α5 × (PCn) + β5···(13) α5: Linear coefficient of PCn β5: Constant DIn = γ5 × (PCn) 2 +δ5×(PCn)+ε5···(14) γ5: Quadratic coefficient of PCn δ5: Linear coefficient of PCn ε5: constant A linear equation represented by the following formula (15) containing α6, which is set to a value corresponding to the sixth oil type as the linear coefficient α of formula (1), and β6, which is set to a value corresponding to the sixth oil type as the constant β of formula (1), is stored as the correlation formula. Alternatively, a quadratic equation represented by the following formula (16) containing γ6, which is set to a value corresponding to the sixth oil type as the quadratic coefficient γ of formula (2), δ6, which is set to a value corresponding to the sixth oil type as the linear coefficient δ of formula (2), and ε6, which is set to a value corresponding to the sixth oil type as the constant ε of formula (2), is stored as the correlation formula. DIn = α6 × (PCn) + β6 ... (15) α6: Linear coefficient of PCn β6: Constant DIn = γ6 × (PCn) 2 +δ6×(PCn)+ε6···(16) γ6: Quadratic coefficient of PCn δ6: Linear coefficient of PCn ε6: constant The deterioration index calculation unit is characterized in that, when the oil is the fifth type of oil, it calculates the predetermined deterioration index using formula (13) or formula (14), and when the oil is the sixth type of oil, it calculates the predetermined deterioration index using formula (15) or formula (16).

[0020]

[10] Preferably, the oil and fat deterioration detection device described in [6] above, wherein the oil and fat are classified into seventh oil type and eighth oil type according to the lipid molecular species in the oil and fat, the seventh oil type is an oil type in which the content of lipid molecular species in the oil and fat is greater than a predetermined content threshold, the rate of increase of the content of diacylglycerol in the oil and fat due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the content of free fatty acids in the oil and fat due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the content of triacylglycerol in the oil and fat due to heating is less than or equal to a predetermined decrease rate threshold, the eighth oil type is an oil type in which the content of lipid molecular species in the oil and fat is less than or equal to the predetermined content threshold, the rate of increase of the content of diacylglycerol in the oil and fat due to heating is greater than the predetermined first increase rate threshold, and the oil and fat An oil type in which the rate of increase of the free fatty acid content is greater than the predetermined second rate of increase threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is greater than the predetermined rate of decrease threshold, wherein the storage unit contains a linear equation represented by the following formula (17) which includes α7, set as the linear coefficient α of formula (1) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (1) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (18) which includes γ7, set as the quadratic coefficient γ of formula (2) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (2) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (2) to a value corresponding to the seventh oil type, DIn = α7 × (PCn) + β7 ... (17) α7: Linear coefficient of PCn β7: Constant DIn = γ7×(PCn) 2 +δ7×(PCn)+ε7 ··· (18) γ7: Quadratic coefficient of PCn δ7: Linear coefficient of PCn ε7: Constant The linear expression represented by the following formula (19) including α8 set to a value corresponding to the 8th oil type as the linear coefficient α in the formula (1), and β8 set to a value corresponding to the 8th oil type as the constant β in the formula (1), or the quadratic expression represented by the following formula (20) including γ8 set to a value corresponding to the 8th oil type as the quadratic coefficient γ in the formula (2), δ8 set to a value corresponding to the 8th oil type as the linear coefficient δ in the formula (2), and ε8 set to a value corresponding to the 8th oil type as the constant ε in the formula (2) are stored as the correlation formula, DIn = α8×(PCn)+β8 ··· (19) α8: Linear coefficient of PCn β8: Constant DIn = γ8×(PCn) 2 +δ8×(PCn)+ε8 ··· (20) γ8: Quadratic coefficient of PCn δ8: Linear coefficient of PCn ε8: Constant When the oil and fat is the 7th oil type, the deterioration index calculation unit calculates the predetermined deterioration index using the formula (17) or the formula (18), and when the oil and fat is the 8th oil type, the deterioration index calculation unit calculates the predetermined deterioration index using the formula (19) or the formula (20).

[0021]

[11] The present invention also relates to an oil and fat deterioration detection system for detecting the degree of deterioration of an oil and fat based on the amount of polar compounds in the oil and fat, which is one of the deterioration indicators of an oil and fat, comprising: a measuring device for measuring the amount of polar compounds contained in the oil and fat; and an oil and fat deterioration detection device for detecting the degree of deterioration of the oil and fat based on the measured value of the amount of polar compounds measured by the measuring device, wherein the oil and fat deterioration detection device stores a correlation between the amount of polar compounds and a predetermined deterioration indicator other than the amount of polar compounds, obtains the measured value of the amount of polar compounds measured by the measuring device, calculates the predetermined deterioration indicator based on the obtained measured value of the amount of polar compounds and the stored correlation, and outputs the calculated predetermined deterioration indicator as the detection result of the degree of deterioration.

[0022]

[12] Preferably, the oil and fat deterioration detection system described in

[11] above, wherein the correlation is a correlation formula in which the predetermined deterioration index is expressed as a polynomial of the amount of polar compound.

[0023]

[13] Preferably, the oil and fat deterioration degree detection system described in

[12] above, wherein the correlation formula is a linear formula represented by the following formula (1) or a quadratic formula represented by the following formula (2), where PC is the amount of polar compound, DI is the predetermined deterioration index, and n is an arbitrary heating time of the oil and fat. DIn = α × (PCn) + β···(1) α: Linear coefficient of PCn β: constant DIn = γ × (PCn) 2 +δ×(PCn)+ε···(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant

[0024]

[14] The present invention also relates to a method for detecting the degree of deterioration of an oil and fat based on the amount of polar compounds in the oil and fat, which is one of the indicators of deterioration of the oil and fat, and comprises: a measuring device for measuring the amount of polar compounds contained in the oil and fat; and an oil and fat deterioration detection device which stores a correlation between the amount of polar compounds and a predetermined deterioration indicator other than the amount of polar compounds, and is characterized in that the method comprises: a measurement step in which the measuring device measures the amount of polar compounds contained in the oil and fat; a data acquisition step in which the oil and fat deterioration detection device acquires a measured value of the amount of polar compounds measured in the measurement step; a deterioration indicator calculation step in which the oil and fat deterioration detection device calculates a predetermined deterioration indicator based on the measured value of the amount of polar compounds acquired in the data acquisition step and the stored correlation; and a detection result output step in which the oil and fat deterioration detection device outputs the predetermined deterioration indicator calculated in the deterioration indicator calculation step as the detection result of the degree of deterioration.

[0025]

[15] Preferably, the method for detecting the degree of oil deterioration described in

[14] above, wherein the correlation is a correlation formula in which the predetermined deterioration index is expressed as a polynomial of the amount of polar compound.

[0026]

[16] Preferably, the method for detecting the degree of deterioration of oil and fat as described in

[15] above, wherein the correlation formula is a linear formula represented by the following formula (1) or a quadratic formula represented by the following formula (2), where PC is the amount of polar compound, DI is the predetermined deterioration index, and n is an arbitrary heating time of the oil and fat. DIn = α × (PCn) + β···(1) α: Linear coefficient of PCn β: constant DIn = γ × (PCn) 2 +δ×(PCn)+ε···(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant

[0027]

[17] The present invention also relates to a fat and oil deterioration detection program for detecting the degree of deterioration of fats and oils, characterized in that it causes a computer to perform the following: a data acquisition process to acquire a measured value of the amount of polar compounds contained in the fat and oil; a deterioration index calculation process to calculate the predetermined deterioration index from the measured value of the amount of polar compounds acquired by the data acquisition process using the correlation between the amount of polar compounds and a predetermined deterioration index other than the amount of polar compounds; and a detection result output process to output the predetermined deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the fat and oil.

[0028]

[18] Preferably, the oil and fat degradation detection program described in

[17] , wherein the correlation is a correlation formula in which the predetermined degradation index is expressed as a polynomial of the amount of polar compound.

[0029]

[19] Preferably, the oil and fat deterioration degree detection program described in

[18] above, wherein the correlation formula is a linear formula represented by the following formula (1) or a quadratic formula represented by the following formula (2), where PC is the amount of polar compound, DI is the predetermined deterioration index, and n is the arbitrary heating time of the oil and fat. DIn = α × (PCn) + β···(1) α: Linear coefficient of PCn β: constant DIn = γ × (PCn) 2 +δ×(PCn)+ε···(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant

[0030]

[20] The present invention also relates to an oil and fat deterioration detection device for detecting the degree of deterioration of an oil and fat, comprising: a storage unit that stores a correlation between a first deterioration index, which is an oil and fat deterioration index and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is an oil and fat deterioration index other than the first deterioration index; a data acquisition unit that acquires a measured value of the first deterioration index; a deterioration index calculation unit that calculates the second deterioration index based on the measured value of the first deterioration index acquired by the data acquisition unit and the correlation stored in the storage unit; and a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration.

[0031]

[21] Preferably, the oil and fat deterioration degree detection device described in

[20] above, wherein the correlation is a correlation formula in which the second deterioration index is expressed as a polynomial of the first deterioration index.

[0032]

[22] Preferably, the oil and fat deterioration degree detection device described in

[21] above, wherein the correlation formula is a linear formula represented by the following formula (31) or a quadratic formula represented by the following formula (32), where Di1 is the first deterioration index, Di2 is the second deterioration index, and n is an arbitrary heating time of the oil and fat. Di2n = α × (Di1n) + β···(31) α: linear coefficient of Di1n β: constant Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε···(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant

[0033]

[23] Preferably, the oil deterioration detection device described in

[22] above, wherein the oil is edible oil for cooking food ingredients, and the linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32) are each set to values ​​corresponding to the amount of fried food per unit time that is fried using the edible oil.

[0034]

[24] Preferably, the oil deterioration detection device described in

[22] above, wherein the oil is edible oil for cooking food ingredients, and the storage unit stores as the correlation formula a linear formula represented by the following formula (33) obtained by adding a term for an air heating variable EH1 set considering air heating in which only the oil is heated without cooking the food ingredients to formula (31), or a quadratic formula represented by the following formula (34) obtained by adding a term for an air heating variable EH2 set considering air heating to formula (32), Di2n=α×(Di1n)+β+EH1···(33) α: linear coefficient of Di1n β: constant EH1: Air heating variable Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε+EH2···(34) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant EH2: Air heating variable The deterioration index calculation unit is characterized in that, when the oil is subjected to the air heating, it calculates the second deterioration index using the formula (33) or formula (34) stored in the memory unit.

[0035]

[25] Preferably, the oil and fat deterioration detection device described in

[22] above, characterized in that the linear coefficient α and constant β of formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε of formula (32) are each set to values ​​corresponding to the type of oil and fat.

[0036]

[26] Preferably, the oil deterioration detection device described in

[25] above, wherein the oil is classified into a first type and a second type according to the fatty acid composition constituting the oil, the first type is an oil having a composition in which the oleic acid content is greater than the linoleic acid content, and the second type is an oil having a composition in which the oleic acid content is less than or equal to the linoleic acid content, and the memory unit sets the linear coefficient α of formula (31) to a value corresponding to the first type A linear equation represented by the following formula (35) which includes β1, the constant β in formula (31) set to a value corresponding to the first oil type, or a quadratic equation represented by the following formula (36) which includes γ1, the quadratic coefficient γ in formula (32) set to a value corresponding to the first oil type, δ1, the linear coefficient δ in formula (32) set to a value corresponding to the first oil type, and ε1, the constant ε in formula (32) set to a value corresponding to the first oil type, Di2n=α1×(Di1n)+β1···(35) α1: Linear coefficient of Di1n β1: Constant Di2n = γ1 × (Di1n) 2 +δ1×(Di1n)+ε1···(36) γ1: Quadratic coefficient of Di1n δ1: Linear coefficient of Di1n ε1: constant A linear equation represented by the following formula (37) is stored as the correlation formula, which includes α2, set to a value corresponding to the second oil type, as the linear coefficient α of formula (31), and β2, set to a value corresponding to the second oil type, as the constant β of formula (31), or a quadratic equation represented by the following formula (38) is stored as the correlation formula, which includes γ2, set to a value corresponding to the second oil type, as the quadratic coefficient γ of formula (32), δ2, set to a value corresponding to the second oil type, as the linear coefficient δ of formula (32), and ε2, set to a value corresponding to the second oil type, as the constant ε of formula (32), as the constant ε2, as the constant corresponding to the second oil type. Di2n=α2×(Di1n)+β2···(37) α2: Linear coefficient of Di1n β2: Constant Di2n = γ2 × (Di1n) 2 +δ²×(Di1n)+ε²···(38) γ2: Quadratic coefficient of Di1n δ2: Linear coefficient of Di1n ε²: constant The deterioration index calculation unit is characterized in that, when the oil is the first type of oil, it calculates the second deterioration index using formula (35) or formula (36) stored in the memory unit, and when the oil is the second type of oil, it calculates the second deterioration index using formula (37) or formula (38) stored in the memory unit.

[0037]

[27] Preferably, the oil deterioration detection device described in

[25] above, wherein the oil is classified into a third type and a fourth type according to the iodine value of the oil, the third type is an oil type in which the iodine value of the oil is less than a predetermined iodine value threshold, and the fourth type is an oil type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, and the storage unit sets α3, which is set to a value corresponding to the third type of oil, as the linear coefficient α of the formula (31), in the formula (31) A linear equation represented by the following formula (39) which includes β3, set as the constant β according to the third type of oil, or a quadratic equation represented by the following formula (40) which includes γ3, set as the quadratic coefficient γ of formula (32) according to the third type of oil, δ3, set as the linear coefficient δ of formula (32) according to the third type of oil, and ε3, set as the constant ε of formula (32) according to the third type of oil, Di2n=α3×(Di1n)+β3···(39) α3: Linear coefficient of Di1n β3: Constant Di2n = γ3 × (Di1n) 2 +δ3×(Di1n)+ε3···(40) γ3: Quadratic coefficient of Di1n δ3: Linear coefficient of Di1n ε3: constant A linear equation represented by the following formula (41) is stored as the correlation formula, which includes α4, set as the linear coefficient α of formula (31) to a value corresponding to the fourth oil type, and β4, set as the constant β of formula (31) to a value corresponding to the fourth oil type, or a quadratic equation represented by the following formula (42) is stored as the correlation formula, which includes γ4, set as the quadratic coefficient γ of formula (32) to a value corresponding to the fourth oil type, δ4, set as the linear coefficient δ of formula (32) to a value corresponding to the fourth oil type, and ε4, set as the constant ε of formula (32) to a value corresponding to the fourth oil type. Di2n=α4×(Di1n)+β4···(41) α4: Linear coefficient of Di1n β4: Constant Di2n = γ4 × (Di1n) 2 +δ4×(Di1n)+ε4···(42) γ4:Quadratic coefficient of Di1n δ4: Linear coefficient of Di1n ε4: constant The deterioration index calculation unit is characterized in that, when the oil is the third type of oil, it calculates the second deterioration index using formula (39) or formula (40), and when the oil is the fourth type of oil, it calculates the second deterioration index using formula (41) or formula (42).

[0038]

[28] Preferably, the oil deterioration detection device described in

[25] above, wherein the oil is classified into a fifth oil type and a sixth oil type according to the CDM value of the oil, the fifth oil type is an oil type in which the CDM value of the oil is equal to or greater than a predetermined CDM threshold, and the sixth oil type is an oil type in which the CDM value of the oil is less than the predetermined CDM threshold, and the storage unit sets α5, which is set to a value corresponding to the fifth oil type as the linear coefficient α of formula (31), A linear equation represented by the following formula (43) which includes β5, set as the constant β according to the fifth type of oil, or a quadratic equation represented by the following formula (44) which includes γ5, set as the quadratic coefficient γ of formula (32) according to the fifth type of oil, δ5, set as the linear coefficient δ of formula (32) according to the fifth type of oil, and ε5, set as the constant ε of formula (32) according to the fifth type of oil, Di2n=α5×(Di1n)+β5···(43) α5: Linear coefficient of Di1n β5: Constant Di2n = γ5 × (Di1n) 2 +δ5×(Di1n)+ε5···(44) γ5: Quadratic coefficient of Di1n δ5: Linear coefficient of Di1n ε5: constant A linear equation represented by the following formula (45) is stored as the correlation formula, which includes α6, set as the linear coefficient α of formula (31) to a value corresponding to the sixth oil type, and β6, set as the constant β of formula (31) to a value corresponding to the sixth oil type, or a quadratic equation represented by the following formula (46) is stored as the correlation formula, which includes γ6, set as the quadratic coefficient γ of formula (32) to a value corresponding to the sixth oil type, δ6, set as the linear coefficient δ of formula (32) to a value corresponding to the sixth oil type, and ε6, set as the constant ε of formula (32) to a value corresponding to the sixth oil type. Di2n=α6×(Di1n)+β6···(45) α6: Linear coefficient of Di1n β6: Constant Di2n = γ6 × (Di1n) 2+δ6×(Di1n)+ε6···(46) γ6: Quadratic coefficient of Di1n δ6: linear coefficient of Di1n ε6: constant The deterioration index calculation unit is characterized in that, when the oil is the fifth type of oil, it calculates the second deterioration index using formula (43) or formula (44), and when the oil is the sixth type of oil, it calculates the second deterioration index using formula (45) or formula (46).

[0039]

[29] Preferably, the oil degradation detection device described in

[25] above, wherein the oil is classified into seventh oil type and eighth oil type according to the lipid molecular species in the oil, the seventh oil type is an oil type in which the content of lipid molecular species in the oil is greater than a predetermined content threshold, the rate of increase of the content of diacylglycerol in the oil due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the content of free fatty acids in the oil due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the content of triacylglycerol in the oil due to heating is less than or equal to a predetermined decrease rate threshold, the eighth oil type is an oil type in which the content of lipid molecular species in the oil is less than or equal to the predetermined content threshold, the rate of increase of the content of diacylglycerol in the oil due to heating is greater than the predetermined first increase rate threshold, and the rate of decrease of the content of free fatty acids in the oil due to heating is less than or equal to a predetermined decrease rate threshold, the eighth oil type is an oil type in which the content of lipid molecular species in the oil is less than or equal to the predetermined content threshold, the rate of increase of the content of diacylglycerol in the oil due to heating is greater than the predetermined first increase rate threshold, and the free An oil type in which the rate of increase in fatty acid content is greater than the predetermined second rate of increase threshold, and the rate of decrease in the triacylglycerol content in the oil due to heating is greater than the predetermined rate of decrease threshold, wherein the storage unit contains a linear equation represented by the following formula (47) which includes α7, set as the linear coefficient α of formula (31) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (31) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (48) which includes γ7, set as the quadratic coefficient γ of formula (32) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (32) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (32) to a value corresponding to the seventh oil type, Di2n=α7×(Di1n)+β7...(47) α7: The linear coefficient of Di1n β7: Constant Di2n = γ7 × (Di1n) 2 +δ7×(Di1n)+ε7···(48) γ7: Quadratic coefficient of Di1n δ7: Linear coefficient of Di1n ε7: constant A linear equation represented by the following formula (49) is stored as the correlation formula, which includes α8, set as the linear coefficient α of formula (31) to a value corresponding to the 8th oil type, and β8, set as the constant β of formula (31) to a value corresponding to the 8th oil type, or a quadratic equation represented by the following formula (50) is stored as the correlation formula, which includes γ8, set as the quadratic coefficient γ of formula (32) to a value corresponding to the 8th oil type, δ8, set as the linear coefficient δ of formula (32) to a value corresponding to the 8th oil type, and ε8, set as the constant ε of formula (32) to a value corresponding to the 8th oil type. Di2n=α8×(Di1n)+β8···(49) α8: Linear coefficient of Di1n β8: Constant Di2n = γ8 × (Di1n) 2 +δ8×(Di1n)+ε8···(50) γ8: Quadratic coefficient of Di1n δ8: Linear coefficient of Di1n ε8: constant The deterioration index calculation unit is characterized in that, when the oil is the seventh type of oil, it calculates the second deterioration index using formula (47) or formula (48), and when the oil is the eighth type of oil, it calculates the second deterioration index using formula (49) or formula (50).

[0040]

[30] Preferably, the oil deterioration detection device described in

[22] above, wherein the oil is edible oil for cooking food ingredients, and when the deterioration index calculation unit calculates the color of the edible oil as the second deterioration index, the linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32) are each set to values ​​corresponding to the type of fried food that is fried using the edible oil.

[0041]

[31] Preferably, the oil and fat degradation detection device described in

[20] above, wherein the first degradation index is any one of the following: the acid value of the oil and fat, the amount of polar compounds in the oil and fat, the color of the oil and fat, and the viscosity increase rate of the oil and fat.

[0042]

[32] The present invention also relates to an oil and fat deterioration detection system for detecting the degree of deterioration of an oil and fat, comprising: a measuring device for measuring a first deterioration index which is an indicator of oil and fat deterioration and is defined based on a substance produced by heating the oil and fat; and an oil and fat deterioration detection device for detecting the degree of deterioration of the oil and fat based on the measured value of the first deterioration index measured by the measuring device, wherein the oil and fat deterioration detection device stores a correlation between the first deterioration index and a second deterioration index which is an indicator of oil and fat deterioration other than the first deterioration index, acquires the measured value of the first deterioration index measured by the measuring device, calculates the second deterioration index based on the acquired measured value of the first deterioration index and the stored correlation, and outputs the calculated second deterioration index as the detection result of the degree of deterioration.

[0043]

[33] Preferably, the oil degradation detection system described in

[32] , wherein the correlation is a correlation formula in which the second degradation index is expressed as a polynomial of the first degradation index.

[0044]

[34] Preferably, the oil and fat deterioration degree detection system described in

[33] above, wherein the correlation formula is a linear formula represented by the following formula (31) or a quadratic formula represented by the following formula (32), where Di1 is the first deterioration index, Di2 is the second deterioration index, and n is an arbitrary heating time of the oil and fat. Di2n = α × (Di1n) + β···(31) α: linear coefficient of Di1n β: constant Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε···(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant

[0045]

[35] The present invention also relates to a method for detecting the degree of deterioration of oils and fats, comprising: a measuring device for measuring a first deterioration index which is an indicator of deterioration of oils and fats and is defined based on a substance produced by heating the oils and fats; and an oil deterioration detection device which stores a correlation between the first deterioration index and a second deterioration index which is an indicator of deterioration of oils and fats other than the first deterioration index, wherein the method includes: a measurement step in which the measuring device measures the first deterioration index; a data acquisition step in which the oil deterioration detection device acquires a measured value of the first deterioration index measured in the measurement step; a deterioration index calculation step in which the oil deterioration detection device calculates the second deterioration index based on the measured value of the first deterioration index acquired in the data acquisition step and the stored correlation; and a detection result output step in which the oil deterioration detection device outputs the second deterioration index calculated in the deterioration index calculation step as the detection result of the degree of deterioration.

[0046]

[36] Preferably, the method for detecting the degree of oil deterioration described in

[35] , wherein the correlation is a correlation formula in which the second deterioration index is expressed as a polynomial of the first deterioration index.

[0047]

[37] Preferably, the method for detecting the degree of deterioration of oil and fat as described in

[36] above, wherein the correlation formula is a linear formula represented by the following formula (31) or a quadratic formula represented by the following formula (32), where Di1 is the first deterioration index, Di2 is the second deterioration index, and n is an arbitrary heating time of the oil and fat. Di2n = α × (Di1n) + β···(31) α: linear coefficient of Di1n β: constant Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε···(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant

[0048]

[38] The present invention also relates to a fat and oil deterioration detection program for detecting the degree of deterioration of fats and oils, characterized in that it causes a computer to execute: a data acquisition process for acquiring measured values ​​of a first deterioration index which is a deterioration index of fats and oils and is defined based on substances produced by heating the fats and oils; a deterioration index calculation process which calculates the second deterioration index from the measured values ​​of the first deterioration index acquired by the data acquisition process using the correlation between the first deterioration index and a second deterioration index which is a deterioration index of fats and oils other than the first deterioration index; and a detection result output process which outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the fats and oils.

[0049]

[39] Preferably, the oil degradation detection program described in

[38] , wherein the correlation is a correlation formula in which the second degradation index is expressed as a polynomial of the first degradation index.

[0050]

[40] Preferably, the oil and fat deterioration degree detection program described in

[39] above, wherein the correlation formula is a linear formula represented by the following formula (31) or a quadratic formula represented by the following formula (32), where Di1 is the first deterioration index, Di2 is the second deterioration index, and n is an arbitrary heating time of the oil and fat. Di2n = α × (Di1n) + β···(31) α: linear coefficient of Di1n β: constant Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε···(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant [Effects of the Invention]

[0051] According to the present invention, various indicators of oil and fat deterioration can be detected simply and with high accuracy. Other problems, configurations, and effects will be clarified by the following description of embodiments. [Brief explanation of the drawing]

[0052] [Figure 1] This diagram shows a part of a kitchen where deep-frying is performed. [Figure 2] This is a system configuration diagram showing one example of the configuration of the oil and fat deterioration degree detection system according to each embodiment of the present invention. [Figure 3] This is a graph of a linear function showing the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil. [Figure 4] This is a quadratic function graph showing the correlation between the acid value of frying oil and the amount of polar compounds it contains. [Figure 5] This is a functional block diagram showing the functions of the cloud according to the first embodiment. [Figure 6] This flowchart shows the processing flow executed in the cloud according to the first embodiment. [Figure 7]This is a graph of a linear function showing the correlation between the acid value of the frying oil and the amount of polar compounds contained in the frying oil, taking into account the amount of frying per hour. [Figure 8] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the amount of polar compounds it contains, taking into account the amount of frying per hour. [Figure 9] This is a functional block diagram showing the functions of the cloud according to the second embodiment. [Figure 10] This flowchart shows the processing flow executed in the cloud according to the second embodiment. [Figure 11] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the amount of polar compounds it contains, taking into account whether or not there is an air heating time. [Figure 12] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the amount of polar compounds it contains, taking into account whether or not there is an air heating time. [Figure 13] This is a functional block diagram showing the functions of the cloud according to the third embodiment. [Figure 14] This flowchart shows the processing flow executed in the cloud according to the third embodiment. [Figure 15] This graph shows the correlation between the acid value of the frying oil and the amount of polar compounds contained in the frying oil for oil types 1 and 2. [Figure 16] This is a functional block diagram showing the functions of the cloud according to the fourth embodiment. [Figure 17] This flowchart shows the processing flow executed in the cloud according to the fourth embodiment. [Figure 18] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the first type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (5), and (6). [Figure 19]This graph shows the correlation between the amount of polar compounds contained in the frying oil of the second type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (7), and (8), respectively. [Figure 20] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for oil types 3 and 4. [Figure 21] This flowchart shows the processing flow executed in the cloud according to the fifth embodiment. [Figure 22] This graph shows the correlation between the amount of polar compounds contained in the third type of frying oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (9), and (10). [Figure 23] This graph shows the correlation between the amount of polar compounds contained in the frying oil for the fourth type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (11), and (12), respectively. [Figure 24] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oil for oil types 5 and 6. [Figure 25] This flowchart shows the processing flow executed in the cloud according to the sixth embodiment. [Figure 26] This graph shows the correlation between the amount of polar compounds contained in the frying oil for the fifth type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (13), and (14), respectively. [Figure 27] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the sixth type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (15), and (16), respectively. [Figure 28]This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oil for oil types 7 and 8, which are classified according to the MG content of new oil. [Figure 29] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 30] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oils classified as oil types 7 and 8, according to the MG content of the heating oil. [Figure 31] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oils classified as oil types 7 and 8, according to the TG content of the heating oil. [Figure 32] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oils of oil types 7 and 8, which are classified according to the rate of increase in DG content due to heating. [Figure 33] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oils of oil types 7 and 8, which are classified according to the rate of increase in FFA content due to heating. [Figure 34] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in frying oils of oil types 7 and 8, which are classified according to the rate of decrease in TG content due to heating. [Figure 35] This flowchart shows the processing flow executed in the cloud according to the seventh embodiment. [Figure 36] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil classified by the MG content of new oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 37]This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the eighth oil type, classified by the MG content of new oil. It compares the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20). [Figure 38] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil classified by the FFA content of new oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 39] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the eighth oil type, classified by the FFA content of new oil. It compares the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20). [Figure 40] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the MG content of the heating oil. It compares the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 41] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the eighth type of oil classified by the MG content of the heating oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20). [Figure 42] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the TG content of the heated oil. It compares the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 43]This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the eighth type of oil classified by the TG content of the heating oil, comparing the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20). [Figure 44] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 45] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20), respectively. [Figure 46] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 47] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20), respectively. [Figure 48] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured acid value with the acid value calculated using formulas (1), (2), (17), and (18). [Figure 49]This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured acid value with the acid value calculated using formulas (1), (2), (19), and (20), respectively. [Figure 50] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in the frying oil and the rate of increase in viscosity of the frying oil. [Figure 51] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil. [Figure 52] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in the frying oil and the rate of increase in viscosity of the frying oil. [Figure 53] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil. [Figure 54] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in the frying oil and the acid value of the frying oil. [Figure 55] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the rate of viscosity increase of the frying oil. [Figure 56] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the color of the frying oil. [Figure 57] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in the frying oil and the acid value of the frying oil. [Figure 58] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the rate of viscosity increase of the frying oil. [Figure 59] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the color of the frying oil. [Figure 60] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the frying oil. [Figure 61] This is a graph of a linear function showing the correlation between the acid value of frying oil and the rate of viscosity increase of the frying oil. [Figure 62] This is a graph of a linear function showing the correlation between the color of the frying oil and the rate of increase in viscosity of the frying oil. [Figure 63] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the frying oil. [Figure 64] This is a quadratic function graph showing the correlation between the acid value of frying oil and the rate of viscosity increase of the frying oil. [Figure 65] This is a quadratic function graph showing the correlation between the color of the frying oil and the rate of increase in viscosity of the frying oil. [Figure 66] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil. [Figure 67] This is a graph of a linear function showing the correlation between the acid value of frying oil and its color. [Figure 68] This is a graph of a linear function showing the correlation between the color of the frying oil and the rate of increase in viscosity of the frying oil. [Figure 69] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil. [Figure 70] This is a quadratic function graph showing the correlation between the acid value of frying oil and the color of the frying oil. [Figure 71] This is a quadratic function graph showing the correlation between the color of the frying oil and the rate of increase in viscosity of the frying oil. [Figure 72] This is a functional block diagram showing the functions of the cloud according to the eighth embodiment. [Figure 73] This flowchart shows the processing flow executed in the cloud according to the eighth embodiment. [Figure 74] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in frying oil and the acid value of the frying oil, taking into account the amount of oil fried per hour. [Figure 75] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in frying oil and the acid value of the frying oil, taking into account the amount of oil fried per hour. [Figure 76] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the rate of viscosity increase of the frying oil, taking into account the amount of oil fried per hour. [Figure 77]This is a quadratic function graph showing the correlation between the acid value of the frying oil and the rate of viscosity increase of the frying oil, taking into account the amount of oil fried per hour. [Figure 78] This is a linear function graph showing the correlation between the acid value of frying oil and the viscosity increase rate, taking into account the amount of oil fried per hour. [Figure 79] This is a quadratic function graph showing the correlation between the acid value of frying oil and the viscosity increase rate of the frying oil, taking into account the amount of oil fried per hour. [Figure 80] This is a functional block diagram showing the functions of the cloud according to the ninth embodiment. [Figure 81] This flowchart shows the processing flow executed in the cloud according to the 9th embodiment. [Figure 82] This is a graph of a linear function showing the correlation between the amount of polar compounds contained in the frying oil and the acid value of the frying oil, taking into account whether or not there is an air heating time. [Figure 83] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the rate of viscosity increase of the frying oil, taking into account the presence or absence of preheating time. [Figure 84] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the color of the frying oil, taking into account whether or not there is a preheating time. [Figure 85] This is a quadratic function graph showing the correlation between the amount of polar compounds contained in the frying oil and the acid value of the frying oil, taking into account whether or not there is an air heating time. [Figure 86] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the rate of viscosity increase of the frying oil, taking into account whether or not there is a preheating time. [Figure 87] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the color of the frying oil, taking into account whether or not there is a preheating time. [Figure 88] This is a graph of a linear function showing the correlation between the acid value of the frying oil and the viscosity increase rate, taking into account whether or not there is a preheating time. [Figure 89] This is a quadratic function graph showing the correlation between the acid value of the frying oil and the viscosity increase rate, taking into account whether or not there is a preheating time. [Figure 90]This is a graph of a linear function showing the correlation between the acid value of frying oil and its color, taking into account whether or not there is an air heating time. [Figure 91] This is a quadratic function graph showing the correlation between the acid value of frying oil and its color, taking into account whether or not there is a preheating time. [Figure 92] This is a functional block diagram showing the functions of the cloud according to the 10th embodiment. [Figure 93] This flowchart shows the processing flow executed in the cloud according to the 10th embodiment. [Figure 94] This graph shows the correlation between the amount of polar compounds contained in the frying oil for oil type 1 and oil type 2 and the rate of viscosity increase of the frying oil. [Figure 95] This graph shows the correlation between the amount of polar compounds contained in the frying oil (Type 1 and Type 2 oils) and the color of the frying oil. [Figure 96] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the frying oil for oil types 1 and 2. [Figure 97] This graph shows the correlation between the acid value of the frying oil and the viscosity increase rate of the frying oil for the first and second types of oil. [Figure 98] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the frying oil for the first and second types of oil. [Figure 99] This graph shows the correlation between the acid value of the frying oil and the viscosity increase rate of the frying oil for the first and second types of oil. [Figure 100] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the frying oil for the first and second types of oil. [Figure 101] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 1 and 2. [Figure 102] This graph shows the correlation between the color of the frying oil (Type 1 and Type 2 oil) and the rate of viscosity increase of the frying oil. [Figure 103] This is a functional block diagram showing the functions of the cloud according to the 11th embodiment. [Figure 104] This flowchart shows the processing flow executed in the cloud according to the 11th embodiment. [Figure 105] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the first type of oil and the viscosity increase rate of the frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (35), and (36), respectively. [Figure 106] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the first type of oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (35), and (36), respectively. [Figure 107] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the first type of frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (35), and (36), respectively. [Figure 108] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the first type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (35), and (36), respectively. [Figure 109] This graph shows the correlation between the viscosity increase rate of the first type of frying oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (35), and (36), respectively. [Figure 110] This graph shows the correlation between the viscosity increase rate of the frying oil for the first type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (35), and (36), respectively. [Figure 111]This graph shows the correlation between the viscosity increase rate of the first type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (35), and (36), respectively. [Figure 112] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the first type of frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (35), and (36), respectively. [Figure 113] This graph shows the correlation between the viscosity increase rate of the frying oil and the color of the frying oil for the first type of oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (35), and (36), respectively. [Figure 114] This graph shows the correlation between the amount of polar compounds contained in the second type of frying oil and the viscosity increase rate of the frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (37), and (38), respectively. [Figure 115] This graph shows the correlation between the amount of polar compounds contained in the second type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (37), and (38), respectively. [Figure 116] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the second type of frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (37), and (38), respectively. [Figure 117] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the second type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (37), and (38), respectively. [Figure 118]This graph shows the correlation between the viscosity increase rate of the second type of frying oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (37), and (38), respectively. [Figure 119] This graph shows the correlation between the viscosity increase rate of the second type of frying oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (37), and (38), respectively. [Figure 120] This graph shows the correlation between the viscosity increase rate of the second type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (37), and (38), respectively. [Figure 121] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the second type of frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (37), and (38), respectively. [Figure 122] This graph shows the correlation between the viscosity increase rate of the frying oil and the color of the second type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (37), and (38), respectively. [Figure 123] This graph shows the correlation between the amount of polar compounds contained in the frying oil for oil types 3 and 4 and the rate of viscosity increase of the frying oil. [Figure 124] This graph shows the correlation between the amount of polar compounds contained in the frying oil for oil types 3 and 4 and the color of the frying oil. [Figure 125] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the third and fourth types of frying oil. [Figure 126] This graph shows the correlation between the acid value of the frying oil and the viscosity increase rate of the frying oil for the third and fourth types of oil. [Figure 127]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the third and fourth types of frying oil. [Figure 128] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oils of the third and fourth oil types. [Figure 129] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the third and fourth types of frying oil. [Figure 130] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 3 and 4. [Figure 131] This graph shows the correlation between the color of the frying oil and the rate of viscosity increase of the frying oil for the third and fourth types of oil. [Figure 132] This flowchart shows the processing flow executed in the cloud according to the 12th embodiment. [Figure 133] This graph shows the correlation between the amount of polar compounds contained in the third type of frying oil and the viscosity increase rate of the frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (39), and (40), respectively. [Figure 134] This graph shows the correlation between the amount of polar compounds contained in the third type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (39), and (40), respectively. [Figure 135] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the third type of frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (39), and (40), respectively. [Figure 136] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the third type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (39), and (40), respectively. [Figure 137]This graph shows the correlation between the viscosity increase rate of the third type of frying oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (39), and (40), respectively. [Figure 138] This graph shows the correlation between the viscosity increase rate of the third type of frying oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (39), and (40), respectively. [Figure 139] This graph shows the correlation between the viscosity increase rate of the third type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (39), and (40), respectively. [Figure 140] This graph shows the correlation between the color of the frying oil for the third type of oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (39), and (40), respectively. [Figure 141] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the third type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (39), and (40), respectively. [Figure 142] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the fourth type of oil and the viscosity increase rate of the frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (41), and (42), respectively. [Figure 143] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the fourth type of oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (41), and (42), respectively. [Figure 144]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the fourth type of oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (41), and (42), respectively. [Figure 145] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the fourth type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (41), and (42), respectively. [Figure 146] This graph shows the correlation between the viscosity increase rate of the fourth type of frying oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (41), and (42), respectively. [Figure 147] This graph shows the correlation between the viscosity increase rate of the frying oil for the fourth type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (41), and (42), respectively. [Figure 148] This graph shows the correlation between the viscosity increase rate of the fourth type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (41), and (42), respectively. [Figure 149] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the fourth type of oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (41), and (42), respectively. [Figure 150] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the fourth type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (41), and (42), respectively. [Figure 151]This graph shows the correlation between the amount of polar compounds contained in the frying oil for oil types 5 and 6 and the rate of viscosity increase of the frying oil. [Figure 152] This graph shows the correlation between the amount of polar compounds contained in the frying oil for oil types 5 and 6 and the color of the frying oil. [Figure 153] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oils of types 5 and 6. [Figure 154] This graph shows the correlation between the acid value of frying oils for oil types 5 and 6 and the rate of viscosity increase of the frying oil. [Figure 155] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the frying oil for oil types 5 and 6. [Figure 156] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oils of types 5 and 6. [Figure 157] This graph shows the correlation between the color of the frying oil and the viscosity increase rate for oil types 5 and 6. [Figure 158] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 5 and 6. [Figure 159] This graph shows the correlation between the color of the frying oil and the rate of viscosity increase of the frying oil for oil types 5 and 6. [Figure 160] This flowchart shows the processing flow executed in the cloud according to the 13th embodiment. [Figure 161] This graph shows the correlation between the amount of polar compounds contained in the frying oil for the fifth type of oil and the viscosity increase rate of the frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (43), and (44), respectively. [Figure 162] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the fifth type of oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (43), and (44), respectively. [Figure 163] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the fifth type of oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (43), and (44), respectively. [Figure 164] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the fifth type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (43), and (44), respectively. [Figure 165] This graph shows the correlation between the viscosity increase rate of frying oil for the fifth type of oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (43), and (44), respectively. [Figure 166] This graph shows the correlation between the viscosity increase rate of the frying oil for the fifth type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (43), and (44), respectively. [Figure 167] This graph shows the correlation between the viscosity increase rate of the frying oil for the fifth type of oil and the color of the frying oil, comparing the measured color with the color calculated from formulas (31), (32), (43), and (44), respectively. [Figure 168] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the fifth type of oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (43), and (44), respectively. [Figure 169] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the fifth type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (43), and (44), respectively. [Figure 170]This graph shows the correlation between the amount of polar compounds contained in the frying oil of the sixth type of oil and the viscosity increase rate of the frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (45), and (46), respectively. [Figure 171] This graph shows the correlation between the amount of polar compounds contained in the frying oil of the sixth type of oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (45), and (46), respectively. [Figure 172] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the sixth type of oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (45), and (46), respectively. [Figure 173] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the sixth type of frying oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (45), and (46), respectively. [Figure 174] This graph shows the correlation between the viscosity increase rate of frying oil for the sixth type of oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (45), and (46), respectively. [Figure 175] This graph shows the correlation between the viscosity increase rate of frying oil for the sixth type of oil and the acid value of the frying oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (45), and (46), respectively. [Figure 176] This graph shows the correlation between the viscosity increase rate of the sixth type of frying oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (45), and (46). [Figure 177]This graph shows the correlation between the color of the frying oil for the sixth type of oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (45), and (46), respectively. [Figure 178] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the sixth type of oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (45), and (46), respectively. [Figure 179] This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the MG content of the new oil. [Figure 180] This graph shows the correlation between the color of the frying oil and the amount of polar compounds contained in the frying oil for oil types 7 and 8, which are classified according to the MG content of the new oil. [Figure 181] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oils classified by the MG content of new oils for oil types 7 and 8. [Figure 182] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of frying oil for oil types 7 and 8, which are classified according to the MG content of new oil. [Figure 183] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of frying oils classified by the MG content of new oils for oil types 7 and 8. [Figure 184] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oil for oil types 7 and 8, which are classified according to the MG content of new oil. [Figure 185] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the MG content of the new oil. [Figure 186] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the MG content of new oil. [Figure 187]This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the MG content of new oil. [Figure 188] This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 189] This graph shows the correlation between the color of the frying oil and the amount of polar compounds contained in the frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 190] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of frying oils classified by the FFA content of new oils for oil types 7 and 8. [Figure 191] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 192] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of frying oils classified by the FFA content of new oils for oil types 7 and 8. [Figure 193] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 194] This graph shows the correlation between the color of the frying oil and the viscosity increase rate for oil types 7 and 8, which are classified according to the FFA content of the new oil. [Figure 195] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 196] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the FFA content of new oil. [Figure 197] This graph shows the correlation between the amount of polar compounds contained in frying oils for oil types 7 and 8, classified according to the MG content of the heating oil, and the viscosity increase rate of the frying oil. [Figure 198]This graph shows the correlation between the color of the frying oil and the amount of polar compounds contained in the frying oil for oil types 7 and 8, which are classified according to the MG content of the heating oil. [Figure 199] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of the frying oil for oil types 7 and 8, which are classified according to the MG content of the heating oil. [Figure 200] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of frying oil for oil types 7 and 8, which are classified according to the MG content of the heating oil. [Figure 201] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the MG content of the heated oil. [Figure 202] This graph shows the correlation between the acid value of the frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the MG content of the heated oil. [Figure 203] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the MG content of the heated oil. [Figure 204] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the MG content of the heating oil. [Figure 205] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the MG content of the heating oil. [Figure 206] This graph shows the correlation between the amount of polar compounds contained in frying oils for oil types 7 and 8, classified by the TG content of the heated oil, and the viscosity increase rate of the frying oil. [Figure 207] This graph shows the correlation between the color of the frying oil and the amount of polar compounds contained in the frying oil for oil types 7 and 8, which are classified according to the TG content of the heated oil. [Figure 208] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the acid value of frying oils classified into types 7 and 8 based on the TG content of the heated oil. [Figure 209] A graph showing the correlation between the viscosity increase rate of fried oil and the acid value of fried oil for the 7th and 8th oil types classified by the TG content of the heating oil. [Figure 210] A graph showing the correlation between the amount of polar compounds contained in fried oil and the viscosity increase rate of fried oil for the 7th and 8th oil types classified by the TG content of the heating oil. [Figure 211] A graph showing the correlation between the acid value of fried oil and the viscosity increase rate of fried oil for the 7th and 8th oil types classified by the TG content of the heating oil. [Figure 212] A graph showing the correlation between the color of fried oil and the viscosity increase rate of fried oil for the 7th and 8th oil types classified by the TG content of the heating oil. [Figure 213] A graph showing the correlation between the amount of polar compounds contained in fried oil and the color of fried oil for the 7th and 8th oil types classified by the TG content of the heating oil. [Figure 214] A graph showing the correlation between the viscosity increase rate of fried oil and the color of fried oil for the 7th and 8th oil types classified by the TG content of the heating oil. [Figure 215] A graph showing the correlation between the viscosity increase rate of fried oil and the amount of polar compounds contained in fried oil for the 7th and 8th oil types classified by the increase rate of DG content due to heating. [Figure 216] A graph showing the correlation between the color of fried oil and the amount of polar compounds contained in fried oil for the 7th and 8th oil types classified by the increase rate of DG content due to heating. [Figure 217] A graph showing the correlation between the acid value of fried oil and the amount of polar compounds contained in fried oil for the 7th and 8th oil types classified by the increase rate of DG content due to heating. [Figure 218] A graph showing the correlation between the acid value of fried oil and the viscosity increase rate of fried oil for the 7th and 8th oil types classified by the increase rate of DG content due to heating. [Figure 219]It is a graph showing the correlation between the amount of polar compounds contained in fried oil and the rate of increase in the viscosity of fried oil related to the 7th and 8th oil types classified by the rate of increase in DG content due to heating. [Figure 220] It is a graph showing the correlation between the acid value of fried oil and the rate of increase in the viscosity of fried oil related to the 7th and 8th oil types classified by the rate of increase in DG content due to heating. [Figure 221] It is a graph showing the correlation between the color of fried oil and the rate of increase in the viscosity of fried oil related to the 7th and 8th oil types classified by the rate of increase in DG content due to heating. [Figure 222] It is a graph showing the correlation between the amount of polar compounds contained in fried oil and the color of fried oil related to the 7th and 8th oil types classified by the rate of increase in DG content due to heating. [Figure 223] It is a graph showing the correlation between the rate of increase in the viscosity of fried oil and the color of fried oil related to the 7th and 8th oil types classified by the rate of increase in DG content due to heating. [Figure 224] It is a graph showing the correlation between the rate of increase in the viscosity of fried oil and the amount of polar compounds contained in fried oil related to the 7th and 8th oil types classified by the rate of increase in FFA content due to heating. [Figure 225] It is a graph showing the correlation between the color of fried oil and the amount of polar compounds contained in fried oil related to the 7th and 8th oil types classified by the rate of increase in FFA content due to heating. [Figure 226] It is a graph showing the correlation between the acid value of fried oil and the amount of polar compounds contained in fried oil related to the 7th and 8th oil types classified by the rate of increase in FFA content due to heating. [Figure 227] It is a graph showing the correlation between the acid value of fried oil and the rate of increase in the viscosity of fried oil related to the 7th and 8th oil types classified by the rate of increase in FFA content due to heating. [Figure 228] It is a graph showing the correlation between the rate of increase in the viscosity of fried oil and the amount of polar compounds contained in fried oil related to the 7th and 8th oil types classified by the rate of increase in FFA content due to heating. [Figure 229]This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oils for oil types 7 and 8, which are classified according to the rate of increase in FFA content due to heating. [Figure 230] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the rate of increase in FFA content due to heating. [Figure 231] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the rate of increase in FFA content due to heating. [Figure 232] This graph shows the correlation between the viscosity increase rate of frying oil and the color of frying oil for oil types 7 and 8, which are classified according to the rate of increase in FFA content due to heating. [Figure 233] This graph shows the correlation between the amount of polar compounds contained in frying oils for oil types 7 and 8, classified by the rate of decrease in TG content due to heating, and the rate of viscosity increase of the frying oil. [Figure 234] This graph shows the correlation between the color of the frying oil and the amount of polar compounds contained in the frying oil for oil types 7 and 8, which are classified according to the rate of decrease in TG content due to heating. [Figure 235] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oils classified as oil types 7 and 8, based on the rate of decrease in TG content due to heating. [Figure 236] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of frying oils classified by the rate of decrease in TG content due to heating, for oil types 7 and 8. [Figure 237] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of frying oils classified by the rate of decrease in TG content due to heating, for oil types 7 and 8. [Figure 238] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oils for oil types 7 and 8, which are classified according to the rate of decrease in TG content due to heating. [Figure 239]This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the frying oil for oil types 7 and 8, which are classified according to the rate of decrease in TG content due to heating. [Figure 240] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for oil types 7 and 8, which are classified according to the rate of decrease in TG content due to heating. [Figure 241] This graph shows the correlation between the viscosity increase rate of frying oil and the color of frying oil for oil types 7 and 8, which are classified according to the rate of decrease in TG content due to heating. [Figure 242] This flowchart shows the processing flow executed in the cloud according to the 14th embodiment. [Figure 243] This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of the frying oil for the seventh type of oil classified by the MG content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (47), and (48), respectively. [Figure 244] This graph shows the correlation between the amount of polar compounds contained in frying oil for the seventh type of oil, classified by the MG content of new oil, and the color of the frying oil. It compares the measured color with the color calculated from formulas (31), (32), (47), and (48). [Figure 245] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for the seventh type of oil classified by the MG content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 246] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of frying oil for the seventh type of oil classified by the MG content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 247]A graph showing the correlation between the amount of polar compounds contained in fried oil and the rate of increase in the viscosity of fried oil related to the seventh oil type classified by the MG content of fresh oil, comparing the measured value of the amount of polar compounds with the case where the amount of polar compounds is calculated from each of Formula (31), Formula (32), Formula (47), and Formula (48). [Figure 248] A graph showing the correlation between the acid value of fried oil and the rate of increase in the viscosity of fried oil related to the seventh oil type classified by the MG content of fresh oil, comparing the measured value of the acid value with the case where the acid value is calculated from each of Formula (31), Formula (32), Formula (47), and Formula (48). [Figure 249] A graph showing the correlation between the color of fried oil and the rate of increase in the viscosity of fried oil related to the seventh oil type classified by the MG content of fresh oil, comparing the measured value of the color with the case where the color is calculated from each of Formula (31), Formula (32), Formula (47), and Formula (48). [Figure 250] A graph showing the correlation between the amount of polar compounds contained in fried oil and the color of fried oil related to the seventh oil type classified by the MG content of fresh oil, comparing the measured value of the amount of polar compounds with the case where the amount of polar compounds is calculated from each of Formula (31), Formula (32), Formula (47), and Formula (48). [Figure 251] A graph showing the correlation between the rate of increase in the viscosity of fried oil and the color of fried oil related to the seventh oil type classified by the MG content of fresh oil, comparing the measured value of the rate of increase in viscosity with the case where the rate of increase in viscosity is calculated from each of Formula (31), Formula (32), Formula (47), and Formula (48). [Figure 252] A graph showing the correlation between the rate of increase in the viscosity of fried oil and the amount of polar compounds contained in fried oil related to the eighth oil type classified by the MG content of fresh oil, comparing the measured value of the rate of increase in viscosity with the case where the rate of increase in viscosity is calculated from each of Formula (31), Formula (32), Formula (49), and Formula (50). [Figure 253]This graph shows the correlation between the amount of polar compounds contained in frying oil for eight types of oil classified by the MG content of new oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 254] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for eight types of oil classified by the MG content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 255] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for eight types of oil classified by the MG content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 256] This graph shows the correlation between the viscosity increase rate of frying oil for eight types of oil classified by the MG content of new oil and the amount of polar compounds contained in the frying oil, comparing the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 257] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of frying oil classified by the MG content of new oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50), respectively. [Figure 258] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the MG content of new oil, comparing the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 259]This graph shows the correlation between the amount of polar compounds contained in frying oil and the color of the frying oil for eight types of oil classified by the MG content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 260] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the MG content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 261] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the seventh oil type, classified by the FFA content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using equations (31), (32), (47), and (48), respectively. [Figure 262] This graph shows the correlation between the amount of polar compounds contained in frying oil for the seventh type of oil, classified by the FFA content of new oil, and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 263] This graph shows the correlation between the acid value of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil classified by the FFA content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 264] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for the seventh type of oil classified by the FFA content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 265]This graph shows the correlation between the viscosity increase rate of frying oil for the seventh oil type, classified by the FFA content of new oil, and the amount of polar compounds contained in the frying oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using equations (31), (32), (47), and (48), respectively. [Figure 266] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of frying oil for the seventh type of oil classified by the FFA content of new oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (47), and (48). [Figure 267] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the FFA content of the new oil, comparing the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 268] This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil classified by the FFA content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 269] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the FFA content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 270] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the eighth oil type classified by the FFA content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 271]This graph shows the correlation between the amount of polar compounds contained in frying oil for eight types of oil classified by the FFA content of new oil and the color of the frying oil, comparing the measured color with the color calculated using formulas (31), (32), (49), and (50), respectively. [Figure 272] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for eight types of oil classified by the FFA content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 273] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for eight types of oil classified by the FFA content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 274] This graph shows the correlation between the viscosity increase rate of frying oil for eight types of oil classified by the FFA content of new oil and the amount of polar compounds contained in the frying oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using equations (31), (32), (49), and (50), respectively. [Figure 275] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of oil classified by the FFA content of new oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50). [Figure 276] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the FFA content of new oil, comparing the measured color with the color calculated using formulas (31), (32), (49), and (50), respectively. [Figure 277]This graph shows the correlation between the color of the frying oil and the amount of polar compounds contained in the frying oil for eight types of oil classified by the FFA content of new oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 278] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the FFA content of new oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 279] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil classified by the MG content of the heating oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 280] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the seventh type of oil classified by the MG content of the heating oil, comparing the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 281] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for the seventh type of oil classified by the MG content of the heating oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 282] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for the seventh type of oil classified by the MG content of the heating oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 283]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the seventh type of oil classified by the MG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 284] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the seventh type of frying oil, classified by the MG content of the heated oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (47), and (48). [Figure 285] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the MG content of the heating oil, comparing the measured color with the color calculated from formulas (31), (32), (47), and (48), respectively. [Figure 286] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the seventh type of oil classified by the MG content of the heating oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 287] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the MG content of the heating oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 288] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the eighth type of oil classified by the MG content of the heating oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 289]This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the MG content of the heating oil. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 290] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for eight types of oil classified by the MG content of the heating oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 291] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for eight types of oil classified by the MG content of the heating oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 292] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the eighth type of frying oil classified by the MG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 293] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of frying oil classified by the MG content of the heated oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50). [Figure 294] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the MG content of the heated oil, comparing the measured color values ​​with the colors calculated using formulas (31), (32), (49), and (50). [Figure 295]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for eight types of oil classified by the MG content of the heating oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 296] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the MG content of the heating oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 297] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil classified by the TG content of the heating oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 298] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the seventh type of oil classified by the TG content of the heating oil, comparing the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 299] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for the seventh type of oil classified by the TG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 300] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for the seventh type of oil classified by the TG content of the heated oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 301]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the seventh type of frying oil classified by the TG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using equations (31), (32), (47), and (48), respectively. [Figure 302] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the seventh type of frying oil, classified by the TG content of the heated oil. It compares the measured acid value with the acid value calculated using formulas (31), (32), (47), and (48). [Figure 303] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the TG content of the heated oil, comparing the measured color with the color calculated from formulas (31), (32), (47), and (48), respectively. [Figure 304] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the seventh type of oil classified by the TG content of the heating oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 305] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the TG content of the heated oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 306] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the eighth type of oil classified by the TG content of the heated oil. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 307]This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the TG content of the heating oil. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 308] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for eight types of oil classified by the TG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 309] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the frying oil for eight types of oil classified by the TG content of the heated oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 310] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the eighth type of frying oil classified by the TG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using equations (31), (32), (49), and (50), respectively. [Figure 311] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of frying oil classified by the TG content of the heated oil, comparing the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50). [Figure 312] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the TG content of the heated oil, comparing the measured color values ​​with the colors calculated using formulas (31), (32), (49), and (50), respectively. [Figure 313]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for eight types of oil classified by the TG content of the heated oil. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 314] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the TG content of the heated oil, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 315] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 316] This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the seventh type of oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 317] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for the seventh type of oil classified by the rate of increase in DG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 318] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the seventh type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 319]This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of frying oil for the seventh type of oil classified by the rate of increase in DG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (47), and (48), respectively. [Figure 320] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the seventh type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured acid value with the acid value calculated using formulas (31), (32), (47), and (48), respectively. [Figure 321] This graph shows the correlation between the color of frying oil and the viscosity increase rate of the seventh type of oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 322] This graph shows the correlation between the amount of polar compounds contained in frying oil and the color of the frying oil for the seventh type of oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (47), and (48), respectively. [Figure 323] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the rate of increase in DG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 324] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 325]This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 326] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of eight types of frying oil classified by the rate of increase in DG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 327] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the eighth type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 328] This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of frying oil for the eighth type of oil classified by the rate of increase in DG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (49), and (50), respectively. [Figure 329] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50), respectively. [Figure 330] This graph shows the correlation between the color of frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of increase in DG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 331]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the eighth type of oil classified by the rate of increase in DG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 332] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the rate of increase in DG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 333] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the seventh type of oil, which is classified according to the rate of increase in FFA content due to heating. The graph compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 334] This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 335] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for the seventh type of oil classified by the rate of increase in FFA content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 336] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the seventh type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (47), and (48), respectively. [Figure 337]This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of frying oil for the seventh type of oil classified by the rate of increase in FFA content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (47), and (48), respectively. [Figure 338] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the seventh type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured acid value with the acid value calculated using formulas (31), (32), (47), and (48), respectively. [Figure 339] This graph shows the correlation between the color of frying oil and the viscosity increase rate of the seventh type of oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured color with the color calculated from formulas (31), (32), (47), and (48), respectively. [Figure 340] This graph shows the correlation between the amount of polar compounds contained in frying oil and the color of the frying oil for the seventh type of oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (47), and (48), respectively. [Figure 341] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the rate of increase in FFA content due to heating, comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 342] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the eighth oil type, which is classified according to the rate of increase in FFA content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 343]This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50). [Figure 344] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 345] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 346] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (49), and (50), respectively. [Figure 347] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50), respectively. [Figure 348] This graph shows the correlation between the color of frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of increase in FFA content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50), respectively. [Figure 349]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the eighth type of oil classified by the rate of increase in FFA content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 350] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the rate of increase in FFA content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 351] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 352] This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the seventh type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 353] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of frying oil for the seventh type of oil classified by the rate of decrease in TG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (47), and (48), respectively. [Figure 354] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the seventh type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 355]This graph shows the correlation between the amount of polar compounds contained in frying oil and the viscosity increase rate of frying oil for the seventh type of oil classified by the rate of decrease in TG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (47), and (48), respectively. [Figure 356] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the seventh type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured acid value with the acid value calculated using formulas (31), (32), (47), and (48), respectively. [Figure 357] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the seventh type of oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (47), and (48). [Figure 358] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the seventh type of oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (47), and (48), respectively. [Figure 359] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for the seventh type of oil classified by the rate of decrease in TG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (47), and (48), respectively. [Figure 360] This graph shows the correlation between the viscosity increase rate of frying oil and the amount of polar compounds contained in the frying oil for the eighth oil type, which is classified according to the rate of decrease in TG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (49), and (50), respectively. [Figure 361]This graph shows the correlation between the color of frying oil and the amount of polar compounds contained in the eighth type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured color with the color calculated using formulas (31), (32), (49), and (50), respectively. [Figure 362] This graph shows the correlation between the amount of polar compounds contained in frying oil and the acid value of the eighth type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 363] This graph shows the correlation between the viscosity increase rate of frying oil and the acid value of the eighth type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 364] This graph shows the correlation between the amount of polar compounds contained in the frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated from equations (31), (32), (49), and (50), respectively. [Figure 365] This graph shows the correlation between the acid value of frying oil and the viscosity increase rate of the eighth type of frying oil, which is classified according to the rate of decrease in TG content due to heating. It compares the measured acid value with the acid value calculated using formulas (31), (32), (49), and (50), respectively. [Figure 366] This graph shows the correlation between the color of the frying oil and the viscosity increase rate of the eighth type of oil classified by the rate of decrease in TG content due to heating, comparing the measured color with the color calculated from formulas (31), (32), (49), and (50), respectively. [Figure 367]This graph shows the correlation between the amount of polar compounds contained in the frying oil and the color of the frying oil for the eighth type of oil classified by the rate of decrease in TG content due to heating. It compares the measured amount of polar compounds with the amount of polar compounds calculated using formulas (31), (32), (49), and (50), respectively. [Figure 368] This graph shows the correlation between the viscosity increase rate of frying oil and the color of the frying oil for eight types of oil classified by the rate of decrease in TG content due to heating. It compares the measured viscosity increase rate with the viscosity increase rate calculated using formulas (31), (32), (49), and (50), respectively. [Figure 369] This graph shows the correlation between the amount of polar compounds in the frying oil and the color of the frying oil, taking into account the type of food being fried. [Figure 370] This graph shows the correlation between the acid value of the frying oil and the color of the frying oil, taking into account the type of food being fried. [Figure 371] This graph shows the correlation between the color of the frying oil and the rate of viscosity increase, taking into account the type of food being fried. [Figure 372] This is a functional block diagram showing the functions of the cloud according to the 15th embodiment. [Figure 373] This flowchart shows the processing flow executed in the cloud according to the 15th embodiment. [Modes for carrying out the invention]

[0053] Hereinafter, as one embodiment of the oil deterioration detection system according to each embodiment of the present invention, we will describe a system that can be applied, for example, when cooking fried foods such as fried chicken, croquettes, and karaage (Japanese fried chicken) using edible oil in convenience stores, supermarkets, and the like.

[0054] Furthermore, in the following explanation, the cooking of fried food will be referred to as "deep-frying," the cooking oil used for deep-frying will be referred to as "deep-frying oil," and the ingredients that are deep-fried will be referred to as "deep-fried ingredients."

[0055] (Configuration of Kitchen 1) First, let's look at an example of the environment in which deep-frying takes place, referring to Figure 1.

[0056] Figure 1 shows a part of the kitchen 1 where deep frying is performed.

[0057] For example, retail stores such as convenience stores and supermarkets have a kitchen 1 within the store where fried food is prepared and sold to customers. Kitchen 1 is equipped with cooking equipment such as an electric fryer 2 for use in frying. Note that the fryer 2 does not necessarily have to be electric; it could also be gas-powered, for example.

[0058] The fryer 2 comprises an oil tank 21 for storing frying oil P and a housing 22 that houses the oil tank 21. Multiple operation switches 22A are provided on the side of the housing 22, including setting switches for setting the temperature of the frying oil P and the details of the frying process according to the type of food Q to be fried, and a start switch for starting the frying process.

[0059] When frying food in fryer 2, the cook first places the food to be fried Q into the fry basket 3, which has a handle 30, and hooks the handle 30 onto the upper end of the housing 22 so that the food to be fried Q in the fry basket 3 is submerged in the frying oil P. At the same time as or before or after this, the cook operates one of the multiple operating switches 22A that corresponds to the type of food to be fried Q.

[0060] Next, the fryer 2 identifies the operation switch 22A operated by the cook, and when the frying time associated with the operated operation switch 22A has elapsed, it notifies the cook that the frying is complete. At the same time, the fry basket 3 containing the fried food (fried ingredients Q after frying) automatically rises from the oil tank 21, lifting the fried food out of the frying oil P.

[0061] Methods for notifying the user when the fried food is ready include, for example, emitting a buzzer sound from the speaker on fryer 2, or displaying the information on a monitor installed near fryer 2.

[0062] When the cook senses that the frying is complete, they lift the fry basket 3 to remove the fried food. In this case, the lifting of the fry basket 3 from the oil tank 21 may be automated by providing a drive mechanism on the fryer 2 side.

[0063] Users of frying oil P (e.g., cooks or store employees) can measure the degradation index of frying oil P using various measuring devices, and manage the quality of frying oil P and the fried food fried in it by determining the degree of degradation of frying oil P based on the measured values ​​of the degradation index of frying oil P and predicting the degree of degradation.

[0064] Degradation indicators for frying oil P are indicators that change with the passage of heating time for frying oil P, and include, for example, the acid value (AV) of frying oil P, the amount of polar compounds (PC) of frying oil P, the color of frying oil P, the viscosity of frying oil P, the viscosity increase rate of frying oil P, the anisidine value of frying oil P, the carbonyl value of frying oil P, the smoke point of frying oil P, the tocopherol content of frying oil P, the iodine value of frying oil P, the refractive index of frying oil P, the amount of volatile components of frying oil P, the volatile component composition of frying oil P, the flavor of frying oil P, the amount of volatile components of fried food fried in frying oil P, the volatile component composition of fried food fried in frying oil P, and the flavor of fried food fried in frying oil P.

[0065] Among these various degradation indicators, the amount of polar compounds in the frying oil P can be measured directly by immersing a PC sensor 41 (see Figure 1), which measures the amount of polar compounds in the frying oil P based on its capacitance, into the frying oil P. This method is simple yet provides highly accurate measurements.

[0066] Furthermore, in the kitchen 1 shown in Figure 1, a camera 42 is mounted on the ceiling above the oil tank 21 to capture images of the surface of the frying oil P inside the oil tank 21. Note that the camera 42 does not necessarily have to be mounted on the ceiling above the oil tank 21; it may be mounted on a wall near the fryer 2, for example, as long as it is able to capture images of the inside of the oil tank 21.

[0067] This camera 42 is a video camera for shooting video and a still camera for shooting still images, and is used to detect the ingredients Q in the frying oil P. Therefore, the image captured by the camera 42 only needs to be an image that can confirm whether or not the ingredients Q are immersed in the oil tank 21, and may also include images other than the surface of the frying oil P, such as a part of the oil tank 21 or a part of the fry basket 3 immersed in the frying oil P.

[0068] (Configuration of the oil and fat degradation detection system 5) Next, the configuration of the oil and fat degradation detection system 5 will be explained with reference to Figure 2.

[0069] Figure 2 is a system configuration diagram showing one example of the configuration of the oil and fat deterioration degree detection system 5 according to each embodiment of the present invention.

[0070] The oil degradation detection system 5 is a system that detects the degree of degradation of frying oil P based on the amount of polar compounds contained in the frying oil P. In the following, the oil degradation detection system 5 detects the acid value (AV [mg KOH / g]) of frying oil P, which is derived based on the amount of polar compounds (PC [%TPM]) contained in the frying oil P, as the degree of degradation of the frying oil P.

[0071] As shown in Figure 2, the oil deterioration detection system 5 includes store terminals 6 installed in each of the multiple stores that make up, for example, a convenience store chain or a supermarket chain, a headquarters terminal 7 installed in a headquarters center that oversees the multiple stores, and a cloud 8 that executes an oil deterioration detection program to detect the degree of deterioration of the frying oil P used in each store.

[0072] The store terminal 6, the headquarters terminal 7, and the cloud 8 are connected to each other via a communication network, such as an internet connection, enabling them to communicate with one another. The PC sensor 41 and camera 42 are also connected to the cloud 8, enabling them to communicate with each other. As a result, the measured values ​​of polar compounds in the frying oil P measured by the PC sensor 41 and the images captured by the camera 42 are transmitted directly to the cloud 8.

[0073] Note that the PC sensor 41 and camera 42 do not necessarily need to be connected to the cloud 8 in a way that enables communication. For example, if the PC sensor 41 and camera 42 are each connected to the store terminal 6 in a way that enables communication, the measured value of the polar compound amount in the frying oil P measured by the PC sensor 41 and the image captured by the camera 42 can be transmitted to the cloud 8 via the store terminal 6.

[0074] In the oil degradation detection system 5, the functions of each store terminal 6 in multiple stores are all the same. Therefore, the following explanation will use the store terminal 6 of any store as an example, and the explanation of the store terminals 6 of other stores will be omitted.

[0075] The store terminal 6 is an input terminal into which information about the store and information about the frying oil P is entered, and is also a notification device that notifies (including text display and sound notification) of various information output from the headquarters terminal 7 and the cloud 8, and has an application (frying oil management app) installed for managing the frying oil P used in the store.

[0076] The headquarters terminal 7 acquires information output from each store's store terminal 6 and the cloud 8, and manages the amount of frying oil P used at each store and performs hygiene management at each store. In addition, the headquarters terminal 7, like the store terminal 6, is also a notification device that notifies (including text display and sound notification) of various information output from each store terminal 6 and the cloud 8.

[0077] Cloud 8 is an embodiment of a fat degradation detection device that detects the degree of degradation of frying oil P based on the amount of polar compounds in the frying oil P. Specifically, Cloud 8 stores the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P, acquires the measured value of the amount of polar compounds in frying oil P measured by the PC sensor 41 (data acquisition process), calculates the acid value of frying oil P based on the acquired measured value of the amount of polar compounds in frying oil P and the stored correlation (degradation index calculation process), and performs a process to output the calculated acid value of frying oil P as the degree of degradation of frying oil P to the store terminal 6 and the headquarters terminal 7 (detection result output process).

[0078] The computers that implement Cloud 8 (for example, computers owned by companies that provide cloud systems) have a hardware configuration consisting of a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), HDD (Hard Disk Drive), and I / F (Interface). Each of these components is connected via a common bus.

[0079] The CPU is the processing unit that controls the overall operation of Cloud8. RAM is a volatile storage medium that allows for high-speed reading and writing of information, and is used, for example, as a workspace when the CPU processes information. ROM is a read-only, non-volatile storage medium that stores programs such as firmware.

[0080] An HDD is a non-volatile storage medium with a large storage capacity that allows for reading and writing of information. It stores the OS (Operating System), control programs for executing various information processing tasks (described later), and application programs. Note that any device that provides the function of storing and managing information as a non-volatile storage medium can be used as a substitute for an HDD, such as an SSD (Solid State Drive).

[0081] The I / F is a connection interface to the communication network, to which each store terminal 6, the headquarters terminal 7, the PC sensor 41, and the camera 42 are connected.

[0082] The computer that realizes this Cloud 8 is an information processing device that uses the arithmetic functions of the CPU to process control programs stored in ROM, and control programs and application programs loaded into RAM from storage media such as HDDs.

[0083] The execution of these information processing processes constitutes a software control unit in Cloud 8 that includes various functions. The combination of the software control unit thus configured and the hardware resources including the above configuration constitutes a functional block that realizes the functions of Cloud 8.

[0084] Furthermore, the oil degradation detection device does not necessarily have to be configured with Cloud 8; it may also be configured with a server device. If the oil degradation detection device is configured with a server device, the server device will have the hardware configuration described above.

[0085] The following describes the functions of Cloud8 and the processes performed on Cloud8 for each embodiment.

[0086] <First Embodiment> Cloud 8 according to the first embodiment of the present invention will be described with reference to Figures 3 to 6.

[0087] (Correlation between the amount of polar compounds and the acid value) First, the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P, as stored in Cloud 8, will be explained with reference to Figures 3 and 4.

[0088] Figure 3 is a graph of a linear function showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P. Figure 4 is a graph of a quadratic function showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P.

[0089] There is a correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P, as shown in the graphs in Figures 3 and 4. Specifically, the higher the amount of polar compounds in frying oil P, the higher the acid value of frying oil P. In other words, the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P is expressed by a correlation equation in which the acid value of frying oil P is expressed as a polynomial of the amount of polar compounds.

[0090] Specifically, if PC is the amount of polar compounds in frying oil P, AV is the acid value of frying oil P, and n is an arbitrary heating time of frying oil P, then the correlation equation between the amount of polar compounds PCn in frying oil P and the acid value AVn of frying oil P at an arbitrary heating time n is given by a linear equation (1) or a quadratic equation (2). AVn = α × (PCn) + β ... (1) AVn = γ × (PCn) 2 +δ×(PCn)+ε···(2)

[0091] Formula (1) is the correlation equation corresponding to the correlation graph shown in Figure 3, and formula (2) is the correlation equation corresponding to the correlation graph shown in Figure 4. By substituting the measured amount of polar compounds at an arbitrary heating time n of the frying oil P into PCn for each of formulas (1) and (2), the acid value AVn at an arbitrary heating time n of the frying oil P can be calculated.

[0092] Note that the linear coefficient α and constant β of PCn in equation (1), and the quadratic coefficient γ, linear coefficient δ, and constant ε of PCn in equation (2), respectively, may be arbitrary fixed values ​​set in advance, or they may be values ​​that fluctuate depending on the usage environment of the frying oil P and the type of frying oil P (oil type). The latter will be explained in detail in the second to seventh embodiments.

[0093] (Cloud 8 Functional Configuration) Next, we will explain the functional configuration of Cloud 8 with reference to Figure 5.

[0094] Figure 5 is a functional block diagram showing the functions of the cloud 8 according to the first embodiment.

[0095] Cloud 8 includes a data acquisition unit 81, a storage unit 82, a degradation index calculation unit 83, and a detection result output unit 84.

[0096] The data acquisition unit 81 acquires the measured value of the polar compound amount of the frying oil P output from the PC sensor 41. In this embodiment, the measured value of the polar compound amount of the frying oil P is measured by the PC sensor 41, but is not limited to this. For example, it may be measured using measurement methods or analysis methods using various measuring devices other than the PC sensor 41, such as the polar compound amount (column chromatography method) described in 2.5.5-2013 of the Standard Method for Analysis of Fats and Oils established by the Japan Oil Chemists' Society.

[0097] The memory unit 82 stores the correlation formula between the amount of polar compounds in the frying oil P and the acid value of the frying oil P, specifically, formula (1) or formula (2). The memory unit 82 may store both formula (1) and formula (2), or it may store only one of formula (1) or formula (2).

[0098] The deterioration index calculation unit 83 calculates the acid value of the frying oil P based on the measured value of the polar compound amount of the frying oil P obtained by the data acquisition unit 81 and the correlation formula of the acid value of the frying oil P with respect to the polar compound amount of the frying oil P read from the storage unit 82.

[0099] Specifically, the degradation index calculation unit 83 substitutes the measured value of polar compounds in the frying oil P obtained by the data acquisition unit 81 into PCn of formula (1) or formula (2) read from the storage unit 82, and calculates the acid value AVn of the frying oil P.

[0100] If both formula (1) and formula (2) are stored in the memory unit 82, the degradation index calculation unit 83 selects either formula (1) or formula (2), and substitutes the measured value of the polar compound amount of the frying oil P into PCn in the selected formula to calculate the acid value AVn of the frying oil P.

[0101] The detection result output unit 84 outputs the acid value of the frying oil P calculated by the deterioration index calculation unit 83 as the detection result of the degree of deterioration of the frying oil P to the store terminal 6 and the headquarters terminal 7, respectively. In this embodiment, the detection result output unit 84 outputs the detection result of the degree of deterioration of the frying oil P to both the store terminal 6 and the headquarters terminal 7, but it is not limited to this, and the detection result of the degree of deterioration of the frying oil P may be output to only one of the store terminal 6 or the headquarters terminal 7.

[0102] (Processing performed within Cloud 8) Next, we will explain the processing flow executed within Cloud 8, referring to Figure 6.

[0103] Figure 6 is a flowchart showing the processing flow executed in Cloud 8 according to the first embodiment.

[0104] As shown in Figure 6, Cloud 8 first has a data acquisition unit 81 that acquires the measured value of the amount of polar compounds in the frying oil P measured by the PC sensor 41 in the measurement step (step S801; data acquisition step).

[0105] Next, the deterioration index calculation unit 83 substitutes the measured value of the polar compound amount of the frying oil P obtained in step S801 into the correlation formula for the acid value of frying oil P with respect to the polar compound amount of frying oil P, i.e., PCn in formula (1) or formula (2), which is stored in the memory unit 82, to calculate the acid value AVn of the frying oil P (step S802; deterioration index calculation step).

[0106] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P calculated in step S802 to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S803; detection result output step), and the processing in the cloud 8 is completed.

[0107] Thus, Cloud 8 can convert the amount of polar compounds in frying oil P to the acid value of frying oil P using a correlation formula between the amount of polar compounds in frying oil P and the acid value of frying oil P. Therefore, store employees can obtain the degree of deterioration of frying oil P as an acid value simply by measuring the amount of polar compounds in frying oil P with the PC sensor 41.

[0108] When detecting the degree of deterioration of frying oil P, if the acid value of frying oil P is measured without using Cloud 8, store employees would have to, for example, immerse a color test piece in the frying oil P and determine the measured value of the acid value of frying oil P based on the color change of the color test piece immersed in the frying oil P. This measurement method is complicated and prone to errors in the measurement results. On the other hand, Cloud 8 calculates the acid value based on the amount of polar compounds that can be measured simply and accurately using the PC sensor 41, thus enabling the acquisition of the acid value of frying oil P with high accuracy.

[0109] <Second Embodiment> Next, Cloud 8A according to the second embodiment of the present invention will be described with reference to Figures 7 to 10. In the following description, components common to those described with Cloud 8 according to the first embodiment will be denoted by the same reference numerals and their descriptions will be omitted. The same applies to the second to fifteenth embodiments.

[0110] In this embodiment, the coefficients and constants included in the correlation equation between the amount of polar compounds in the frying oil P and the acid value of the frying oil P are set to values ​​corresponding to the weight W per unit heating time of the food item Q (ingredients) fried using the frying oil P (hereinafter referred to as "amount of fried food per unit time W").

[0111] Figure 7 is a linear function graph showing the correlation between the acid value of the frying oil and the amount of polar compounds contained in the frying oil P, taking into account the amount of frying per hour W. Figure 8 is a quadratic function graph showing the correlation between the acid value of the frying oil and the amount of polar compounds contained in the frying oil P, taking into account the amount of frying per hour W.

[0112] As shown in Figures 7 and 8, the graph showing the correlation between the acid value of frying oil P and the amount of polar compounds in frying oil P has a different slope depending on the amount W fried per hour of the food item Q fried using frying oil P. Therefore, the linear coefficient α of PCn in equation (1), and the quadratic coefficient γ and linear coefficient δ of PCn in equation (2), are set to values ​​corresponding to the amount W fried per unit time of the food item Q fried using frying oil P.

[0113] Furthermore, since the amount of fried food Q fried per unit time W at a store can be calculated based on the daily sales of the store using frying oil P, the linear coefficient α of PCn in formula (1), and the quadratic coefficient γ and linear coefficient δ of PCn in formula (2) can be values ​​corresponding to the daily sales of the store using frying oil P. The daily sales of a store can be calculated, for example, by taking the average daily value from the total sales of the store over the past year, or by taking the average daily value from the total sales of the store over a predetermined period (such as each season). In addition, it is preferable that the daily sales of the store be the amount extracted from the sales of fried food cooked using frying oil P.

[0114] Figures 7 and 8 classify the amount of fried food per hour W in stores into three ranges: "high," "medium," and "low." The correlation graph for the "low" case (W < 2000), where the amount of fried food per hour W is less than 2,000g, is shown with multiple circles; the correlation graph for the "medium" case (2000 ≤ W < 12000), where the amount of fried food per hour W is 2,000g or more but less than 12,000g, is shown with multiple triangles; and the correlation graph for the "high" case (W ≥ 12000), where the amount of fried food per hour W is 12,000g or more, is shown with multiple dashes.

[0115] The slope of the correlation graph when the amount of fried food per hour W is "low" is the smallest of the three correlation graphs. The slope of the correlation graph when the amount of fried food per hour W is "medium" is greater than the slope of the correlation graph when the amount of fried food per hour W is "low". The slope of the correlation graph when the amount of fried food per hour W is "high" is even greater than the slope of the correlation graph when the amount of fried food per hour W is "medium", making it the largest of the three correlation graphs.

[0116] Therefore, in the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P, the linear coefficient α of PCn in equation (1), and the quadratic coefficient γ and linear coefficient δ of PCn in equation (2), are set to be larger as the amount of frying W per unit time increases.

[0117] The amount of polar compounds in the frying oil P increases with the heating time in the fryer 2 (total heating time of the fryer 2, including the time spent frying the food Q and the time spent preheating the fryer), and the acid value of the frying oil P increases with the amount of food Q fried. Therefore, the correlation between the amount of polar compounds in the frying oil P and the acid value of the frying oil P can be made more accurate by considering both the heating time in the fryer 2 and the amount of food Q fried.

[0118] Note that the "unit time" does not necessarily have to be one hour; it can be any time set arbitrarily. Also, the classification of the amount of fried food W per unit time in a store does not necessarily have to be based on the thresholds of "2,000g" and "12,000g" used in the correlations shown in Figures 7 and 8; any value can be used as the threshold for each store.

[0119] Figure 9 is a functional block diagram showing the functions of Cloud 8A according to the second embodiment.

[0120] The cloud 8A according to this embodiment includes a data acquisition unit 81A, a frying amount determination unit 85, a storage unit 82A, a deterioration index calculation unit 83A, and a detection result output unit 84.

[0121] The data acquisition unit 81A acquires not only the measured value of polar compounds in the frying oil P output from the PC sensor 41, but also frying information output from the store terminal 6. This "frying information" includes the heating time in the fryer 2 and the weight of the food to be fried Q in the fryer 2 (i.e., the amount of food to be fried Q). Note that the frying information does not necessarily have to be output from the store terminal 6; for example, the headquarters terminal 7 may output the information acquired from the store terminal 6, or it may be output from a separate management terminal that manages the fryer 2.

[0122] The frying amount determination unit 85 calculates the frying amount W per unit time (1 hour in this embodiment) based on the frying cooking information acquired by the data acquisition unit 81A, determines the classification of the frying amount W per unit time in the store (in this embodiment, "low", "medium", or "high" for the frying amount W per hour), and sets the linear coefficient α of PCn in formula (1) or the quadratic coefficient γ and linear coefficient δ of PCn in formula (2), stored in the storage unit 82A, to values ​​corresponding to the frying amount W per unit time. As a result, formula (1) or formula (2) stored in the storage unit 82A is updated.

[0123] In this embodiment, Cloud 8A calculates the amount of fried food per unit time (1 hour) W based on the frying information output from the store terminal 6 and determines the classification of the amount of fried food per unit time W at the store. However, it is not limited to this, and the classification of the amount of fried food per unit time W may be performed on the store terminal 6 side, and Cloud 8A may set the linear coefficient α of PCn in formula (1), or the quadratic coefficient γ and linear coefficient δ of PCn in formula (2), respectively, based on the classification information output from the store terminal 6. In other words, Cloud 8A does not necessarily need to have a function to determine the classification of the amount of fried food per unit time W.

[0124] The deterioration index calculation unit 83A calculates the acid value of the frying oil P based on the measured value of the polar compound amount of the frying oil P obtained by the data acquisition unit 81A and either formula (1) in which the linear coefficient α of PCn is set by the frying amount determination unit 85, or formula (2) in which the quadratic coefficient γ and linear coefficient δ of PCn are set by the frying amount determination unit 85.

[0125] Similar to the first embodiment, the detection result output unit 84 outputs the acid value of the frying oil P calculated by the deterioration index calculation unit 83A to the store terminal 6 and the headquarters terminal 7, respectively, as the detection result of the degree of deterioration of the frying oil P.

[0126] Figure 10 is a flowchart showing the processing flow executed in Cloud 8A according to the second embodiment.

[0127] In Cloud 8A, first, the data acquisition unit 81A acquires the frying cooking information output from the store terminal 6 (step S811; frying cooking information acquisition step).

[0128] Next, the frying amount determination unit 85 calculates the frying amount W per unit time based on the frying cooking information acquired in step S811 and determines the classification of the frying amount W per unit time in the store (step S812; frying amount determination step). It then sets the linear coefficient α of PCn in formula (1) or the quadratic coefficient γ and linear coefficient δ of PCn in formula (2), stored in the storage unit 82A, to values ​​corresponding to the frying amount W per unit time (step S812; parameter setting step). As a result, formula (1) or formula (2) stored in the storage unit 82A is updated.

[0129] Next, the data acquisition unit 81A acquires the measured value of the amount of polar compounds in the frying oil P output from the PC sensor 41 (step S813; data acquisition step).

[0130] Next, the deterioration index calculation unit 83A substitutes the measured value of polar compounds in the frying oil P obtained in step S813 into PCn of formula (1) or formula (2) updated in step S812, and calculates the acid value AVn of the frying oil P (step S814; deterioration index calculation step).

[0131] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P calculated in step S814 to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S815; detection result output step), and the processing in the cloud 8A is completed.

[0132] In this embodiment, Cloud 8A calculates the acid value of the frying oil P using formula (1) or formula (2) corresponding to the amount of frying W per unit time at the store. Therefore, it can calculate the acid value of the frying oil P with higher accuracy than when using a predetermined formula (1) or formula (2).

[0133] <Third Embodiment> Next, a third embodiment of the present invention, Cloud 8B, will be described with reference to Figures 11 to 14.

[0134] Figure 11 is a linear function graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P, depending on whether or not a preheating time is performed. Figure 12 is a quadratic function graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P, depending on whether or not a preheating time is performed.

[0135] As shown in Figures 11 and 12, the graph showing the correlation between the acid value of frying oil P and the amount of polar compounds in frying oil P differs depending on whether or not the frying oil P has been preheated. This "preheating" means heating only the frying oil P without frying the ingredients Q, that is, without adding the ingredients Q to the frying oil P.

[0136] In Figures 11 and 12, the correlation graph when the frying oil P is not preheated is shown with multiple circles, and the correlation graph when the frying oil P is preheated is shown with multiple triangles.

[0137] In the correlation graph shown in Figure 11, when frying oil P is subjected to air heating, the rate of increase in acid value tends to be smaller than the rate of increase in polar compounds, and the acid value decreases by EH1. Similarly, in the correlation graph shown in Figure 12, when frying oil P is subjected to air heating, the rate of increase in acid value tends to be smaller than the rate of increase in polar compounds, and the acid value decreases by EH2.

[0138] Here, "EH1" and "EH2" correspond to air heating variables, respectively, that are set considering the air heating of the frying oil P. The air heating variables increase as the air heating time increases.

[0139] Thus, when the frying oil P is subjected to air heating, the correlation equation between the amount of polar compounds in the frying oil P and the acid value of the frying oil P is expressed as the following equation (3), which is a linear equation obtained by subtracting the air heating variable EH1 from equation (1), or as the following equation (4), which is a quadratic equation obtained by subtracting the air heating variable EH2 from equation (2). AVn = α × (PCn) + β - EH1···(3) AVn = γ × (PCn) 2 +δ×(PCn)+ε-EH2···(4)

[0140] Figure 13 is a functional block diagram showing the functions of Cloud 8B according to the third embodiment.

[0141] The cloud 8B according to this embodiment includes a data acquisition unit 81B, an air heating determination unit 86, a storage unit 82B, a deterioration index calculation unit 83B, and a detection result output unit 84.

[0142] The data acquisition unit 81B acquires not only the measured amount of polar compounds in the frying oil P output from the PC sensor 41, but also a surface image of the frying oil P output from the camera 42.

[0143] The preheating determination unit 86 determines whether or not preheating has been performed on the frying oil P based on the surface image of the frying oil P acquired by the data acquisition unit 81B. As described above, preheating is the heating of frying oil P when no ingredients Q have been added, so the preheating determination unit 86 determines that the time when the ingredients Q are not included in the surface image of the frying oil P captured by the camera 42 is the time when preheating has been performed.

[0144] Furthermore, the method for determining whether or not preheating has occurred does not necessarily have to be based on the presence or absence of fried food Q in the surface image of the frying oil P captured by the camera 42. For example, a temperature sensor may be attached to the fryer 2, and preheating may be determined when the temperature measured by the temperature sensor falls below a predetermined temperature (preheating temperature). Alternatively, for example, a weight sensor may be attached to the fryer 2, and preheating may be determined based on the increase or decrease in weight measured by the weight sensor.

[0145] Furthermore, for example, stores record information such as the type and quantity of ingredients Q to be fried, and the time of frying, and it is possible to determine whether or not preheating has been performed based on this record. Alternatively, for example, it is possible to determine whether or not preheating has been performed by calculating the preheating time based on the store's pre-registered daily frying schedule.

[0146] Furthermore, since frying is initiated when the operation switch 22A of the fryer 2 is operated, it is possible to determine whether or not preheating has been performed based on the operation of the operation switch 22A. Alternatively, it is possible to determine whether or not preheating has been performed based on the amount of electricity or gas consumed by the fryer 2.

[0147] In addition to formula (1) or formula (2), formula (3) or formula (4) are stored in memory unit 82B as correlation formulas between the amount of polar compounds contained in the frying oil P and the acid value of the frying oil P.

[0148] If the preheating determination unit 86 determines that preheating has been performed on the frying oil P, it sets the preheating variable EH1 of formula (3) or the preheating variable EH2 of formula (4) stored in the storage unit 82B.

[0149] In this embodiment, the preheating determination unit 86 determines whether or not preheating has been performed on the frying oil P. However, it is not limited to this. For example, if the data acquisition unit 81B acquires information regarding the presence or absence of preheating from a store terminal 6 or a headquarters terminal 7, the preheating determination unit 86 may set the preheating variable EH1 of formula (3) or the preheating variable EH2 of formula (4) stored in the storage unit 82B based on the information acquired by the data acquisition unit 81B (information indicating that preheating has been performed). In other words, the cloud 8B does not necessarily need to have a function to determine whether or not preheating has been performed on the frying oil P.

[0150] If the air heating determination unit 86 determines that air heating has been performed on the frying oil P, the deterioration index calculation unit 83B calculates the acid value of the frying oil P based on the measured value of the polar compound amount of the frying oil P obtained by the data acquisition unit 81B and formula (3) or formula (4) stored in the storage unit 82B.

[0151] Furthermore, if the air heating determination unit 86 determines that the frying oil P has not been air heated, the deterioration index calculation unit 83B calculates the acid value of the frying oil P based on the measured value of the polar compound amount of the frying oil P obtained by the data acquisition unit 81B and formula (1) or formula (2) stored in the storage unit 82B.

[0152] Similar to the first and second embodiments, the detection result output unit 84 outputs the acid value of the frying oil P calculated by the deterioration index calculation unit 83B as the detection result of the degree of deterioration of the frying oil P to the store terminal 6 and the headquarters terminal 7, respectively.

[0153] Figure 14 is a flowchart showing the processing flow executed in Cloud 8B according to the third embodiment.

[0154] In Cloud 8B, first, the data acquisition unit 81B acquires the measured amount of polar compounds in the frying oil P output from the PC sensor 41 and the surface image of the frying oil P output from the camera 42 (step S821; data acquisition step).

[0155] Next, the air heating determination unit 86 determines whether or not air heating has been performed on the frying oil P based on the surface image of the frying oil P acquired in step S821 (step S822; air heating determination step).

[0156] If it is determined in step S822 that the frying oil P has been preheated (step S822 / YES), the preheating determination unit 86 then sets the preheating variable EH1 of formula (3) or the preheating variable EH2 of formula (4) stored in the storage unit 82B (step S823; parameter setting step).

[0157] Next, the deterioration index calculation unit 83B substitutes the measured value of the polar compound amount of the frying oil P obtained in step S821 into PCn of formula (3) or PCn of formula (4) to calculate the acid value AVn of the frying oil P (step S824; deterioration index calculation step).

[0158] On the other hand, if it is determined in step S822 that the frying oil P has not been preheated (step S822 / NO), the deterioration index calculation unit 83B substitutes the measured value of the polar compound amount of the frying oil P obtained in step S821 into PCn of formula (1) or PCn of formula (2) stored in the storage unit 82B to calculate the acid value AVn of the frying oil P (step S825; deterioration index calculation step).

[0159] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P, calculated in step S824 or step S825, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S826; detection result output step), and the processing in the cloud 8B is completed.

[0160] In this embodiment, Cloud 8B calculates the acid value of the frying oil P by using either formula (1) or formula (2) and either formula (3) or formula (4) depending on whether or not the frying oil P has been air-heated. This allows for a more accurate calculation of the acid value of the frying oil P than when formula (1) or formula (2) is used uniformly without considering whether or not the frying oil P has been air-heated.

[0161] <Fourth Embodiment> Next, a cloud 8C according to the fourth embodiment of the present invention will be described with reference to Figures 15 to 19.

[0162] Figure 15 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the first and second types of oil. Specifically, this correlation graph shows the correlation between the acid value of frying oil P and the amount of polar compounds in frying oil P measured using the PC sensor 41.

[0163] Frying oil P is classified into two types, Type 1 and Type 2, based on the fatty acid composition that makes up Frying oil P.

[0164] The first type of oil is one in which the oleic acid content in frying oil P is greater than the linoleic acid content (oleic acid content > linoleic acid content). Examples of the first type of oil include palm oil, olive oil, peanut oil, safflower oil, and rapeseed oil.

[0165] On the other hand, the second type of oil is an oil with a composition in which the oleic acid content in frying oil P is less than or equal to the linoleic acid content (oleic acid content ≤ linoleic acid content). Examples of the second type of oil include corn oil, soybean oil, and grapeseed oil.

[0166] As shown in Figure 15, the correlation graph between the acid value of frying oil P and the amount of polar compounds contained in frying oil P shows different graphs for the first oil type and the second oil type.

[0167] In Figure 15, the correlation graph of the acid value of frying oil P with the amount of polar compounds contained in frying oil P corresponding to the first type of oil is shown with multiple circles, and the correlation graph of the acid value of frying oil P with the amount of polar compounds contained in frying oil P corresponding to the second type of oil is shown with multiple triangles.

[0168] When the frying oil P is of type 1, the correlation equation between the amount of polar compounds in the frying oil P and the acid value of the frying oil P is expressed as the following linear equation (5), which includes α1 as the linear coefficient α of PCn in equation (1) and β1 as the constant β, or as the following quadratic equation (6), which includes γ1 as the quadratic coefficient γ of PCn in equation (2), δ1 as the linear coefficient δ of PCn, and ε1 as the constant ε. AVn = α1 × (PCn) + β1 ... (5) AVn = γ1 × (PCn) 2 +δ1×(PCn)+ε1···(6)

[0169] Furthermore, when the frying oil P is of type 2, the correlation equation between the acid value of frying oil P and the amount of polar compounds in frying oil P is expressed as the following linear equation (7), which includes α² as the linear coefficient α of PCn in equation (1) and β² as the constant β, or as the following quadratic equation (8), which includes γ² as the quadratic coefficient γ of PCn in equation (2), δ² as the linear coefficient δ of PCn, and ε² as the constant ε. AVn = α² × (PCn) + β²···(7) AVn = γ² × (PCn) 2 +δ²×(PCn)+ε²···(8)

[0170] In this case, the linear coefficient α2 of PCn in equation (7) is smaller than the linear coefficient α1 of PCn in equation (5) (α2 < α1), and the constant β2 in equation (7) is smaller than the constant β1 in equation (5) (β2 < β1). Also, the quadratic coefficient γ2 of PCn in equation (8) is smaller than the quadratic coefficient γ1 of PCn in equation (6) (γ2 < γ1), the linear coefficient δ2 of PCn in equation (8) is smaller than the linear coefficient δ1 of PCn in equation (6) (δ2 < δ1), and the constant ε2 in equation (8) is smaller than the constant ε1 in equation (6) (ε2 < ε1).

[0171] Therefore, in particular, when the amount of polar compounds in frying oil P is measured with the PC sensor 41, there is a difference in the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P between the first type of oil and the second type of oil. Using a formula that takes this difference into account (formula (5) or formula (6) for the first type of oil, and formula (7) or formula (8) for the second type of oil) allows for a more accurate calculation of the acid value of frying oil P.

[0172] Figure 16 is a functional block diagram showing the functions of Cloud 8C according to the fourth embodiment.

[0173] The cloud 8C according to this embodiment includes a data acquisition unit 81C, an oil type discrimination unit 87, a storage unit 82C, a deterioration index calculation unit 83C, and a detection result output unit 84.

[0174] The data acquisition unit 81C acquires not only the measured value of polar compounds in the frying oil P output from the PC sensor 41, but also information related to the fatty acid composition of the frying oil P output from the store terminal 6. This information related to the fatty acid composition of the frying oil P may include, for example, information indicating the specific type of oil such as palm oil, corn oil, and olive oil, or information indicating the oleic acid content and linoleic acid content of the frying oil P. Note that the information related to the fatty acid composition of the frying oil P does not necessarily have to be output from the store terminal 6, but may be output from the headquarters terminal 7, or from both the store terminal 6 and the headquarters terminal 7, or from an external terminal that manages the frying oil P.

[0175] The oil type discrimination unit 87 determines the type of frying oil P, i.e., whether it is a first-type or second-type oil, based on the information relating to the fatty acid composition of the frying oil P obtained by the data acquisition unit 81C. If the oil type discrimination unit 87 determines that the frying oil P is a first-type oil, it selects formula (5) or formula (6) stored in the storage unit 82C. If the oil type discrimination unit 87 determines that the frying oil P is a second-type oil, it selects formula (7) or formula (8) stored in the storage unit 82C.

[0176] If the oil type discrimination unit 87 determines that the frying oil P is the first type of oil, the deterioration index calculation unit 83C calculates the acid value of the frying oil P based on the measured value of the polar compound amount of the frying oil P obtained by the data acquisition unit 81C and formula (5) or formula (6) stored in the storage unit 82C.

[0177] Furthermore, if the oil type discrimination unit 87 determines that the frying oil P is a second type of oil, the deterioration index calculation unit 83C calculates the acid value of the frying oil P based on the measured value of the polar compound amount of the frying oil P obtained by the data acquisition unit 81C and formula (7) or formula (8) stored in the storage unit 82C.

[0178] In this embodiment, the oil type discrimination unit 87 determines the type of frying oil P (whether it is the first oil type or the second oil type), but it is not limited to this. For example, if the data acquisition unit 81C acquires information about the type of frying oil P itself from the store terminal 6 or the headquarters terminal 7, that is, information such as "first oil type" or "second oil type," the deterioration index calculation unit 83C may select a formula to be used to calculate the acid value of the frying oil P based on the information about the type of frying oil P acquired by the data acquisition unit 81C. In other words, the cloud 8C does not necessarily need to have a function to determine the type (oil type) of frying oil P. The same applies to the cloud 8C according to the fifth to seventh embodiments described later.

[0179] The detection result output unit 84 outputs the acid value of the frying oil P calculated by the deterioration index calculation unit 83C to the store terminal 6 and the headquarters terminal 7, respectively, as a detection result of the degree of deterioration of the frying oil P, similar to the first to third embodiments.

[0180] Figure 17 is a flowchart showing the processing flow executed in Cloud 8C according to the fourth embodiment.

[0181] In Cloud 8C, first, the data acquisition unit 81C acquires the measured amount of polar compounds in the frying oil P output from the PC sensor 41 and information related to the fatty acid composition of the frying oil P output from the store terminal 6 (step S831; data acquisition step).

[0182] Next, the oil type discrimination unit 87 determines whether the frying oil P is a first oil type or a second oil type based on the information relating to the fatty acid composition of the frying oil P obtained in step S831 (step S832; oil type discrimination step).

[0183] If it is determined in step S832 that the frying oil P is a first type of oil (step S832 / first type of oil), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S831 into PCn of formula (5) or PCn of formula (6) stored in the memory unit 82C to calculate the acid value AVn of the frying oil P (step S833; deterioration index calculation step).

[0184] On the other hand, if it is determined in step S832 that the frying oil P is a second type of oil (step S832 / second type of oil), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S831 into PCn of formula (7) or PCn of formula (8) stored in the storage unit 82C to calculate the acid value AVn of the frying oil P (step S834; deterioration index calculation step).

[0185] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P, calculated in step S833 or step S834, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S835; detection result output step), and the processing in the cloud 8C is completed.

[0186] Figure 18 is a graph showing the correlation between the acid value of frying oil P for the first type of oil and the amount of polar compounds contained in the frying oil P (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (5), and (6), respectively. Figure 19 is a graph showing the correlation between the acid value of frying oil P for the second type of oil and the amount of polar compounds contained in the frying oil P (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (7), and (8), respectively.

[0187] In Figure 18, for the case where frying oil P is oil type 1, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graph corresponding to equation (1) is shown with multiple dashes, the correlation graph corresponding to equation (2) is shown with multiple squares, the correlation graph corresponding to equation (5) is shown with multiple asterisks, and the correlation graph corresponding to equation (6) is shown with multiple triangles.

[0188] In Figure 19, for the case where frying oil P is type 2, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graph corresponding to equation (1) is shown with multiple dashes, the correlation graph corresponding to equation (2) is shown with multiple squares, the correlation graph corresponding to equation (7) is shown with multiple asterisks, and the correlation graph corresponding to equation (8) is shown with multiple triangles.

[0189] As shown in Figure 18, the correlation graphs corresponding to equation (5) and equation (6) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (5) and equation (6).

[0190] Similarly, as shown in Figure 19, the correlation graphs corresponding to equation (7) and equation (8) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (7) and equation (8).

[0191] Thus, when the amount of polar compounds in the frying oil P is measured by the PC sensor 41, the cloud 8C can determine the type of frying oil P as either a first-type or second-type oil based on the fatty acid composition of the frying oil P, and calculate the acid value of the frying oil P using the corresponding correlation formula. This allows for a more accurate calculation of the acid value of the frying oil P than when formula (1) or formula (2) is used uniformly without considering the oil type classification based on the fatty acid composition of the frying oil P.

[0192] <Fifth Embodiment> Next, the Cloud 8C according to the fifth embodiment of the present invention will be described with reference to Figures 20 to 23. Note that the functional block diagram showing the functions of the Cloud 8C according to this embodiment is the same as that of the Cloud 8C according to the fourth embodiment; therefore, it is omitted from the illustration, and common components are denoted by the same reference numerals. The same applies to the sixth and seventh embodiments below.

[0193] Figure 20 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the third and fourth types of oil. Specifically, this correlation graph shows the correlation between the acid value of frying oil P and the amount of polar compounds in frying oil P measured using the PC sensor 41.

[0194] Frying oil P is classified into Type 3 and Type 4 based on its iodine value (IV).

[0195] The third oil type is an oil type in which the iodine value of frying oil P is less than a predetermined iodine value threshold (for example, 100) (IV < IVth), and includes, for example, sunflower oil, rapeseed oil, olive oil, peanut oil, and safflower oil.

[0196] On the other hand, the fourth oil type is an oil type in which the iodine value of frying oil P is greater than or equal to a predetermined iodine value threshold (for example, 100) (IV ≧ IVth), and includes, for example, rice bran oil, white sesame oil, cottonseed oil, corn oil, soybean oil, and grape seed oil.

[0197] As shown in FIG. 20, the correlation graph of the acid value of frying oil P with respect to the amount of polar compounds contained in frying oil P is different between the third oil type and the fourth oil type.

[0198] In FIG. 20, the correlation graph of the acid value of frying oil P with respect to the amount of polar compounds contained in frying oil P corresponding to the third oil type is indicated by a plurality of ○ marks, and the correlation graph of the acid value of frying oil P with respect to the amount of polar compounds contained in frying oil P corresponding to the fourth oil type is indicated by a plurality of ▲ marks, respectively.

[0199] When frying oil P is of the third oil type, the correlation formula of the acid value of frying oil P with respect to the amount of polar compounds in frying oil P is the following formula (9) represented by a linear equation including α3 as the linear coefficient of PCn in formula (1) and β3 as the constant β, or the following formula (10) represented by a quadratic equation including γ3 as the quadratic coefficient of PCn in formula (2), δ3 as the linear coefficient of PCn, and ε3 as the constant ε. <000167...​​​​​​​​Furthermore, when the frying oil P is of type 4, the correlation equation between the amount of polar compounds in the frying oil P and the acid value of the frying oil P is expressed as the following linear equation (11), which includes α4 as the linear coefficient α of PCn in equation (1) and β4 as the constant β, or as the following quadratic equation (12), which includes γ4 as the quadratic coefficient γ of PCn in equation (2), δ4 as the linear coefficient δ of PCn, and ε4 as the constant ε. AVn = α⁴ × (PCn) + β⁴···(11) AVn = γ₄ × (PCn) 2 +δ₄×(PCn)+ε₄···(12)

[0201] In this case, the linear coefficient α4 of PCn in equation (11) is smaller than the linear coefficient α3 of PCn in equation (9) (α4 < α3), and the constant β4 in equation (11) is smaller than the constant β3 in equation (9) (β4 < β3). Also, the quadratic coefficient γ4 of PCn in equation (12) is smaller than the quadratic coefficient γ3 of PCn in equation (10) (γ4 < γ3), the linear coefficient δ4 of PCn in equation (12) is smaller than the linear coefficient δ3 of PCn in equation (10) (δ4 < δ3), and the constant ε4 in equation (12) is smaller than the constant ε3 in equation (10) (ε4 < ε3).

[0202] Therefore, in particular, when the amount of polar compounds in frying oil P is measured with the PC sensor 41, there is a difference in the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P between the third type and the fourth type of oil. Using a formula that takes this difference into account (formula (9) or formula (10) for the third type of oil, and formula (11) or formula (12) for the fourth type of oil) allows for a more accurate calculation of the acid value of frying oil P.

[0203] Figure 21 is a flowchart showing the processing flow executed in Cloud 8C according to the fifth embodiment.

[0204] In the cloud 8C according to this embodiment, first, the data acquisition unit 81C acquires the measured value of polar compounds in the frying oil P output from the PC sensor 41 and information related to the iodine value of the frying oil P output from the store terminal 6 (step S841; data acquisition step).

[0205] The information relating to the iodine value of the frying oil P may include, for example, information indicating the specific type of oil or information indicating the iodine value of the frying oil P. Note that the information relating to the iodine value of the frying oil P does not necessarily have to be output from the store terminal 6 to the cloud 8C; for example, it may be output from the headquarters terminal 7 to the cloud 8C.

[0206] Next, the oil type determination unit 87 determines whether the frying oil P is a third-type or fourth-type oil based on the information regarding the iodine value of the frying oil P obtained in step S841 (step S842; oil type determination step).

[0207] If it is determined in step S842 that the frying oil P is a third type of oil (step S842 / third type of oil), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S841 into PCn of formula (9) or PCn of formula (10) stored in the memory unit 82C to calculate the acid value AVn of the frying oil P (step S843; deterioration index calculation step).

[0208] On the other hand, if it is determined in step S842 that the frying oil P is a fourth type of oil (step S842 / fourth type of oil), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S841 into PCn of formula (11) or PCn of formula (12) stored in the storage unit 82C to calculate the acid value AVn of the frying oil P (step S844; deterioration index calculation step).

[0209] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P, calculated in step S843 or step S844, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S845; detection result output step), and the processing in the cloud 8C is completed.

[0210] Figure 22 is a graph showing the correlation between the acid value of frying oil P for the third type of oil and the amount of polar compounds contained in the frying oil P (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (9), and (10), respectively. Figure 23 is a graph showing the correlation between the acid value of frying oil P for the fourth type of oil and the amount of polar compounds contained in the frying oil P (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (11), and (12), respectively.

[0211] In Figure 22, for the case where frying oil P is a third type of oil, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graph corresponding to equation (1) is shown with multiple dashes, the correlation graph corresponding to equation (2) is shown with multiple squares, the correlation graph corresponding to equation (9) is shown with multiple asterisks, and the correlation graph corresponding to equation (10) is shown with multiple triangles.

[0212] In Figure 23, for the case where frying oil P is type 4, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graph corresponding to equation (1) is shown with multiple dashes, the correlation graph corresponding to equation (2) is shown with multiple squares, the correlation graph corresponding to equation (11) is shown with multiple asterisks, and the correlation graph corresponding to equation (12) is shown with multiple triangles.

[0213] As shown in Figure 22, the correlation graphs corresponding to equation (9) and equation (10) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (9) and equation (10).

[0214] Similarly, as shown in Figure 23, the correlation graphs corresponding to equation (11) and equation (12) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (11) and equation (12).

[0215] Thus, when the amount of polar compounds in the frying oil P is measured by the PC sensor 41, the cloud 8C can determine the type of frying oil P as either a third-type or fourth-type oil based on its iodine value, and calculate the acid value of the frying oil P using the corresponding correlation formula. This allows for a more accurate calculation of the acid value of the frying oil P than when formula (1) or formula (2) is used uniformly without considering the oil type classification based on the iodine value of the frying oil P.

[0216] <Sixth Embodiment> Next, a cloud 8C according to the sixth embodiment of the present invention will be described with reference to Figures 24 to 27.

[0217] Figure 24 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 5 and 6. Specifically, this correlation graph shows the correlation between the acid value of frying oil P and the amount of polar compounds in frying oil P measured using the PC sensor 41.

[0218] Frying oil P is classified into Type 5 and Type 6 based on the value of the CDM (Conductmetric Determination Method) test, which is one of the oxidative stability tests for oils and fats (hereinafter simply referred to as "CDM value").

[0219] The fifth type of oil is one in which the CDM value of the frying oil P is equal to or greater than a predetermined CDM threshold (for example, 26 at a measurement temperature of 97.8°C) (CDM value ≥ predetermined CDM threshold), and includes, for example, palm oil, olive oil, peanut oil, refined sesame oil, and safflower oil.

[0220] On the other hand, the sixth oil type is an oil type in which the CDM value of the frying oil P is less than a predetermined CDM threshold (for example, 26 at a measurement temperature of 97.8°C) (CDM value < predetermined CDM threshold), and includes, for example, cottonseed oil, corn oil, soybean oil, and grapeseed oil.

[0221] As shown in Figure 24, the correlation graph between the amount of polar compounds contained in frying oil P and the acid value of frying oil P is different for oil type 5 and oil type 6.

[0222] In Figure 24, the correlation graph between the amount of polar compounds in frying oil P corresponding to the 5th oil type and the acid value of frying oil P is shown with multiple circles, and the correlation graph between the amount of polar compounds in frying oil P corresponding to the 6th oil type and the acid value of frying oil P is shown with multiple triangles.

[0223] When the frying oil P is of type 5, the correlation equation between the acid value of frying oil P and the amount of polar compounds in frying oil P is expressed as the following linear equation (13), which includes α5 as the linear coefficient α of PCn in equation (1) and β5 as the constant β, or as the following quadratic equation (14), which includes γ5 as the quadratic coefficient γ of PCn in equation (2), δ5 as the linear coefficient δ of PCn, and ε5 as the constant ε. AVn = α5 × (PCn) + β5 ... (13) AVn = γ5 × (PCn) 2 +δ5×(PCn)+ε5···(14)

[0224] Furthermore, when the frying oil P is of type 6, the correlation equation between the amount of polar compounds in the frying oil P and the acid value of the frying oil P is expressed as the following linear equation (15), which includes α6 as the linear coefficient α of PCn in equation (1) and β6 as the constant β, or as the following quadratic equation (16), which includes γ6 as the quadratic coefficient γ of PCn in equation (2), δ6 as the linear coefficient δ of PCn, and ε6 as the constant ε. AVn = α6 × (PCn) + β6 ... (15) AVn = γ6 × (PCn) 2 +δ6×(PCn)+ε6···(16)

[0225] In this case, the linear coefficient α6 of PCn in equation (15) is smaller than the linear coefficient α5 of PCn in equation (13) (α6 < α5), and the constant β6 in equation (15) is smaller than the constant β5 in equation (13) (β6 < β5). Also, the quadratic coefficient γ6 of PCn in equation (16) is smaller than the quadratic coefficient γ5 of PCn in equation (14) (γ6 < γ5), the linear coefficient δ6 of PCn in equation (16) is smaller than the linear coefficient δ5 of PCn in equation (14) (δ6 < δ5), and the constant ε6 in equation (16) is smaller than the constant ε5 in equation (14) (ε6 < ε5).

[0226] Therefore, in particular, when the amount of polar compounds in frying oil P is measured with the PC sensor 41, there is a difference in the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P between the 5th oil type and the 6th oil type. Therefore, using a formula that takes this difference into account (formula (13) or formula (14) for the 5th oil type, and formula (15) or formula (16) for the 6th oil type) allows for a more accurate calculation of the acid value of frying oil P.

[0227] Figure 25 is a flowchart showing the processing flow executed in Cloud 8C according to the sixth embodiment.

[0228] In the cloud 8C according to this embodiment, first, the data acquisition unit 81C acquires the measured value of the polar compound amount of the frying oil P output from the PC sensor 41 and information related to the CDM value of the frying oil P output from the store terminal 6 (step S851; data acquisition step).

[0229] The information relating to the CDM value of the frying oil P includes, for example, information indicating the specific type of oil and information indicating the CDM value of the frying oil P. Note that the information relating to the CDM value of the frying oil P does not necessarily have to be output from the store terminal 6 to the cloud 8C; for example, it may be output from the headquarters terminal 7 to the cloud 8C.

[0230] Next, the oil type determination unit 87 determines whether the frying oil P is the fifth oil type or the sixth oil type based on the information relating to the CDM value of the frying oil P obtained in step S851 (step S852; oil type determination step).

[0231] If it is determined in step S852 that the frying oil P is a fifth type of oil (step S852 / fifth type of oil), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S851 into PCn of formula (13) or PCn of formula (14) stored in the memory unit 82C to calculate the acid value AVn of the frying oil P (step S853; deterioration index calculation step).

[0232] On the other hand, if it is determined in step S852 that the frying oil P is a sixth type of oil (step S852 / sixth type of oil), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S851 into PCn of formula (15) or PCn of formula (16) stored in the storage unit 82C to calculate the acid value AVn of the frying oil P (step S854; deterioration index calculation step).

[0233] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P, calculated in step S853 or step S854, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S855; detection result output step), and the processing in the cloud 8C is completed.

[0234] Figure 26 is a graph showing the correlation between the acid value of frying oil P for the fifth type of oil and the amount of polar compounds contained in the frying oil P (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from formulas (1), (2), (13), and (14), respectively. Figure 27 is a graph showing the correlation between the acid value of frying oil P for the sixth type of oil and the amount of polar compounds contained in the frying oil P (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from formulas (1), (2), (15), and (16), respectively.

[0235] In Figure 26, for the case where frying oil P is type 5, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graph corresponding to formula (1) is shown with multiple dashes, the correlation graph corresponding to formula (2) is shown with multiple squares, the correlation graph corresponding to formula (13) is shown with multiple asterisks, and the correlation graph corresponding to formula (14) is shown with multiple triangles.

[0236] In Figure 27, for the case where frying oil P is type 6, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graph corresponding to equation (1) is shown with multiple dashes, the correlation graph corresponding to equation (2) is shown with multiple squares, the correlation graph corresponding to equation (15) is shown with multiple asterisks, and the correlation graph corresponding to equation (16) is shown with multiple triangles.

[0237] As shown in Figure 26, the correlation graphs corresponding to equation (13) and equation (14) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than to the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are closer to the correlation graphs corresponding to equation The correlation graph corresponding to (13) and the correlation graph corresponding to formula (14) are located further away from the correlation graph corresponding to the measured acid value of frying oil P.

[0238] Similarly, as shown in Figure 27, the correlation graphs corresponding to equation (15) and equation (16) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (15) and equation (16).

[0239] Thus, when the amount of polar compounds in the frying oil P is measured by the PC sensor 41, the cloud 8C can determine the type of frying oil P as either a fifth-type or sixth-type oil based on the CDM value of the frying oil P, and calculate the acid value of the frying oil P using the corresponding correlation formula. This allows for a more accurate calculation of the acid value of the frying oil P than when formula (1) or formula (2) is used uniformly without considering the oil type classification based on the CDM value of the frying oil P.

[0240] <Seventh Embodiment> Next, a cloud 8C according to the seventh embodiment of the present invention will be described with reference to Figures 28 to 49.

[0241] The main component of frying oil P (vegetable oil) is triacylglycerol (TG). When heated, a portion of this TG breaks down into diacylglycerol (DG), monoacylglycerol (MG), and free fatty acid (FFA).

[0242] These TG, DG, MG, and FFA are generally collectively referred to as "lipid molecular species" and are found in both new oil (oil that has never been heated and is in a state shortly after production) and heated oil. However, since some TG is broken down during the manufacturing and storage processes, new oil also contains small amounts of DG, MG, and FFA.

[0243] Since the proportions of TG, DG, MG, and FFA in fresh oil and heated oil differ depending on the type of oil, frying oil P is classified into type 7 and type 8 based on the lipid molecular species in frying oil P.

[0244] The seventh oil type is an oil type in which the content of lipid molecular species in frying oil P exceeds a predetermined content threshold. Specifically, it is an oil type in which the MG content of new oil exceeds a predetermined threshold for new oil MG content (for example, MG content of new oil > 0.1g / 100g), an oil type in which the FFA content of new oil exceeds a predetermined threshold for new oil FFA content (for example, FFA content of new oil > 0.07g / 100g), an oil type in which the MG content of heated oil exceeds a predetermined threshold for heated oil MG content (for example, MG content of heated oil > 0.2g / 100g), and an oil type in which the TG content of heated oil exceeds a predetermined threshold for heated oil TG content (for example, TG content of heated oil > 70g / 100g).

[0245] Furthermore, the seventh oil type is one in which the rate of increase in DG content in frying oil P due to heating is less than or equal to a predetermined DG increase rate threshold (first increase rate threshold) (for example, the rate of increase in DG content due to heating ≤ 1.4g / 100g), the rate of increase in FFA content in frying oil P due to heating is less than or equal to a predetermined FFA increase rate threshold (second increase rate threshold) (for example, the rate of increase in FFA content due to heating ≤ 0.1g / 100g), and the rate of decrease in TG content in frying oil P due to heating is less than or equal to a predetermined decrease rate threshold (for example, the rate of decrease in TG content due to heating ≤ 13g / 100g).

[0246] Examples of the seventh type of oil include palm oil, sunflower oil, safflower oil, and rapeseed oil.

[0247] On the other hand, the eighth oil type is an oil type in which the content of lipid molecular species in frying oil P is below a predetermined content threshold. Specifically, this includes oil types in which the MG content of new oil is below a predetermined threshold for new oil MG content (for example, MG content of new oil ≤ 0.1g / 100g), oil types in which the FFA content of new oil is below a predetermined threshold for new oil FFA content (for example, FFA content of new oil ≤ 0.07g / 100g), oil types in which the MG content of heated oil is below a predetermined threshold for heated oil MG content (for example, MG content of heated oil ≤ 0.2g / 100g), and oil types in which the TG content of heated oil is below a predetermined threshold for heated oil TG content (for example, TG content of heated oil ≤ 70g / 100g).

[0248] Furthermore, the eighth oil type is an oil type in which the rate of increase of DG content in frying oil P due to heating is greater than a predetermined DG increase rate threshold (first increase rate threshold) (for example, the rate of increase of DG content due to heating > 1.4g / 100g), an oil type in which the rate of increase of FFA content in frying oil P due to heating is greater than a predetermined FFA increase rate threshold (second increase rate threshold) (for example, the rate of increase of FFA content due to heating > 0.1g / 100g), and an oil type in which the rate of decrease of TG content in frying oil P due to heating is greater than a predetermined decrease rate threshold (for example, the rate of decrease of TG content due to heating > 13g / 100g).

[0249] Examples of the eighth type of oil include cottonseed oil, corn oil, soybean oil, and grapeseed oil.

[0250] Figure 28 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the MG content of new oil. Figure 29 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the FFA content of new oil. Figure 30 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the MG content of heated oil. Figure 31 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the TG content of heated oil.

[0251] Figure 32 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the rate of increase in DG content due to heating. Figure 33 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the rate of increase in FFA content due to heating. Figure 34 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for oil types 7 and 8, classified by the rate of decrease in TG content due to heating.

[0252] The correlation graphs in Figures 28-34 specifically show the correlation between the acid value of frying oil P and the amount of polar compounds in frying oil P, as measured using the PC sensor 41.

[0253] As shown in Figures 28-34, the correlation graphs between the amount of polar compounds contained in frying oil P and the acid value of frying oil P are different for oil type 7 and oil type 8.

[0254] Figures 28-34 show the correlation graphs between the acid value of frying oil P and the amount of polar compounds contained in frying oil P corresponding to the 7th type of oil, indicated by multiple circles, and the correlation graphs between the acid value of frying oil P and the amount of polar compounds contained in frying oil P corresponding to the 8th type of oil, indicated by multiple triangles.

[0255] When the frying oil P is of type 7, the correlation equation between the acid value of frying oil P and the amount of polar compounds in frying oil P is expressed as the following linear equation (17), which includes α7 as the linear coefficient α of PCn in equation (1) and β7 as the constant β, or as the following quadratic equation (18), which includes γ7 as the quadratic coefficient γ of PCn in equation (2), δ7 as the linear coefficient δ of PCn, and ε7 as the constant ε. AVn = α7 × (PCn) + β7 ... (17) AVn = γ7 × (PCn) 2 +δ7×(PCn)+ε7···(18)

[0256] Furthermore, when the frying oil P is of type 8, the correlation equation between the acid value of frying oil P and the amount of polar compounds in frying oil P is expressed as the following linear equation (19), which includes α8 as the linear coefficient α of PCn in equation (1) and β8 as the constant β, or as the following quadratic equation (20), which includes γ8 as the quadratic coefficient γ of PCn in equation (2), δ8 as the linear coefficient δ of PCn and ε8 as the constant ε. AVn = α8 × (PCn) + β8 ... (19) AVn = γ8 × (PCn) 2 +δ8×(PCn)+ε8···(20)

[0257] In this case, the linear coefficient α8 of PCn in equation (19) is smaller than the linear coefficient α7 of PCn in equation (17) (α8 < α7), and the constant β8 in equation (19) is smaller than the constant β7 in equation (17) (β8 < β7). Also, the quadratic coefficient γ8 of PCn in equation (20) is smaller than the quadratic coefficient γ7 of PCn in equation (18) (γ8 < γ7), the linear coefficient δ8 of PCn in equation (20) is smaller than the linear coefficient δ7 of PCn in equation (18) (δ8 < δ7), and the constant ε8 in equation (20) is smaller than the constant ε7 in equation (18) (ε8 < ε7).

[0258] Therefore, in particular, when the amount of polar compounds in frying oil P is measured with the PC sensor 41, there is a difference in the correlation between the amount of polar compounds in frying oil P and the acid value of frying oil P between oil type 7 and oil type 8. Using a formula that takes this difference into account (formula (17) or formula (18) for oil type 7, and formula (19) or formula (20) for oil type 8) allows for a more accurate calculation of the acid value of frying oil P.

[0259] Figure 35 is a flowchart showing the processing flow executed in Cloud 8C according to the seventh embodiment.

[0260] In the cloud 8C according to this embodiment, first, the data acquisition unit 81C acquires the measured value of the amount of polar compounds in the frying oil P output from the PC sensor 41 and information relating to the lipid molecular species of the frying oil P output from the store terminal 6 (step S861; data acquisition step).

[0261] The information relating to the lipid molecular species of the frying oil P includes, for example, information indicating the specific oil type. Note that the information relating to the lipid molecular species of the frying oil P does not necessarily have to be output from the store terminal 6 to the cloud 8C; for example, it may be output from the headquarters terminal 7 to the cloud 8C.

[0262] Next, the oil type discrimination unit 87 determines whether the frying oil P is oil type 7 or oil type 8 based on the information relating to the lipid molecular species of the frying oil P obtained in step S861 (step S862; oil type discrimination step).

[0263] If it is determined in step S862 that the frying oil P is of type 7 (step S862 / type 7), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S861 into PCn of formula (17) or PCn of formula (18) stored in the memory unit 82C to calculate the acid value AVn of the frying oil P (step S863; deterioration index calculation step).

[0264] On the other hand, if it is determined in step S862 that the frying oil P is of type 8 (step S862 / type 8), the deterioration index calculation unit 83C substitutes the measured value of the polar compound amount of the frying oil P obtained in step S861 into PCn of formula (19) or PCn of formula (20) stored in the storage unit 82C to calculate the acid value AVn of the frying oil P (step S864; deterioration index calculation step).

[0265] Then, the detection result output unit 84 outputs the acid value AVn of the frying oil P, calculated in step S863 or step S864, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S865; detection result output step), and the processing in the cloud 8C is completed.

[0266] Figure 36 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P (measured using PC sensor 41) for the seventh type of oil classified by the MG content of new oil. It compares the measured acid value with the acid value calculated from equations (1), (2), (17), and (18). Figure 37 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P (measured using PC sensor 41) for the eighth type of oil classified by the MG content of new oil. It compares the measured acid value with the acid value calculated from equations (1), (2), (19), and (20).

[0267] Figure 38 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the 7th oil type classified by the FFA content of new oil (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from formulas (1), (2), (17), and (18). Figure 39 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the 8th oil type classified by the FFA content of new oil (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from formulas (1), (2), (19), and (20).

[0268] Figure 40 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P (measured using PC sensor 41) for the seventh type of oil classified by the MG content of the heating oil, comparing the measured acid value with the acid value calculated from equations (1), (2), (17), and (18). Figure 41 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P (measured using PC sensor 41) for the eighth type of oil classified by the MG content of the heating oil, comparing the measured acid value with the acid value calculated from equations (1), (2), (19), and (20).

[0269] Figure 42 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P (measured using PC sensor 41) for the seventh type of oil classified by the TG content of the heated oil, comparing the measured acid value with the acid value calculated from equations (1), (2), (17), and (18). Figure 43 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P (measured using PC sensor 41) for the eighth type of oil classified by the TG content of the heated oil, comparing the measured acid value with the acid value calculated from equations (1), (2), (19), and (20).

[0270] Figure 44 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the seventh type of oil classified by the rate of increase in DG content due to heating (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (17), and (18). Figure 45 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the eighth type of oil classified by the rate of increase in DG content due to heating (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (19), and (20).

[0271] Figure 46 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the seventh type of oil classified by the rate of increase in FFA content due to heating (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (17), and (18). Figure 47 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the eighth type of oil classified by the rate of increase in FFA content due to heating (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (19), and (20).

[0272] Figure 48 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the seventh type of oil classified by the rate of decrease in TG content due to heating (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (17), and (18). Figure 49 is a graph showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P for the eighth type of oil classified by the rate of decrease in TG content due to heating (measured using PC sensor 41), comparing the measured acid value with the acid value calculated from equations (1), (2), (19), and (20).

[0273] In Figures 36, 38, 40, 42, 44, 46, and 48, for the case where frying oil P is type 7, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graphs corresponding to equation (1) are shown with multiple dashes, the correlation graphs corresponding to equation (2) are shown with multiple squares, the correlation graphs corresponding to equation (17) are shown with multiple asterisks, and the correlation graphs corresponding to equation (18) are shown with multiple triangles.

[0274] In Figures 37, 39, 41, 43, 45, 47, and 49, for the case where frying oil P is type 8, the correlation graphs corresponding to the measured acid value of frying oil P are shown with multiple circles, the correlation graphs corresponding to equation (1) are shown with multiple dashes, the correlation graphs corresponding to equation (2) are shown with multiple squares, the correlation graphs corresponding to equation (19) are shown with multiple asterisks, and the correlation graphs corresponding to equation (20) are shown with multiple triangles.

[0275] As shown in Figures 36, 38, 40, 42, 44, 46, and 48, the correlation graphs corresponding to equation (17) and equation (18) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (17) and equation (18).

[0276] Similarly, as shown in Figures 37, 39, 41, 43, 45, 47, and 49, the correlation graphs corresponding to equation (19) and equation (20) are located closer to the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (1) and equation (2). In other words, the correlation graphs corresponding to equation (1) and equation (2) are located further away from the correlation graphs corresponding to the measured acid value of frying oil P than the correlation graphs corresponding to equation (19) and equation (20).

[0277] Thus, when the amount of polar compounds in the frying oil P is measured by the PC sensor 41, the cloud 8C classifies the type of frying oil P into type 7 and type 8 based on the lipid molecular species of the frying oil P, and calculates the acid value of the frying oil P using the correlation formula corresponding to the oil type. This allows for a more accurate calculation of the acid value of the frying oil P than when formula (1) or formula (2) is used uniformly without considering the oil type classification based on the lipid molecular species of the frying oil P.

[0278] <Eighth Embodiment> Next, a cloud 9 according to the eighth embodiment of the present invention will be described with reference to Figures 50 to 73.

[0279] In the first to seventh embodiments described above, the correlation between the amount of polar compounds contained in the frying oil P and the acid value of the frying oil P was used as an example for explanation. However, in the eighth embodiment and beyond, other correlations will be used as examples for explanation. Accordingly, in the eighth embodiment and beyond, the cloud codes will be 9 and derived numbers of 9. However, the hardware configuration of the cloud is the same as in the first to seventh embodiments, so the explanation will be omitted.

[0280] Figure 50 is a graph of a linear function showing the correlation between the amount of polar compounds contained in frying oil P and the rate of viscosity increase of frying oil P. Figure 51 is a graph of a linear function showing the correlation between the amount of polar compounds contained in frying oil P and the color of frying oil P. Figure 52 is a graph of a quadratic function showing the correlation between the amount of polar compounds contained in frying oil P and the rate of viscosity increase of frying oil P. Figure 53 is a graph of a quadratic function showing the correlation between the amount of polar compounds contained in frying oil P and the color of frying oil P.

[0281] There is a correlation between the amount of polar compounds contained in frying oil P and the viscosity increase rate of frying oil P, as shown in the graphs in Figures 50 and 52. Specifically, the greater the amount of polar compounds contained in frying oil P, the greater the viscosity increase rate of frying oil P. In other words, the correlation between the amount of polar compounds contained in frying oil P and the viscosity increase rate of frying oil P is expressed by a correlation equation in which the viscosity increase rate of frying oil P is expressed as a polynomial of the amount of polar compounds.

[0282] Furthermore, there is a correlation between the amount of polar compounds contained in frying oil P and the color of frying oil P (actually, this is a numerical value indicating the intensity of the color, and the same applies hereafter), as shown in the graphs in Figures 51 and 53. Specifically, the more polar compounds contained in frying oil P there are, the larger the numerical value related to the color of frying oil P becomes (the darker the color of frying oil P). In other words, the correlation between the amount of polar compounds contained in frying oil P and the color of frying oil P is shown by a correlation equation in which the color of frying oil P is expressed as a polynomial of the amount of polar compounds.

[0283] Figure 54 is a graph of a linear function showing the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P. Figure 55 is a graph of a linear function showing the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P. Figure 56 is a graph of a linear function showing the correlation between the color of frying oil P and the acid value of frying oil P. Figure 57 is a graph of a quadratic function showing the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P. Figure 58 is a graph of a quadratic function showing the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P. Figure 59 is a graph of a quadratic function showing the correlation between the color of frying oil P and the acid value of frying oil P.

[0284] There is a correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P, as shown in the graphs in Figures 54 and 57. Specifically, the higher the acid value of frying oil P, the greater the amount of polar compounds contained in frying oil P. In other words, the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P is expressed by a correlation equation in which the amount of polar compounds in frying oil P is expressed as a polynomial of the acid value.

[0285] Furthermore, there is a correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P, as shown in the graphs in Figures 55 and 58. Specifically, the higher the acid value of frying oil P, the greater the viscosity increase rate of frying oil P. In other words, the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P is expressed by a correlation equation in which the viscosity increase rate of frying oil P is expressed as a polynomial of the acid value.

[0286] Furthermore, there is a correlation between the acid value of frying oil P and the color of frying oil P, as shown in the graphs in Figures 56 and 59. Specifically, the higher the acid value of frying oil P, the higher the numerical value related to the color of frying oil P. In other words, the correlation between the acid value of frying oil P and the color of frying oil P is shown by a correlation equation in which the color of frying oil P is expressed as a polynomial of the acid value.

[0287] Figure 60 is a graph of a linear function showing the correlation between the amount of polar compounds contained in frying oil P and the viscosity increase rate of frying oil P. Figure 61 is a graph of a linear function showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P. Figure 62 is a graph of a linear function showing the correlation between the color of frying oil P and the viscosity increase rate of frying oil P. Figure 63 is a graph of a quadratic function showing the correlation between the amount of polar compounds contained in frying oil P and the viscosity increase rate of frying oil P. Figure 64 is a graph of a quadratic function showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P. Figure 65 is a graph of a quadratic function showing the correlation between the color of frying oil P and the viscosity increase rate of frying oil P.

[0288] There is a correlation between the viscosity increase rate of frying oil P and the amount of polar compounds contained in frying oil P, as shown in the graphs in Figures 60 and 63. Specifically, the greater the viscosity increase rate of frying oil P, the greater the amount of polar compounds contained in frying oil P. In other words, the correlation between the viscosity increase rate of frying oil P and the amount of polar compounds contained in frying oil P is expressed by a correlation equation in which the amount of polar compounds in frying oil P is expressed in terms of the viscosity increase rate.

[0289] Furthermore, there is a correlation between the viscosity increase rate of the frying oil P and the acid value of the frying oil P, as shown in the graphs in Figures 61 and 64. Specifically, the greater the viscosity increase rate of the frying oil P, the greater the acid value of the frying oil P. In other words, the correlation between the viscosity increase rate of the frying oil P and the acid value of the frying oil P is expressed by a correlation equation in which the acid value of the frying oil is expressed as a polynomial of the viscosity increase rate.

[0290] Furthermore, there is a correlation between the viscosity increase rate of the frying oil P and the color of the frying oil P, as shown in the graphs in Figures 62 and 65. Specifically, the higher the viscosity increase rate of the frying oil P, the higher the numerical value related to the color of the frying oil P. In other words, the correlation between the viscosity increase rate of the frying oil P and the color of the frying oil P is expressed by a correlation equation in which the color of the frying oil P is expressed as a polynomial of the viscosity increase rate.

[0291] Figure 66 is a graph of a linear function showing the correlation between the amount of polar compounds contained in frying oil P and the color of frying oil P. Figure 67 is a graph of a linear function showing the correlation between the acid value of frying oil P and the color of frying oil P. Figure 68 is a graph of a linear function showing the correlation between the viscosity increase rate of frying oil P and the color of frying oil P. Figure 69 is a graph of a quadratic function showing the correlation between the amount of polar compounds contained in frying oil P and the color of frying oil P. Figure 70 is a graph of a quadratic function showing the correlation between the acid value of frying oil P and the color of frying oil P. Figure 71 is a graph of a quadratic function showing the correlation between the viscosity increase rate of frying oil P and the color of frying oil P.

[0292] There is a correlation between the color of the frying oil P and the amount of polar compounds it contains, as shown in the graphs in Figures 66 and 69. Specifically, the larger the value related to the color of the frying oil P, the greater the amount of polar compounds it contains. In other words, the correlation between the color of the frying oil P and the amount of polar compounds it contains is expressed by a correlation equation in which the amount of polar compounds in the frying oil P is expressed as a polynomial of color.

[0293] Furthermore, there is a correlation between the color of the frying oil P and the acid value of the frying oil P, as shown in the graphs in Figures 67 and 70. Specifically, the higher the numerical value related to the color of the frying oil P, the higher the acid value of the frying oil P. In other words, the correlation between the color of the frying oil P and the acid value of the frying oil P is expressed by a correlation equation in which the acid value of the frying oil P is expressed as a polynomial of color.

[0294] Furthermore, there is a correlation between the color of the frying oil P and the viscosity increase rate of the frying oil P, as shown in the graphs in Figures 68 and 71. Specifically, the larger the value related to the color of the frying oil P, the greater the viscosity increase rate of the frying oil P. In other words, the correlation between the color of the frying oil P and the viscosity increase rate of the frying oil P is shown by a correlation equation in which the viscosity increase rate of the frying oil P is expressed as a polynomial of color.

[0295] Here, the acid value, polar compound content, color, and viscosity increase rate of the frying oil P each correspond to the first degradation indicators defined based on the substances produced by heating the frying oil P, which is a type of oil. The acid value of the frying oil P is the value corresponding to the free fatty acids produced by heating the frying oil P. The polar compound content of the frying oil P is the proportion of polar compounds produced by heating the frying oil P that make up the oil. The color (intensity of color) of the frying oil P is a value that changes due to oxides, polymers, and substances leached from the food to be fried Q and their reactions produced by heating the frying oil P. The viscosity of the frying oil P changes due to the progress of polymerization reactions caused by heating the frying oil P and substances leached from the food to be fried Q, and the viscosity increase rate is the percentage of the increase relative to the viscosity of new oil.

[0296] Let this first degradation index be Di1, and let the second degradation index, which is a degradation index other than the first degradation index, be Di2. If the heating time of the frying oil P is n, then the correlation equation between the second degradation index of the frying oil P and the first degradation index of the frying oil P at any heating time n is given by a linear equation (31) or a quadratic equation (32). Di2n = α × (Di1n) + β···(31) Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε···(32)

[0297] Formula (31) is the correlation formula corresponding to the correlation graphs shown in Figures 50, 51, 54-56, 60-62, and 66-68, respectively, and formula (32) is the correlation formula corresponding to the correlation graphs shown in Figures 52, 53, 57-59, 63-65, and 69-71, respectively. By substituting the measured value related to the first deterioration index at an arbitrary heating time n of the frying oil P into Di1n for each of these formulas (31) and (32), the second deterioration index Di2n at an arbitrary heating time n of the frying oil P can be calculated.

[0298] In the following explanation, various measuring devices corresponding to the acid value, polar compound content, color, and viscosity increase rate of the frying oil P, which are the first degradation indicators, will be collectively referred to as "measuring device 4". Therefore, for example, if the first degradation indicator is the polar compound content, measuring device 4 will be the PC sensor 42, and if the first degradation indicator is color, measuring device 4 will be the camera 42.

[0299] Next, the functional configuration of the cloud 9 according to the eighth embodiment will be described with reference to Figure 72.

[0300] Figure 72 is a functional block diagram showing the functions of the cloud 9 according to the eighth embodiment.

[0301] As shown in Figure 72, the cloud 9 includes a data acquisition unit 91, a storage unit 92, a degradation index calculation unit 93, and a detection result output unit 94.

[0302] The data acquisition unit 91 acquires the measured value of the first deterioration index of the frying oil P output from the measuring device 4.

[0303] The memory unit 92 stores the correlation formula between the first deterioration index of the frying oil P and the second deterioration index, specifically formula (31) or formula (32). The memory unit 92 may store both formula (31) and formula (32), or it may store only one of formula (31) or formula (32).

[0304] The deterioration index calculation unit 93 calculates the second deterioration index of the frying oil P based on the measured value of the first deterioration index of the frying oil P acquired by the data acquisition unit 91 and the correlation formula of the second deterioration index of the frying oil P with respect to the first deterioration index of the frying oil P read from the storage unit 92.

[0305] Specifically, the deterioration index calculation unit 93 substitutes the measured value of the first deterioration index of the frying oil P acquired by the data acquisition unit 91 into Di1n of formula (31) or formula (32) read from the storage unit 92, and calculates the second deterioration index Di2n of the frying oil P.

[0306] If both formula (31) and formula (32) are stored in the memory unit 92, the deterioration index calculation unit 93 selects one of formulas (31) and (32), and substitutes the measured value of the first deterioration index of the frying oil P into Di1n of the selected formula to calculate the second deterioration index of the frying oil P.

[0307] The detection result output unit 94 outputs the second deterioration index of the frying oil P, calculated by the deterioration index calculation unit 93, to the store terminal 6 and the headquarters terminal 7 as the detection result of the degree of deterioration of the frying oil P. The detection result output unit 94 is not necessarily required to output the detection result of the degree of deterioration of the frying oil P to both the store terminal 6 and the headquarters terminal 7; it may output the detection result of the degree of deterioration of the frying oil P to only one of the two terminals.

[0308] Next, we will explain the flow of processing performed within Cloud 9, referring to Figure 73.

[0309] Figure 73 is a flowchart showing the processing flow executed in the cloud 9 according to the eighth embodiment.

[0310] As shown in Figure 73, Cloud 9 first acquires the measured value of the first deterioration index of the frying oil P measured by the measuring device 4 in the measurement step, via the data acquisition unit 91 (step S901; data acquisition step).

[0311] Next, the deterioration index calculation unit 93 substitutes the measured value of the first deterioration index of the frying oil P obtained in step S901 into the correlation formula for the second deterioration index of the frying oil P with respect to the first deterioration index of the frying oil P, i.e., Di1n of formula (31) or formula (32), which is stored in the storage unit 92, to calculate the second deterioration index Di2n of the frying oil P (step S902; deterioration index calculation step).

[0312] Then, the detection result output unit 94 outputs the second deterioration index Di2n of the frying oil P, calculated in step S902, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S903; detection result output step), and the processing in the cloud 9 is completed.

[0313] Thus, Cloud 9 can convert from the first deterioration index of frying oil P to a second deterioration index other than the first deterioration index by using a correlation formula between the first deterioration index and the second deterioration index of frying oil P. As a result, store employees can obtain a detection result of the degree of deterioration of frying oil P in a second deterioration index different from the first deterioration index simply by measuring the first deterioration index of frying oil P with the measuring device 4. This allows store employees to easily and accurately detect various deterioration indicators that show the degree of deterioration of frying oil P according to the conditions in the kitchen 1 and the requirements of headquarters.

[0314] <Ninth Embodiment> Next, a cloud 9A according to the ninth embodiment of the present invention will be described with reference to Figures 74 to 81.

[0315] In this embodiment, the coefficients and constants included in the correlation equation between the first deterioration index of the frying oil P and the second deterioration index of the frying oil P are set to values ​​corresponding to the weight W per unit heating time of the food item Q (ingredients) fried using the frying oil P (hereinafter referred to as "amount of frying per unit time W").

[0316] Figure 74 is a linear function graph showing the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P, taking into account the frying rate W per hour. Figure 75 is a quadratic function graph showing the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P, taking into account the frying rate W per hour. Figure 76 is a linear function graph showing the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P, taking into account the frying rate W per hour. Figure 77 is a quadratic function graph showing the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P, taking into account the frying rate W per hour. Figure 78 is a linear function graph showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P, taking into account the frying rate W per hour. Figure 79 is a quadratic function graph showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P, taking into account the frying rate W per hour.

[0317] As shown in Figures 74 and 75, the graphs showing the correlation between the acid value of frying oil P and the amount of polar compounds contained in frying oil P have different slopes depending on the amount W of fried food Q per hour that is fried using frying oil P.

[0318] Similarly, as shown in Figures 76 and 77, the graphs showing the correlation between the acid value of the frying oil P and the viscosity increase rate of the frying oil P have different slopes depending on the amount W of food Q fried per hour using the frying oil P.

[0319] Similarly, as shown in Figures 78 and 79, the graphs showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P have different slopes depending on the amount W of food Q fried per hour using frying oil P.

[0320] Therefore, the linear coefficient α of Di1n in equation (31), and the quadratic coefficient γ and linear coefficient δ of Di1n in equation (32), are set to values ​​corresponding to the amount W of fried food Q per unit time, which is fried using frying oil P.

[0321] Furthermore, since the amount of fried food Q fried per unit time W at a store can be calculated based on the daily sales of the store using frying oil P, the linear coefficient α of Di1n in formula (31), and the quadratic coefficient γ and linear coefficient δ of Di1n in formula (32) can be values ​​corresponding to the daily sales of the store using frying oil P. The daily sales of a store can be calculated, for example, by taking the average daily value from the total sales of the store over the past year, or by taking the average daily value from the total sales of the store over a predetermined period (such as each season). In addition, it is preferable that the daily sales of the store be the amount extracted from the sales of fried food cooked using frying oil P.

[0322] Figures 74-79 classify the amount of fried food per hour W in stores into three ranges: "high," "medium," and "low." The correlation graph for the "low" case (W < 2000), where the amount of fried food per hour W is less than 2,000g, is shown with multiple circles; the correlation graph for the "medium" case (2000 ≤ W < 12000), where the amount of fried food per unit time W is 2,000g or more but less than 12,000g, is shown with multiple triangles; and the correlation graph for the "high" case (W ≥ 12000), where the amount of fried food per hour W is 12,000g or more, is shown with multiple dashes.

[0323] In Figures 74-77, the slope of the correlation graph when the amount of fried food per hour W is "high" is the smallest of the three correlation graphs. The slope of the correlation graph when the amount of fried food per hour W is "medium" is greater than the slope of the correlation graph when the amount of fried food per hour W is "high". The slope of the correlation graph when the amount of fried food per hour W is "low" is even greater than the slope of the correlation graph when the amount of fried food per hour W is "medium", making it the largest of the three correlation graphs.

[0324] Therefore, in the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P, and the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P (i.e., the horizontal axis of the correlation graph is the acid value of frying oil P), the linear coefficient α of Di1n in equation (31), and the quadratic coefficient γ and linear coefficient δ of Di1n in equation (32), are set to be larger as the amount of frying W per hour decreases.

[0325] On the other hand, in Figures 78 and 79, the slope of the correlation graph when the amount of fried food per hour W is "low" is the smallest of the three correlation graphs. The slope of the correlation graph when the amount of fried food per hour W is "medium" is greater than the slope of the correlation graph when the amount of fried food per hour W is "low". The slope of the correlation graph when the amount of fried food per hour W is "high" is even greater than the slope of the correlation graph when the amount of fried food per hour W is "medium", making it the largest of the three correlation graphs.

[0326] Therefore, in the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P, the linear coefficient α of Di1n in equation (31), and the quadratic coefficient γ and linear coefficient δ of Di1n in equation (32), are set to be larger as the amount of frying W per unit time increases.

[0327] Since the amount of polar compounds and viscosity increase rate in the frying oil P increase with the heating time in the fryer 2 (total heating time of the fryer 2, including the time spent frying the food Q and the time spent emptying the fryer), and the acid value of the frying oil P increases with the amount of food Q fried, the correlation between the amount of polar compounds or viscosity increase rate in the frying oil P and the acid value of the frying oil P can be made more accurate by considering both the heating time in the fryer 2 and the amount of food Q fried.

[0328] Note that the "unit time" does not necessarily have to be one hour; it can be any time set arbitrarily. Also, the classification of the amount of fried food W per unit time in a store does not necessarily have to be based on the thresholds of "2,000g" and "12,000g" used in the correlation shown in Figures 74-79; any value can be used as the threshold for each store.

[0329] Figure 80 is a functional block diagram showing the functions of Cloud 9A according to the eighth embodiment. Figure 81 is a flowchart showing the processing flow executed by Cloud 9A according to the eighth embodiment.

[0330] As shown in Figure 80, the cloud 9A includes a data acquisition unit 91A, a frying amount determination unit 95, a storage unit 92A, a deterioration index calculation unit 93A, and a detection result output unit 94.

[0331] As shown in Figure 81, in the cloud 9A, first, the data acquisition unit 91A acquires the frying information output from the store terminal 6 (step S911). This "frying information" includes the heating time in the fryer 2 and the weight of the fried food Q to be fried in the fryer 2 (i.e., the amount of fried food Q).

[0332] Next, the frying amount determination unit 95 calculates the frying amount W per unit time (1 hour in this embodiment) based on the frying cooking information acquired in step S911, determines the classification of the frying amount W per unit time in the store (step S912), and sets the linear coefficient α of Di1n in formula (31) or the quadratic coefficient γ of Di1n and the linear coefficient δ of PCn in formula (32), stored in the storage unit 92A, to values ​​corresponding to the frying amount W per unit time (step S912). As a result, formula (31) or formula (32) stored in the storage unit 92A is updated.

[0333] Next, the data acquisition unit 91A acquires the measured value of the first deterioration index of the frying oil P output from the measuring device 4 (step S913).

[0334] Next, the deterioration index calculation unit 93A substitutes the measured value of the first deterioration index of the frying oil P obtained in step S913 into Di1 of formula (31) or formula (32) updated in step S912, and calculates the second deterioration index Di2n of the frying oil P (step S914).

[0335] Then, the detection result output unit 94 outputs the second deterioration index Di2n of the frying oil P, calculated in step S914, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S915), and the processing in the cloud 9A is completed.

[0336] In this embodiment, Cloud 9A calculates the second deterioration index of the frying oil P using formula (31) or formula (32) corresponding to the amount of frying W per unit time at the store. Therefore, it can calculate the second deterioration index of the frying oil P with higher accuracy than when using a predetermined formula (31) or formula (32).

[0337] <Tenth Embodiment> Next, a cloud 9B according to the tenth embodiment of the present invention will be described with reference to Figures 82 to 93.

[0338] Figure 82 is a linear function graph showing the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P, depending on whether or not a preheating time is taken. Figure 83 is a linear function graph showing the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P, depending on whether or not a preheating time is taken. Figure 84 is a linear function graph showing the correlation between the color of frying oil P and the acid value of frying oil P, depending on whether or not a preheating time is taken. Figure 85 is a quadratic function graph showing the correlation between the amount of polar compounds contained in frying oil P and the acid value of frying oil P, depending on whether or not a preheating time is taken. Figure 86 is a quadratic function graph showing the correlation between the viscosity increase rate of frying oil P and the acid value of frying oil P, depending on whether or not a preheating time is taken. Figure 87 is a quadratic function graph showing the correlation between the color of frying oil P and the acid value of frying oil P, depending on whether or not a preheating time is taken.

[0339] Figure 88 is a graph of a linear function showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P with and without preheating time. Figure 89 is a graph of a quadratic function showing the correlation between the acid value of frying oil P and the viscosity increase rate of frying oil P with and without preheating time.

[0340] Figure 90 is a graph of a linear function showing the correlation between the acid value of frying oil P and the color of frying oil P, depending on whether or not a preheating time is performed. Figure 91 is a graph of a quadratic function showing the correlation between the acid value of frying oil P and the color of frying oil P, depending on whether or not a preheating time is performed.

[0341] As shown in Figures 82-91, the graph showing the correlation between the second deterioration index of frying oil P and the first deterioration index of frying oil P differs depending on whether or not the frying oil P has been preheated. Preheating means heating only the frying oil P without frying the ingredients Q, that is, without adding the ingredients Q to the frying oil P.

[0342] In Figures 82-91, the correlation graph for the case where the frying oil P is not preheated is shown with multiple circles, and the correlation graph for the case where the frying oil P is preheated is shown with multiple triangles.

[0343] In the correlation graphs shown in Figures 82-84, when frying oil P is subjected to air heating, the rate of increase of the second deterioration indicator (any of the amount of polar compounds, viscosity increase rate, or color) tends to be larger than the rate of increase of the acid value, which is the first deterioration indicator, and the second deterioration indicator rises by EH1 minute. Similarly, in the correlation graphs shown in Figures 85-87, when frying oil P is subjected to air heating, the rate of increase of the second deterioration indicator (any of the amount of polar compounds, viscosity increase rate, or color) tends to be larger than the rate of increase of the acid value, which is the first deterioration indicator, and the second deterioration indicator rises by EH2 minutes.

[0344] On the other hand, the correlation graphs shown in Figures 88 and 90 show that when frying oil P is subjected to air heating, the rate of increase in the acid value, which is the second deterioration indicator, tends to be smaller than the rate of increase in the first deterioration indicator (viscosity increase rate or color), and the acid value decreases by EH1. Similarly, the correlation graphs shown in Figures 89 and 91 show that when frying oil P is subjected to air heating, the rate of increase in the acid value, which is the second deterioration indicator, tends to be smaller than the rate of increase in the first deterioration indicator (viscosity increase rate or color), and the acid value decreases by EH2.

[0345] Here, "EH1" and "EH2" correspond to air heating variables set considering the air heating of the frying oil P, respectively. When the acid value is adopted as the first deterioration indicator of the frying oil P, they become positive variables (EH1>0, EH2>0) (see Figures 82-87), and when the acid value is adopted as the second deterioration indicator of the frying oil P, they become negative variables (EH1<0, EH2<0) (see Figures 88-91). The absolute value of the air heating variable becomes larger as the air heating time increases.

[0346] Thus, when the frying oil P is subjected to air heating, the correlation equation between the first deterioration index of the frying oil P and the second deterioration index of the frying oil P is expressed as the following equation (33), which is a linear equation obtained by adding the term of the air heating variable EH1 to equation (31), or as the following equation (34), which is a quadratic equation obtained by adding the term of the air heating variable EH2 to equation (32). Di2n=α×(Di1n)+β+EH1···(33) Di2n = γ × (Di1n) 2 +δ×(Di1n)+ε+EH2···(34)

[0347] Figure 92 is a functional block diagram showing the functions of Cloud 9B according to the 10th embodiment. Figure 93 is a flowchart showing the processing flow executed by Cloud 9B according to the 10th embodiment.

[0348] As shown in Figure 92, the cloud 9B according to this embodiment includes a data acquisition unit 91B, an air heating determination unit 96, a storage unit 92B, a deterioration index calculation unit 93B, and a detection result output unit 94.

[0349] As shown in Figure 93, in the cloud 9B, first, the data acquisition unit 91B acquires the measured value of the first deterioration index of the frying oil P output from the measuring device 4 and the surface image of the frying oil P output from the camera 42 (step S921).

[0350] Next, the empty heating determination unit 96 determines whether or not empty heating has been performed on the frying oil P based on the surface image of the frying oil P acquired in step S921 (step S922). As described above, empty heating is the heating of the frying oil P when no ingredients Q have been added, so the empty heating determination unit 96 determines that the time when the ingredients Q are not included in the surface image of the frying oil P captured by the camera 42 is the time when empty heating has been performed.

[0351] Furthermore, the method for determining whether or not preheating has occurred does not necessarily have to be based on the presence or absence of fried food Q in the surface image of the frying oil P captured by the camera 42. For example, a temperature sensor may be attached to the fryer 2, and preheating may be determined when the temperature measured by the temperature sensor falls below a predetermined temperature (preheating temperature). Alternatively, for example, a weight sensor may be attached to the fryer 2, and preheating may be determined based on the increase or decrease in weight measured by the weight sensor.

[0352] Furthermore, for example, stores record information such as the type and quantity of ingredients Q to be fried, and the time of frying, and it is possible to determine whether or not preheating has been performed based on this record. Alternatively, for example, it is possible to determine whether or not preheating has been performed by calculating the preheating time based on the store's pre-registered daily frying schedule.

[0353] Furthermore, since frying is initiated when the operation switch 22A of the fryer 2 is operated, it is possible to determine whether or not preheating has been performed based on the operation of the operation switch 22A. Alternatively, it is possible to determine whether or not preheating has been performed based on the amount of electricity or gas consumed by the fryer 2.

[0354] If it is determined in step S922 that the frying oil P has been preheated (step S922 / YES), the preheating determination unit 96 then sets the preheating variable EH1 of formula (33) or the preheating variable EH2 of formula (34) stored in the storage unit 92B (step S923).

[0355] Next, if it is determined in step S922 that the frying oil P has not been preheated (step S922 / NO), the deterioration index calculation unit 93B substitutes the measured value of the first deterioration index of the frying oil P obtained in step S921 into Di1n of formula (31) or Di1n of formula (32) stored in the storage unit 92B to calculate the second deterioration index Di2n of the frying oil P (step S925).

[0356] Then, the detection result output unit 94 outputs the second deterioration index Di2n of the frying oil P, calculated in step S924 or step S925, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S926), and the processing in the cloud 9B is completed.

[0357] In this embodiment, the preheating determination unit 96 determines whether or not preheating has been performed on the frying oil P. However, it is not limited to this. For example, if the data acquisition unit 91B acquires information regarding the presence or absence of preheating from a store terminal 6 or a headquarters terminal 7, the preheating determination unit 96 may set the preheating variable EH1 of formula (33) or the preheating variable EH2 of formula (34) stored in the storage unit 92B based on the information acquired by the data acquisition unit 91B (information indicating that preheating has been performed). In other words, the cloud 9B does not necessarily need to have a function to determine whether or not preheating has been performed on the frying oil P.

[0358] In this embodiment, Cloud 9B calculates the second deterioration index of the frying oil P by using formula (31) or formula (32) and formula (33) or formula (34) depending on whether or not the frying oil P has been preheated. This allows for a more accurate calculation of the second deterioration index of the frying oil P than when formula (31) or formula (32) is used uniformly without considering whether or not the frying oil P has been preheated.

[0359] <Embodiment 11> Next, the Cloud 9C according to the 11th embodiment of the present invention will be described with reference to Figures 94 to 122.

[0360] Figure 94 is a graph showing the correlation between the amount of polar compounds contained in frying oil P for the first and second types of oil and the viscosity increase rate of frying oil P. Figure 95 is a graph showing the correlation between the amount of polar compounds contained in frying oil P for the first and second types of oil and the color of frying oil P. Figure 96 is a graph showing the correlation between the acid value of frying oil P for the first and second types of oil and the amount of polar compounds contained in frying oil P. Figure 97 is a graph showing the correlation between the acid value of frying oil P for the first and second types of oil and the viscosity increase rate of frying oil P.

[0361] Figure 98 is a graph showing the correlation between the amount of polar compounds contained in the frying oil P and the viscosity increase rate of the frying oil P for the first and second types of oil. Figure 99 is a graph showing the correlation between the acid value of the frying oil P and the viscosity increase rate of the frying oil P for the first and second types of oil. Figure 100 is a graph showing the correlation between the color of the frying oil P and the viscosity increase rate of the frying oil P for the first and second types of oil. Figure 101 is a graph showing the correlation between the amount of polar compounds contained in the frying oil P and the color of the frying oil P for the first and second types of oil. Figure 102 is a graph showing the correlation between the viscosity increase rate of the frying oil P and the color of the frying oil P for the first and second types of oil.

[0362] In addition, the correlation graphs shown in Figures 94 to 102 each show that the first degradation index, corresponding to the horizontal axis, is a value measured using the measuring device 4.

[0363] As described above in the fourth embodiment, the frying oil P is classified into a first type and a second type based on the fatty acid composition that makes up the frying oil P.

[0364] As shown in Figures 94-102, the correlation graphs of the second deterioration index of frying oil P and the first deterioration index of frying oil P are different for the first and second types of oil. In Figures 94-102, the correlation graphs of the second deterioration index of frying oil P and the first deterioration index of frying oil P corresponding to the first type of oil are shown with multiple circles, and the correlation graphs of the second deterioration index of frying oil P and the first deterioration index of frying oil P corresponding to the second type of oil are shown with multiple triangles.

[0365] When the frying oil P is of type 1, the correlation equation between the acid value of frying oil P and the amount of polar compounds in frying oil P is expressed as the following linear equation (35), which includes α1 as the linear coefficient α of Di1n in equation (31) and β1 as the constant β, or as the following quadratic equation (36), which includes γ1 as the quadratic coefficient γ of Di1n in equation (32), δ1 as the linear coefficient δ of Di1n, and ε1 as the constant ε. Di2n=α1×(Di1n)+β1···(35) Di2n = γ1 × (Di1n) 2 +δ1×(PCn)+ε1···(36)

[0366] Furthermore, when the frying oil P is of type 2, the correlation equation between the second deterioration index of frying oil P and the first deterioration index of frying oil P is the following equation (37), which is a linear expression in which α2 is the linear coefficient α of Di1n in equation (31) and β2 is the constant β, or the following equation (38), which is a quadratic expression in which γ2 is the quadratic coefficient γ of Di1n in equation (32), δ2 is the linear coefficient δ of Di1n, and ε2 is the constant ε. Di2n=α2×(Di1n)+β2···(37) Di2n = γ2 × (Di1n) 2 +δ²×(Di1n)+ε²···(38)

[0367] In this case, in Figures 94, 95, 99, and 100, the linear coefficient α2 of Di1n in equation (37) is smaller than the linear coefficient α1 of Di1n in equation (35) (α2 < α1), and the constant β2 in equation (37) is smaller than the constant β1 in equation (35) (β2 < β1). Also, the quadratic coefficient γ2 of Di1n in equation (38) is smaller than the quadratic coefficient γ1 of Di1n in equation (36) (γ2 < γ1), the linear coefficient δ2 of Di1n in equation (38) is smaller than the linear coefficient δ1 of Di1n in equation (36) (δ2 < δ1), and the constant ε2 in equation (38) is smaller than the constant ε1 in equation (36) (ε2 < ε1).

[0368] On the other hand, in Figures 96-98, 101, and 102, the linear coefficient α2 of Di1n in equation (37) is greater than the linear coefficient α1 of Di1n in equation (35) (α2>α1), and the constant β2 in equation (37) is greater than the constant β1 in equation (35) (β2>β1). Also, the quadratic coefficient γ2 of Di1n in equation (38) is greater than the quadratic coefficient γ1 of Di1n in equation (36) (γ2>γ1), the linear coefficient δ2 of Di1n in equation (38) is greater than the linear coefficient δ1 of Di1n in equation (36) (δ2>δ1), and the constant ε2 in equation (38) is greater than the constant ε1 in equation (36) (ε2>ε1).

[0369] Therefore, in particular, when the first deterioration index of frying oil P is measured with the measuring device 4, there is a difference in the correlation between the first and second types of oil and the second deterioration index of frying oil P. Therefore, using a formula that takes this difference into account (formula (35) or formula (36) for the first type of oil, and formula (37) or formula (38) for the second type of oil) allows for a more accurate calculation of the second deterioration index of frying oil P.

[0370] Figure 103 is a functional block diagram showing the functions of Cloud 9C according to the 11th embodiment. Figure 104 is a flowchart showing the processing flow executed by Cloud 9C according to the 11th embodiment.

[0371] As shown in Figure 103, the cloud 9C according to this embodiment includes a data acquisition unit 91C, an oil type discrimination unit 97, a storage unit 92C, a deterioration index calculation unit 93C, and a detection result output unit 94.

[0372] As shown in Figure 104, in the cloud 9C, first, the data acquisition unit 91C acquires the measured value of the first deterioration index of the frying oil P output from the measuring device 4 and information related to the fatty acid composition of the frying oil P output from the store terminal 6 (step S931).

[0373] Next, the oil type discrimination unit 97 determines whether the frying oil P is a first oil type or a second oil type based on the information regarding the fatty acid composition of the frying oil P obtained in step S931 (step S932).

[0374] If it is determined in step S932 that the frying oil P is a first type of oil (step S932 / first type of oil), the deterioration index calculation unit 93C substitutes the measured value of the first deterioration index of the frying oil P obtained in step S931 into Di1n of formula (35) or Di1n of formula (36) stored in the storage unit 92C to calculate the second deterioration index Di2n of the frying oil P (step S933).

[0375] On the other hand, if it is determined in step S932 that the frying oil P is a second type of oil (step S932 / second type of oil), the deterioration index calculation unit 93C substitutes the measured value of the first deterioration index of the frying oil P obtained in step S931 into Di1n of formula (37) or Di1n of formula (38) stored in the storage unit 92C to calculate the second deterioration index Di2n of the frying oil P (step S934).

[0376] Then, the detection result output unit 94 outputs the second deterioration index Di2n of the frying oil P, calculated in step S933 or step S934, to the store terminal 6 and the headquarters terminal 7 respectively as the detection result of the degree of deterioration of the frying oil P (step S935), and the processing in the cloud 9C is completed.

[0377] Figure 105 is a graph showing the correlation between the viscosity increase rate of frying oil P and the amount of polar compounds contained in frying oil P for the first type of oil (measured using measuring device 4), comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (35), and (36), respectively. Figure 106 is a graph showing the correlation between the color of frying oil P and the amount of polar compounds contained in frying oil P for the first type of oil (measured using measuring device 4), comparing the measured color with the color calculated from equations (31), (32), (35), and (36), respectively.

[0378] Figure 107 is a graph showing the correlation between the amount of polar compounds contained in frying oil P for the first type of oil and the acid value (measured using measuring device 4), comparing the measured value of the amount of polar compounds with the value calculated from formulas (31), (32), (35), and (36), respectively. Figure 108 is a graph showing the correlation between the viscosity increase rate of frying oil P for the first type of oil and the acid value (measured using measuring device 4), comparing the measured value of the viscosity increase rate with the value calculated from formulas (31), (32), (35), and (36), respectively.

[0379] Figure 109 is a graph showing the correlation between the viscosity increase rate of the first type of frying oil P (measured using measuring device 4) and the amount of polar compounds contained in the frying oil P, comparing the measured value of the amount of polar compounds with the value calculated from formulas (31), (32), (35), and (36), respectively. Figure 110 is a graph showing the correlation between the acid value of the frying oil P and the viscosity increase rate of the first type of frying oil P (measured using measuring device 4), comparing the measured value of the acid value with the value calculated from formulas (31), (32), (35), and (36), respectively. Figure 111 is a graph showing the correlation between the viscosity increase rate of the first type of frying oil P (measured using measuring device 4) and the color of the frying oil P, comparing the measured color with the color calculated from formulas (31), (32), (35), and (36), respectively.

[0380] Figure 112 is a graph showing the correlation between the amount of polar compounds contained in the frying oil P for the first type of oil (measured using measuring device 4) and the measured amount of polar compounds, comparing it with the amount calculated from formulas (31), (32), (35), and (36), respectively. Figure 113 is a graph showing the correlation between the viscosity increase rate of the frying oil P for the first type of oil (measured using measuring device 4) and the measured viscosity increase rate, comparing it with the viscosity increase rate calculated from formulas (31), (32), (35), and (36), respectively.

[0381] Figure 114 is a graph showing the correlation between the viscosity increase rate of frying oil P for the second type of oil and the amount of polar compounds contained in the frying oil P (measured using measuring device 4), comparing the measured viscosity increase rate with the viscosity increase rate calculated from equations (31), (32), (37), and (38), respectively. Figure 115 is a graph showing the correlation between the color of frying oil P for the second type of oil and the amount of polar compounds contained in the frying oil P (measured using measuring device 4), comparing the measured color with the color calculated from equations (31), (32), (37), and (38), respectively.

[0382] Figure 116 is a graph showing the correlation between the amount of polar compounds contained in the second type of frying oil P and the acid value (measured using measuring device 4), comparing the measured value of the amount of polar compounds with the value calculated from formulas (31), (32), (37), and (38), respectively. Figure 117 is a graph showing the correlation between the viscosity increase rate of the second type of frying oil P and the acid value (measured using measuring device 4), comparing the measured value of the viscosity increase rate with the value calculated from formulas (31), (32), (37), and (38), respectively.

[0383] Figure 118 is a graph showing the correlation between the viscosity increase rate of the second type of frying oil P (measured using measuring device 4) and the amount of polar compounds contained in the frying oil P, comparing the measured value of the amount of polar compounds with the value calculated from formulas (31), (32), (37), and (38), respectively. Figure 119 is a graph showing the correlation between the acid value of the frying oil P (measured using measuring device 4) and the viscosity increase r...

Claims

1. An oil and fat deterioration detection device for detecting the degree of deterioration of an oil and fat based on the amount of polar compounds in the oil and fat, which is one of the indicators of oil and fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The linear coefficient α and constant β included in formula (1), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (2), are each set to values ​​corresponding to the amount of food fried per unit time using the edible oil. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

2. A fat deterioration detection device for detecting the degree of deterioration of a fat based on the amount of polar compounds in the fat, which is one of the indicators of fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The aforementioned storage unit is A linear equation represented by the following equation (3), obtained by subtracting the air heating variable EH1, which is set to consider air heating where only the oil is heated without cooking the ingredients, from the aforementioned equation (1), or a quadratic equation represented by the following equation (4), obtained by subtracting the air heating variable EH2, which is set to consider air heating, from the aforementioned equation (2), is stored as the correlation equation. DIn=α×(PCn)+β−EH1...(3) α: Linear coefficient of PCn β: constant EH1: Air heating variable DIn=γ×(PCn) 2 +δ×(PCn)+ε−EH2...(4) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant EH2: Air heating variable The aforementioned deterioration index calculation unit, When the oil is subjected to the aforementioned air heating, the predetermined deterioration index is calculated using the formula (3) or formula (4) stored in the memory unit. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

3. A fat deterioration detection device for detecting the degree of deterioration of a fat based on the amount of polar compounds in the fat, which is one of the indicators of fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oils and fats are The oils and fats are classified into first type and second type based on the fatty acid composition of the oils and fats. The first oil type mentioned above is, The oil is a type of oil in which the oleic acid content is greater than the linoleic acid content. The aforementioned second type of oil is, The oil type having a composition in which the oleic acid content in the oil is less than or equal to the linoleic acid content, The aforementioned storage unit is A linear equation represented by the following formula (5), which includes α1, set to a value corresponding to the first oil type, as the linear coefficient α of formula (1), and β1, set to a value corresponding to the first oil type, as the constant β of formula (1), or a quadratic equation represented by the following formula (6), which includes γ1, set to a value corresponding to the first oil type, as the quadratic coefficient γ of formula (2), δ1, set to a value corresponding to the first oil type, as the linear coefficient δ of formula (2), and ε1, set to a value corresponding to the first oil type, as the constant ε of formula (2), DIn=α1×(PCn)+β1...(5) α1: Linear coefficient of PCn β1: Constant DIn=γ1×(PCn) 2 +δ1×(PCn)+ε1...(6) γ1: Quadratic coefficient of PCn δ1: Linear coefficient of PCn ε1: Constant A linear equation represented by the following formula (7) is stored as the correlation formula, which includes α2, set as the linear coefficient α of formula (1) to a value corresponding to the second oil type, and β2, set as the constant β of formula (1) to a value corresponding to the second oil type, or a quadratic equation represented by the following formula (8) is stored as the correlation formula, which includes γ2, set as the quadratic coefficient γ of formula (2) to a value corresponding to the second oil type, δ2, set as the linear coefficient δ of formula (2) to a value corresponding to the second oil type, and ε2, set as the constant ε of formula (2) to a value corresponding to the second oil type. DIn=α2×(PCn)+β2...(7) α2: Linear coefficient of PCn β2: Constant DIn=γ2×(PCn) 2 +δ2×(PCn)+ε2...(8) γ2: Quadratic coefficient of PCn δ²: Linear coefficient of PCn ε²: constant The aforementioned deterioration index calculation unit, If the oil is the first type of oil, the predetermined deterioration index is calculated using the formula (5) or formula (6) stored in the memory unit. If the oil is the second type of oil, the predetermined deterioration index is calculated using formula (7) or formula (8) stored in the memory unit. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

4. A fat deterioration detection device for detecting the degree of deterioration of a fat based on the amount of polar compounds in the fat, which is one of the indicators of fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into third-class and fourth-class based on their iodine value. The aforementioned third type of oil is, The oil is of a type in which the iodine value of the oil is less than a predetermined iodine value threshold, The fourth type of oil is, The oil is of a type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, The aforementioned storage unit is A linear equation represented by the following formula (9), which includes α3, set as the linear coefficient α of formula (1) to a value corresponding to the third oil type, and β3, set as the constant β of formula (1) to a value corresponding to the third oil type, or a quadratic equation represented by the following formula (10), which includes γ3, set as the quadratic coefficient γ of formula (2) to a value corresponding to the third oil type, δ3, set as the linear coefficient δ of formula (2) to a value corresponding to the third oil type, and ε3, set as the constant ε of formula (2) to a value corresponding to the third oil type, DIn=α3×(PCn)+β3...(9) α3: Linear coefficient of PCn β3: Constant DIn=γ3×(PCn) 2 +δ3×(PCn)+ε3...(10) γ3: Quadratic coefficient of PCn δ³: Linear coefficient of PCn ε³: constant The following linear equation, represented by formula (11), includes α4, set as the linear coefficient α in formula (1) to a value corresponding to the fourth oil type, and β4, set as the constant β in formula (1) to a value corresponding to the fourth oil type, or the following quadratic equation, represented by formula (12), includes γ4, set as the quadratic coefficient γ in formula (2) to a value corresponding to the fourth oil type, δ4, set as the linear coefficient δ in formula (2) to a value corresponding to the fourth oil type, and ε4, set as the constant ε in formula (2) to a value corresponding to the fourth oil type, and is stored as the correlation formula. DIn=α4×(PCn)+β4...(11) α4: Linear coefficient of PCn β4: Constant DIn=γ4×(PCn) 2 +δ4×(PCn)+ε4...(12) γ4: Quadratic coefficient of PCn δ₄: ​​Linear coefficient of PCn ε₄: constant The aforementioned deterioration index calculation unit, If the oil is the third type of oil, the predetermined deterioration index is calculated using formula (9) or formula (10). If the oil is the fourth type of oil, the predetermined deterioration index is calculated using formula (11) or formula (12). A device for detecting the degree of oil and fat deterioration, characterized by the above features.

5. A fat deterioration detection device for detecting the degree of deterioration of a fat based on the amount of polar compounds in the fat, which is one of the indicators of fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into five types and six types based on their CDM value. The fifth type of oil is, The oil type is such that the CDM value of the oil is equal to or greater than a predetermined CDM threshold. The sixth type of oil is, The oil type is such that the CDM value of the oil is less than the predetermined CDM threshold, The aforementioned storage unit is A linear equation represented by the following formula (13), which includes α5, set as the linear coefficient α of formula (1) to a value corresponding to the fifth oil type, and β5, set as the constant β of formula (1) to a value corresponding to the fifth oil type, or a quadratic equation represented by the following formula (14), which includes γ5, set as the quadratic coefficient γ of formula (2) to a value corresponding to the fifth oil type, δ5, set as the linear coefficient δ of formula (2) to a value corresponding to the fifth oil type, and ε5, set as the constant ε of formula (2) to a value corresponding to the fifth oil type, DIn=α5×(PCn)+β5...(13) α5: Linear coefficient of PCn β5: Constant DIn=γ5×(PCn) 2 +δ5×(PCn)+ε5...(14) γ5: Quadratic coefficient of PCn δ5: Linear coefficient of PCn ε5: constant A linear equation represented by the following formula (15) containing α6, which is set to a value corresponding to the sixth oil type as the linear coefficient α of formula (1), and β6, which is set to a value corresponding to the sixth oil type as the constant β of formula (1), is stored as the correlation formula. Alternatively, a quadratic equation represented by the following formula (16) containing γ6, which is set to a value corresponding to the sixth oil type as the quadratic coefficient γ of formula (2), δ6, which is set to a value corresponding to the sixth oil type as the linear coefficient δ of formula (2), and ε6, which is set to a value corresponding to the sixth oil type as the constant ε of formula (2), is stored as the correlation formula. DIn=α6×(PCn)+β6...(15) α6: Linear coefficient of PCn β6: Constant DIn=γ6×(PCn) 2 +δ6×(PCn)+ε6...(16) γ6: Quadratic coefficient of PCn δ6: Linear coefficient of PCn ε₆: constant The aforementioned deterioration index calculation unit, If the oil is the fifth type of oil, the predetermined deterioration index is calculated using formula (13) or formula (14). If the oil is the sixth type of oil, the predetermined deterioration index is calculated using formula (15) or formula (16). A device for detecting the degree of oil and fat deterioration, characterized by the above features.

6. A fat deterioration detection device for detecting the degree of deterioration of a fat based on the amount of polar compounds in the fat, which is one of the indicators of fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into seventh and eighth oil types based on the lipid molecular species present. The seventh type of oil is, The oil is such that the content of lipid molecular species in the oil is greater than a predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is less than or equal to a predetermined decrease rate threshold. The eighth oil type mentioned above is, The oil is such that the content of lipid molecular species in the oil is below the predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is greater than the predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is greater than the predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is greater than the predetermined decrease rate threshold. The aforementioned storage unit is A linear equation represented by the following formula (17), which includes α7, set as the linear coefficient α of formula (1) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (1) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (18), which includes γ7, set as the quadratic coefficient γ of formula (2) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (2) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (2) to a value corresponding to the seventh oil type, DIn=α7×(PCn)+β7...(17) α7: Linear coefficient of PCn β7: Constant DIn=γ7×(PCn) 2 +δ7×(PCn)+ε7...(18) γ7: Quadratic coefficient of PCn δ7: Linear coefficient of PCn ε7: Constant A linear equation represented by the following formula (19) containing α8, which is set as the linear coefficient α of formula (1) to a value corresponding to the eighth oil type, and β8, which is set as the constant β of formula (1) to a value corresponding to the eighth oil type, or a quadratic equation represented by the following formula (20) containing γ8, which is set as the quadratic coefficient γ of formula (2) to a value corresponding to the eighth oil type, δ8, which is set as the linear coefficient δ of formula (2) to a value corresponding to the eighth oil type, and ε8, which is set as the constant ε of formula (2) to a value corresponding to the eighth oil type, is stored as the correlation formula. DIn=α8×(PCn)+β8...(19) α8: Linear coefficient of PCn β8: Constant DIn=γ8×(PCn) 2 +δ8×(PCn)+ε8...(20) γ8: Quadratic coefficient of PCn δ8: Linear coefficient of PCn ε₁: constant The aforementioned deterioration index calculation unit, If the oil is the seventh type of oil, the predetermined deterioration index is calculated using formula (17) or formula (18). If the oil is the eighth type of oil, the predetermined deterioration index is calculated using formula (19) or formula (20). A device for detecting the degree of oil and fat deterioration, characterized by the above features.

7. A fat deterioration degree detection device for detecting the degree of fat deterioration based on the amount of polar compounds in the fat, which is one of the indicators of fat deterioration, A storage unit that stores the correlation between the amount of the polar compound and a predetermined degradation index other than the amount of the polar compound, A data acquisition unit that acquires measured values ​​of the polar compound amount, A degradation index calculation unit calculates a predetermined degradation index based on the measured values ​​of the polar compound amount obtained by the data acquisition unit and the correlation stored in the storage unit, The system includes a detection result output unit that outputs the predetermined deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The aforementioned predetermined degradation index is a correlation equation expressed as a polynomial of the amount of the polar compound, Let the amount of the polar compound be PC, Let the aforementioned predetermined degradation index be DI. If n is the arbitrary heating time of the oil and fat, It will be a linear expression represented by the following formula (1), or a quadratic expression represented by the following formula (2). DIn=α×(PCn)+β...(1) α: Linear coefficient of PCn β: constant DIn=γ×(PCn) 2 +δ×(PCn)+ε...(2) γ: Quadratic coefficient of PCn δ: linear coefficient of PCn ε: constant The aforementioned oil is edible oil used for cooking food ingredients. When the deterioration index calculation unit calculates the color of the edible oil as the predetermined deterioration index, the linear coefficient α and constant β included in formula (1), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (2) are each set to values ​​corresponding to the type of food being fried using the edible oil. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

8. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32), are each set to values ​​corresponding to the amount of food fried per unit time using the edible oil. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

9. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The aforementioned storage unit is A linear equation represented by the following equation (33), obtained by adding a term for the air heating variable EH1, which is set to take into account air heating where only the oil is heated without cooking the ingredients, to the above equation (31), or a quadratic equation represented by the following equation (34), obtained by adding a term for the air heating variable EH2, which is set to take into account air heating, to the above equation (32), is stored as the correlation equation. Di2n=α×(Di1n)+β+EH1...(33) α: linear coefficient of Di1n β: constant EH1: Air heating variable Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε+EH2...(34) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant EH2: Air heating variable The aforementioned deterioration index calculation unit, When the oil is subjected to the aforementioned air heating, the second deterioration index is calculated using the formula (33) or formula (34) stored in the memory unit. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

10. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The oils and fats are classified into first type and second type based on the fatty acid composition of the oils and fats. The first oil type mentioned above is, The oil is a type of oil in which the oleic acid content is greater than the linoleic acid content. The aforementioned second type of oil is, The oil type having a composition in which the oleic acid content in the oil is less than or equal to the linoleic acid content, The aforementioned storage unit is A linear equation represented by the following formula (35), which includes α1, set to a value corresponding to the first oil type, as the linear coefficient α of the formula (31), and β1, set to a value corresponding to the first oil type, as the constant β of the formula (31), or a quadratic equation represented by the following formula (36), which includes γ1, set to a value corresponding to the first oil type, as the quadratic coefficient γ of the formula (32), δ1, set to a value corresponding to the first oil type, as the linear coefficient δ of the formula (32), and ε1, set to a value corresponding to the first oil type, as the constant ε of the formula (32), Di2n=α1×(Di1n)+β1...(35) α1: The linear coefficient of Di1n β1: Constant Di2n=γ1×(Di1n) 2 +δ1×(Di1n)+ε1...(36) γ1: Quadratic coefficient of Di1n δ1: The linear coefficient of Di1n ε1: Constant A linear equation represented by the following formula (37) is stored as the correlation formula, which includes α2, set to a value corresponding to the second oil type, as the linear coefficient α of formula (31), and β2, set to a value corresponding to the second oil type, as the constant β of formula (31), or a quadratic equation represented by the following formula (38) is stored as the correlation formula, which includes γ2, set to a value corresponding to the second oil type, as the quadratic coefficient γ of formula (32), δ2, set to a value corresponding to the second oil type, as the linear coefficient δ of formula (32), and ε2, set to a value corresponding to the second oil type, as the constant ε of formula (32), as the constant ε2, as the constant corresponding to the second oil type. Di2n=α2×(Di1n)+β2...(37) α2: The linear coefficient of Di1n β2: Constant Di2n=γ2×(Di1n) 2 +δ2×(Di1n)+ε2...(38) γ2: Quadratic coefficient of Di1n δ²: First coefficient of Di1n ε²: constant The aforementioned deterioration index calculation unit, If the oil is the first type of oil, the second deterioration index is calculated using the formula (35) or formula (36) stored in the memory unit. If the oil is the second type of oil, the second deterioration index is calculated using formula (37) or formula (38) stored in the memory unit. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

11. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into third-class and fourth-class based on their iodine value. The aforementioned third type of oil is, The oil is of a type in which the iodine value of the oil is less than a predetermined iodine value threshold, The fourth type of oil is, The oil is of a type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, The aforementioned storage unit is A linear equation represented by the following formula (39), which includes α3, set to a value corresponding to the third oil type as the linear coefficient α of formula (31), and β3, set to a value corresponding to the third oil type as the constant β of formula (31), or a quadratic equation represented by the following formula (40), which includes γ3, set to a value corresponding to the third oil type as the quadratic coefficient γ of formula (32), δ3, set to a value corresponding to the third oil type as the linear coefficient δ of formula (32), and ε3, set to a value corresponding to the third oil type as the constant ε of formula (32), Di2n=α3×(Di1n)+β3...(39) α3: Linear coefficient of Di1n β3: Constant Di2n=γ3×(Di1n) 2 +δ3×(Di1n)+ε3...(40) γ3: Quadratic coefficient of Di1n δ³: First-order coefficient of Di1n ε³: constant A linear equation represented by the following formula (41) containing α4, which is set to a value corresponding to the fourth oil type as the linear coefficient α of formula (31), and β4, which is set to a value corresponding to the fourth oil type as the constant β of formula (31), is stored as the correlation formula. Alternatively, a quadratic equation represented by the following formula (42) contains γ4, which is set to a value corresponding to the fourth oil type as the quadratic coefficient γ of formula (32), δ4, which is set to a value corresponding to the fourth oil type as the linear coefficient δ of formula (32), and ε4, which is set to a value corresponding to the fourth oil type as the constant ε of formula (32), is stored as the correlation formula. Di2n=α4×(Di1n)+β4...(41) α4: Linear coefficient of Di1n β4: Constant Di2n=γ4×(Di1n) 2 +δ4×(Di1n)+ε4...(42) γ4: Quadratic coefficient of Di1n δ₄: ​​Linear coefficient of Di₁n ε₄: constant The aforementioned deterioration index calculation unit, If the oil is the third type of oil, the second deterioration index is calculated using formula (39) or formula (40). If the oil is the fourth type of oil, the second deterioration index is calculated using formula (41) or formula (42). A device for detecting the degree of oil and fat deterioration, characterized by the above features.

12. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into five types and six types based on their CDM value. The fifth type of oil is, The oil type is such that the CDM value of the oil is equal to or greater than a predetermined CDM threshold. The sixth type of oil is, The oil type is such that the CDM value of the oil is less than the predetermined CDM threshold, The aforementioned storage unit is A linear equation represented by the following formula (43) which includes α5, set as the linear coefficient α of formula (31) to a value corresponding to the fifth oil type, and β5, set as the constant β of formula (31) to a value corresponding to the fifth oil type, or a quadratic equation represented by the following formula (44) which includes γ5, set as the quadratic coefficient γ of formula (32) to a value corresponding to the fifth oil type, δ5, set as the linear coefficient δ of formula (32) to a value corresponding to the fifth oil type, and ε5, set as the constant ε of formula (32) to a value corresponding to the fifth oil type, Di2n=α5×(Di1n)+β5...(43) α5: The linear coefficient of Di1n β5: Constant Di2n=γ5×(Di1n) 2 +δ5×(Di1n)+ε5...(44) γ5: Quadratic coefficient of Di1n δ5: First-order coefficient of Di1n ε5: constant A linear equation represented by the following formula (45) is stored as the correlation formula, which includes α6, set as the linear coefficient α of formula (31) to a value corresponding to the sixth oil type, and β6, set as the constant β of formula (31) to a value corresponding to the sixth oil type, or a quadratic equation represented by the following formula (46) is stored as the correlation formula, which includes γ6, set as the quadratic coefficient γ of formula (32) to a value corresponding to the sixth oil type, δ6, set as the linear coefficient δ of formula (32) to a value corresponding to the sixth oil type, and ε6, set as the constant ε of formula (32) to a value corresponding to the sixth oil type. Di2n=α6×(Di1n)+β6...(45) α6: The linear coefficient of Di1n β6: Constant Di2n=γ6×(Di1n) 2 +δ6×(Di1n)+ε6...(46) γ6: Quadratic coefficient of Di1n δ6: First-order coefficient of Di1n ε₆: constant The aforementioned deterioration index calculation unit, If the oil is the fifth type of oil, the second deterioration index is calculated using formula (43) or formula (44). If the oil is the sixth type of oil, the second deterioration index is calculated using formula (45) or formula (46). A device for detecting the degree of oil and fat deterioration, characterized by the above features.

13. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into seventh and eighth oil types based on the lipid molecular species present. The seventh type of oil is, The oil is such that the content of lipid molecular species in the oil is greater than a predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is less than or equal to a predetermined decrease rate threshold. The eighth oil type mentioned above is, The oil is such that the content of lipid molecular species in the oil is below the predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is greater than the predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is greater than the predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is greater than the predetermined decrease rate threshold. The aforementioned storage unit is A linear equation represented by the following formula (47), which includes α7, set as the linear coefficient α of formula (31) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (31) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (48), which includes γ7, set as the quadratic coefficient γ of formula (32) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (32) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (32) to a value corresponding to the seventh oil type, Di2n=α7×(Di1n)+β7...(47) α7: The linear coefficient of Di1n β7: Constant Di2n=γ7×(Di1n) 2 +δ7×(Di1n)+ε7...(48) γ7: Quadratic coefficient of Di1n δ7: First-order coefficient of Di1n ε7: Constant A linear equation represented by the following formula (49) is stored as the correlation formula, which includes α8, set as the linear coefficient α of formula (31) to a value corresponding to the eighth oil type, and β8, set as the constant β of formula (31) to a value corresponding to the eighth oil type, or a quadratic equation represented by the following formula (50) is stored as the correlation formula, which includes γ8, set as the quadratic coefficient γ of formula (32) to a value corresponding to the eighth oil type, δ8, set as the linear coefficient δ of formula (32) to a value corresponding to the eighth oil type, and ε8, set as the constant ε of formula (32) to a value corresponding to the eighth oil type. Di2n=α8×(Di1n)+β8...(49) α8: The linear coefficient of Di1n β8: Constant Di2n=γ8×(Di1n) 2 +δ8×(Di1n)+ε8...(50) γ8: Quadratic coefficient of Di1n δ8: Linear coefficient of Di1n ε₁: constant The aforementioned deterioration index calculation unit, If the oil is the seventh type of oil, the second deterioration index is calculated using formula (47) or formula (48). If the oil is the eighth type of oil, the second deterioration index is calculated using formula (49) or formula (50). A device for detecting the degree of oil and fat deterioration, characterized by the above features.

14. A device for detecting the degree of deterioration of oils and fats, A storage unit that stores the correlation between a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index. A data acquisition unit that acquires the measured value of the first degradation index, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. When the deterioration index calculation unit calculates the color of the edible oil as the second deterioration index, the linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32) are each set to values ​​corresponding to the type of food being fried using the edible oil. A device for detecting the degree of oil and fat deterioration, characterized by the above features.

15. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32), are each set to values ​​corresponding to the amount of food fried per unit time using the edible oil. A system for detecting the degree of oil and fat deterioration, characterized by the following features.

16. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The aforementioned storage unit is A linear equation represented by the following equation (33), obtained by adding a term for the air heating variable EH1, which is set to take into account air heating where only the oil is heated without cooking the ingredients, to the above equation (31), or a quadratic equation represented by the following equation (34), obtained by adding a term for the air heating variable EH2, which is set to take into account air heating, to the above equation (32), is stored as the correlation equation. Di2n=α×(Di1n)+β+EH1...(33) α: linear coefficient of Di1n β: constant EH1: Air heating variable Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε+EH2...(34) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant EH2: Air heating variable The aforementioned deterioration index calculation unit, When the oil is subjected to the aforementioned air heating, the second deterioration index is calculated using the formula (33) or formula (34) stored in the memory unit. A system for detecting the degree of oil and fat deterioration, characterized by the following features.

17. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The oils and fats are classified into first type and second type based on the fatty acid composition of the oils and fats. The first oil type mentioned above is, The oil is a type of oil in which the oleic acid content is greater than the linoleic acid content. The aforementioned second type of oil is, The oil type having a composition in which the oleic acid content in the oil is less than or equal to the linoleic acid content, The aforementioned storage unit is A linear equation represented by the following formula (35), which includes α1, set to a value corresponding to the first oil type, as the linear coefficient α of the formula (31), and β1, set to a value corresponding to the first oil type, as the constant β of the formula (31), or a quadratic equation represented by the following formula (36), which includes γ1, set to a value corresponding to the first oil type, as the quadratic coefficient γ of the formula (32), δ1, set to a value corresponding to the first oil type, as the linear coefficient δ of the formula (32), and ε1, set to a value corresponding to the first oil type, as the constant ε of the formula (32), Di2n=α1×(Di1n)+β1...(35) α1: The linear coefficient of Di1n β1: Constant Di2n=γ1×(Di1n) 2 +δ1×(Di1n)+ε1...(36) γ1: Quadratic coefficient of Di1n δ1: The linear coefficient of Di1n ε1: Constant A linear equation represented by the following formula (37) is stored as the correlation formula, which includes α2, set to a value corresponding to the second oil type, as the linear coefficient α of formula (31), and β2, set to a value corresponding to the second oil type, as the constant β of formula (31), or a quadratic equation represented by the following formula (38) is stored as the correlation formula, which includes γ2, set to a value corresponding to the second oil type, as the quadratic coefficient γ of formula (32), δ2, set to a value corresponding to the second oil type, as the linear coefficient δ of formula (32), and ε2, set to a value corresponding to the second oil type, as the constant ε of formula (32), as the constant ε2, as the constant corresponding to the second oil type. Di2n=α2×(Di1n)+β2...(37) α2: The linear coefficient of Di1n β2: Constant Di2n=γ2×(Di1n) 2 +δ2×(Di1n)+ε2...(38) γ2: Quadratic coefficient of Di1n δ²: First coefficient of Di1n ε²: constant The aforementioned deterioration index calculation unit, If the oil is the first type of oil, the second deterioration index is calculated using the formula (35) or formula (36) stored in the memory unit. If the oil is the second type of oil, the second deterioration index is calculated using formula (37) or formula (38) stored in the memory unit. A system for detecting the degree of oil and fat deterioration, characterized by the following features.

18. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into third-class and fourth-class based on their iodine value. The aforementioned third type of oil is, The oil is of a type in which the iodine value of the oil is less than a predetermined iodine value threshold, The fourth type of oil is, The oil is of a type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, The aforementioned storage unit is A linear equation represented by the following formula (39), which includes α3, set to a value corresponding to the third oil type as the linear coefficient α of formula (31), and β3, set to a value corresponding to the third oil type as the constant β of formula (31), or a quadratic equation represented by the following formula (40), which includes γ3, set to a value corresponding to the third oil type as the quadratic coefficient γ of formula (32), δ3, set to a value corresponding to the third oil type as the linear coefficient δ of formula (32), and ε3, set to a value corresponding to the third oil type as the constant ε of formula (32), Di2n=α3×(Di1n)+β3...(39) α3: Linear coefficient of Di1n β3: Constant Di2n=γ3×(Di1n) 2 +δ3×(Di1n)+ε3...(40) γ3: Quadratic coefficient of Di1n δ³: First-order coefficient of Di1n ε³: constant A linear equation represented by the following formula (41) containing α4, which is set to a value corresponding to the fourth oil type as the linear coefficient α of formula (31), and β4, which is set to a value corresponding to the fourth oil type as the constant β of formula (31), is stored as the correlation formula. Alternatively, a quadratic equation represented by the following formula (42) contains γ4, which is set to a value corresponding to the fourth oil type as the quadratic coefficient γ of formula (32), δ4, which is set to a value corresponding to the fourth oil type as the linear coefficient δ of formula (32), and ε4, which is set to a value corresponding to the fourth oil type as the constant ε of formula (32), is stored as the correlation formula. Di2n=α4×(Di1n)+β4...(41) α4: Linear coefficient of Di1n β4: Constant Di2n=γ4×(Di1n) 2 +δ4×(Di1n)+ε4...(42) γ4: Quadratic coefficient of Di1n δ₄: ​​Linear coefficient of Di₁n ε₄: constant The aforementioned deterioration index calculation unit, If the oil is the third type of oil, the second deterioration index is calculated using formula (39) or formula (40). If the oil is the fourth type of oil, the second deterioration index is calculated using formula (41) or formula (42). A system for detecting the degree of oil and fat deterioration, characterized by the following features.

19. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into five types and six types based on their CDM value. The fifth type of oil is, The oil type is such that the CDM value of the oil is equal to or greater than a predetermined CDM threshold. The sixth type of oil is, The oil type is such that the CDM value of the oil is less than the predetermined CDM threshold, The aforementioned storage unit is A linear equation represented by the following formula (43) which includes α5, set as the linear coefficient α of formula (31) to a value corresponding to the fifth oil type, and β5, set as the constant β of formula (31) to a value corresponding to the fifth oil type, or a quadratic equation represented by the following formula (44) which includes γ5, set as the quadratic coefficient γ of formula (32) to a value corresponding to the fifth oil type, δ5, set as the linear coefficient δ of formula (32) to a value corresponding to the fifth oil type, and ε5, set as the constant ε of formula (32) to a value corresponding to the fifth oil type, Di2n=α5×(Di1n)+β5...(43) α5: The linear coefficient of Di1n β5: Constant Di2n=γ5×(Di1n) 2 +δ5×(Di1n)+ε5...(44) γ5: Quadratic coefficient of Di1n δ5: First-order coefficient of Di1n ε5: constant A linear equation represented by the following formula (45) is stored as the correlation formula, which includes α6, set as the linear coefficient α of formula (31) to a value corresponding to the sixth oil type, and β6, set as the constant β of formula (31) to a value corresponding to the sixth oil type, or a quadratic equation represented by the following formula (46) is stored as the correlation formula, which includes γ6, set as the quadratic coefficient γ of formula (32) to a value corresponding to the sixth oil type, δ6, set as the linear coefficient δ of formula (32) to a value corresponding to the sixth oil type, and ε6, set as the constant ε of formula (32) to a value corresponding to the sixth oil type. Di2n=α6×(Di1n)+β6...(45) α6: The linear coefficient of Di1n β6: Constant Di2n=γ6×(Di1n) 2 +δ6×(Di1n)+ε6...(46) γ6: Quadratic coefficient of Di1n δ6: First-order coefficient of Di1n ε₆: constant The aforementioned deterioration index calculation unit, If the oil is the fifth type of oil, the second deterioration index is calculated using formula (43) or formula (44). If the oil is the sixth type of oil, the second deterioration index is calculated using formula (45) or formula (46). A system for detecting the degree of oil and fat deterioration, characterized by the following features.

20. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into seventh and eighth oil types based on the lipid molecular species present. The seventh type of oil is, The oil is such that the content of lipid molecular species in the oil is greater than a predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is less than or equal to a predetermined decrease rate threshold. The eighth oil type mentioned above is, The oil is such that the content of lipid molecular species in the oil is below the predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is greater than the predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is greater than the predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is greater than the predetermined decrease rate threshold. The aforementioned storage unit is A linear equation represented by the following formula (47), which includes α7, set as the linear coefficient α of formula (31) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (31) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (48), which includes γ7, set as the quadratic coefficient γ of formula (32) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (32) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (32) to a value corresponding to the seventh oil type, Di2n=α7×(Di1n)+β7...(47) α7: The linear coefficient of Di1n β7: Constant Di2n=γ7×(Di1n) 2 +δ7×(Di1n)+ε7...(48) γ7: Quadratic coefficient of Di1n δ7: First-order coefficient of Di1n ε7: Constant A linear equation represented by the following formula (49) is stored as the correlation formula, which includes α8, set as the linear coefficient α of formula (31) to a value corresponding to the eighth oil type, and β8, set as the constant β of formula (31) to a value corresponding to the eighth oil type, or a quadratic equation represented by the following formula (50) is stored as the correlation formula, which includes γ8, set as the quadratic coefficient γ of formula (32) to a value corresponding to the eighth oil type, δ8, set as the linear coefficient δ of formula (32) to a value corresponding to the eighth oil type, and ε8, set as the constant ε of formula (32) to a value corresponding to the eighth oil type. Di2n=α8×(Di1n)+β8...(49) α8: The linear coefficient of Di1n β8: Constant Di2n=γ8×(Di1n) 2 +δ8×(Di1n)+ε8...(50) γ8: Quadratic coefficient of Di1n δ8: Linear coefficient of Di1n ε₁: constant The aforementioned deterioration index calculation unit, If the oil is the seventh type of oil, the second deterioration index is calculated using formula (47) or formula (48). If the oil is the eighth type of oil, the second deterioration index is calculated using formula (49) or formula (50). A system for detecting the degree of oil and fat deterioration, characterized by the following features.

21. A system for detecting the degree of deterioration of oils and fats, A measuring device for measuring a first deterioration index of the oil and fat, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, The system includes an oil deterioration degree detection device that detects the degree of deterioration of the oil based on the measured value of the first deterioration index measured by the measuring device, The oil and fat deterioration degree detection device is, A storage unit that stores the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index, A data acquisition unit that acquires the measured value of the first degradation index measured by the measuring device, A degradation index calculation unit calculates a second degradation index based on the measured value of the first degradation index acquired by the data acquisition unit and the correlation stored in the storage unit. The system includes a detection result output unit that outputs the second deterioration index calculated by the deterioration index calculation unit as the detection result of the degree of deterioration, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. When the deterioration index calculation unit calculates the color of the edible oil as the second deterioration index, the linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32) are each set to values ​​corresponding to the type of food being fried using the edible oil. A system for detecting the degree of oil and fat deterioration, characterized by the following features.

22. A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32), are each set to values ​​corresponding to the amount of food fried per unit time using the edible oil. A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

23. A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The oil and fat deterioration degree detection device is, A linear equation represented by the following equation (33), obtained by adding a term for the air heating variable EH1, which is set to take into account air heating where only the oil is heated without cooking the ingredients, to the above equation (31), or a quadratic equation represented by the following equation (34), obtained by adding a term for the air heating variable EH2, which is set to take into account air heating, to the above equation (32), is stored as the correlation equation. Di2n=α×(Di1n)+β+EH1...(33) α: linear coefficient of Di1n β: constant EH1: Air heating variable Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε+EH2...(34) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant EH2: Air heating variable In the aforementioned degradation index calculation step, When the oil is subjected to the aforementioned air heating, the second deterioration index is calculated using formula (33) or formula (34). A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

24. A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The oils and fats are classified into first type and second type based on the fatty acid composition of the oils and fats. The first oil type mentioned above is, The oil is a type of oil in which the oleic acid content is greater than the linoleic acid content. The aforementioned second type of oil is, The oil type having a composition in which the oleic acid content in the oil is less than or equal to the linoleic acid content, The oil and fat deterioration degree detection device is, A linear equation represented by the following formula (35), which includes α1, set to a value corresponding to the first oil type, as the linear coefficient α of the formula (31), and β1, set to a value corresponding to the first oil type, as the constant β of the formula (31), or a quadratic equation represented by the following formula (36), which includes γ1, set to a value corresponding to the first oil type, as the quadratic coefficient γ of the formula (32), δ1, set to a value corresponding to the first oil type, as the linear coefficient δ of the formula (32), and ε1, set to a value corresponding to the first oil type, as the constant ε of the formula (32), Di2n=α1×(Di1n)+β1...(35) α1: The linear coefficient of Di1n β1: Constant Di2n=γ1×(Di1n) 2 +δ1×(Di1n)+ε1...(36) γ1: Quadratic coefficient of Di1n δ1: The linear coefficient of Di1n ε1: Constant A linear equation represented by the following formula (37) is stored as the correlation formula, which includes α2, set to a value corresponding to the second oil type, as the linear coefficient α of formula (31), and β2, set to a value corresponding to the second oil type, as the constant β of formula (31), or a quadratic equation represented by the following formula (38) is stored as the correlation formula, which includes γ2, set to a value corresponding to the second oil type, as the quadratic coefficient γ of formula (32), δ2, set to a value corresponding to the second oil type, as the linear coefficient δ of formula (32), and ε2, set to a value corresponding to the second oil type, as the constant ε of formula (32), as the constant ε2, as the constant corresponding to the second oil type. Di2n=α2×(Di1n)+β2...(37) α2: The linear coefficient of Di1n β2: Constant Di2n=γ2×(Di1n) 2 +δ2×(Di1n)+ε2...(38) γ2: Quadratic coefficient of Di1n δ²: First coefficient of Di1n ε²: constant In the aforementioned degradation index calculation step, If the oil is the first type of oil, the second deterioration index is calculated using formula (35) or formula (36). If the oil is the second type of oil, the second deterioration index is calculated using formula (37) or formula (38). A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

25. A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into third-class and fourth-class based on their iodine value. The aforementioned third type of oil is, The oil is of a type in which the iodine value of the oil is less than a predetermined iodine value threshold, The fourth type of oil is, The oil is of a type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, The oil and fat deterioration degree detection device is, A linear equation represented by the following formula (39), which includes α3, set to a value corresponding to the third oil type as the linear coefficient α of formula (31), and β3, set to a value corresponding to the third oil type as the constant β of formula (31), or a quadratic equation represented by the following formula (40), which includes γ3, set to a value corresponding to the third oil type as the quadratic coefficient γ of formula (32), δ3, set to a value corresponding to the third oil type as the linear coefficient δ of formula (32), and ε3, set to a value corresponding to the third oil type as the constant ε of formula (32), Di2n=α3×(Di1n)+β3...(39) α3: Linear coefficient of Di1n β3: Constant Di2n=γ3×(Di1n) 2 +δ3×(Di1n)+ε3...(40) γ3: Quadratic coefficient of Di1n δ³: First-order coefficient of Di1n ε³: constant A linear equation represented by the following formula (41) containing α4, which is set to a value corresponding to the fourth oil type as the linear coefficient α of formula (31), and β4, which is set to a value corresponding to the fourth oil type as the constant β of formula (31), is stored as the correlation formula. Alternatively, a quadratic equation represented by the following formula (42) contains γ4, which is set to a value corresponding to the fourth oil type as the quadratic coefficient γ of formula (32), δ4, which is set to a value corresponding to the fourth oil type as the linear coefficient δ of formula (32), and ε4, which is set to a value corresponding to the fourth oil type as the constant ε of formula (32), is stored as the correlation formula. Di2n=α4×(Di1n)+β4...(41) α4: Linear coefficient of Di1n β4: Constant Di2n=γ4×(Di1n) 2 +δ4×(Di1n)+ε4...(42) γ4: Quadratic coefficient of Di1n δ₄: ​​Linear coefficient of Di₁n ε₄: constant In the aforementioned degradation index calculation step, If the oil is the third type of oil, the second deterioration index is calculated using formula (39) or formula (40). If the oil is the fourth type of oil, the second deterioration index is calculated using formula (41) or formula (42). A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

26. A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into five types and six types based on their CDM value. The fifth type of oil is, The oil type is such that the CDM value of the oil is equal to or greater than a predetermined CDM threshold. The sixth type of oil is, The oil type is such that the CDM value of the oil is less than the predetermined CDM threshold, The oil and fat deterioration degree detection device is, A linear equation represented by the following formula (43) which includes α5, set as the linear coefficient α of formula (31) to a value corresponding to the fifth oil type, and β5, set as the constant β of formula (31) to a value corresponding to the fifth oil type, or a quadratic equation represented by the following formula (44) which includes γ5, set as the quadratic coefficient γ of formula (32) to a value corresponding to the fifth oil type, δ5, set as the linear coefficient δ of formula (32) to a value corresponding to the fifth oil type, and ε5, set as the constant ε of formula (32) to a value corresponding to the fifth oil type, Di2n=α5×(Di1n)+β5...(43) α5: The linear coefficient of Di1n β5: Constant Di2n=γ5×(Di1n) 2 +δ5×(Di1n)+ε5...(44) γ5: Quadratic coefficient of Di1n δ5: First-order coefficient of Di1n ε5: constant A linear equation represented by the following formula (45) is stored as the correlation formula, which includes α6, set as the linear coefficient α of formula (31) to a value corresponding to the sixth oil type, and β6, set as the constant β of formula (31) to a value corresponding to the sixth oil type, or a quadratic equation represented by the following formula (46) is stored as the correlation formula, which includes γ6, set as the quadratic coefficient γ of formula (32) to a value corresponding to the sixth oil type, δ6, set as the linear coefficient δ of formula (32) to a value corresponding to the sixth oil type, and ε6, set as the constant ε of formula (32) to a value corresponding to the sixth oil type. Di2n=α6×(Di1n)+β6...(45) α6: The linear coefficient of Di1n β6: Constant Di2n=γ6×(Di1n) 2 +δ6×(Di1n)+ε6...(46) γ6: Quadratic coefficient of Di1n δ6: First-order coefficient of Di1n ε₆: constant In the aforementioned degradation index calculation step, If the oil is the fifth type of oil, the second deterioration index is calculated using formula (43) or formula (44). If the oil is the sixth type of oil, the second deterioration index is calculated using formula (45) or formula (46). A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

27. ​​A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into seventh and eighth oil types based on the lipid molecular species present. The seventh type of oil is, The oil is such that the content of lipid molecular species in the oil is greater than a predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is less than or equal to a predetermined decrease rate threshold. The eighth oil type mentioned above is, The oil is such that the content of lipid molecular species in the oil is below the predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is greater than the predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is greater than the predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is greater than the predetermined decrease rate threshold. The oil and fat deterioration degree detection device is, A linear equation represented by the following formula (47), which includes α7, set as the linear coefficient α of formula (31) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (31) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (48), which includes γ7, set as the quadratic coefficient γ of formula (32) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (32) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (32) to a value corresponding to the seventh oil type, Di2n=α7×(Di1n)+β7...(47) α7: The linear coefficient of Di1n β7: Constant Di2n=γ7×(Di1n) 2 +δ7×(Di1n)+ε7...(48) γ7: Quadratic coefficient of Di1n δ7: First-order coefficient of Di1n ε7: Constant A linear equation represented by the following formula (49) is stored as the correlation formula, which includes α8, set as the linear coefficient α of formula (31) to a value corresponding to the eighth oil type, and β8, set as the constant β of formula (31) to a value corresponding to the eighth oil type, or a quadratic equation represented by the following formula (50) is stored as the correlation formula, which includes γ8, set as the quadratic coefficient γ of formula (32) to a value corresponding to the eighth oil type, δ8, set as the linear coefficient δ of formula (32) to a value corresponding to the eighth oil type, and ε8, set as the constant ε of formula (32) to a value corresponding to the eighth oil type. Di2n=α8×(Di1n)+β8...(49) α8: The linear coefficient of Di1n β8: Constant Di2n=γ8×(Di1n) 2 +δ8×(Di1n)+ε8...(50) γ8: Quadratic coefficient of Di1n δ8: Linear coefficient of Di1n ε₁: constant In the aforementioned degradation index calculation step, If the oil is the seventh type of oil, the second deterioration index is calculated using formula (47) or formula (48). If the oil is the eighth type of oil, the second deterioration index is calculated using formula (49) or formula (50). A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

28. A method for detecting the degree of deterioration of oils and fats, Using a measuring device that measures a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, and an oil and fat deterioration degree detection device that stores the correlation between the first deterioration index and a second deterioration index, which is a deterioration index of the oil and fat other than the first deterioration index, The measuring device performs a measurement step of measuring the first degradation index, The oil degradation detection device includes a data acquisition step in which it acquires the measured value of the first degradation index measured in the measurement step, The oil degradation degree detection device performs a degradation index calculation step in which it calculates a second degradation index based on the measured value of the first degradation index obtained in the data acquisition step and the stored correlation, The oil degradation detection device includes a detection result output step that outputs the second degradation index calculated in the degradation index calculation step as the degradation degree detection result, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. If the color of the edible oil is calculated as the second deterioration index in the deterioration index calculation step, the linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32) are each set to values ​​corresponding to the type of food being fried using the edible oil. A method for detecting the degree of oil and fat deterioration, characterized by the features described above.

29. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32), are each set to values ​​corresponding to the amount of food fried per unit time using the edible oil. A program for detecting the degree of oil and fat deterioration, characterized by the following features.

30. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. The aforementioned degradation index calculation process is: The correlation equation is a linear equation represented by the following equation (33), which is obtained by adding a term for the air heating variable EH1, which is set to take into account air heating where only the oil is heated without cooking the ingredients, to the above equation (31), or a quadratic equation represented by the following equation (34), which is obtained by adding a term for the air heating variable EH2, which is set to take into account air heating, to the above equation (32). Di2n=α×(Di1n)+β+EH1...(33) α: linear coefficient of Di1n β: constant EH1: Air heating variable Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε+EH2...(34) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant EH2: Air heating variable When the oil is subjected to the aforementioned air heating, the second deterioration index is calculated using formula (33) or formula (34). A program for detecting the degree of oil and fat deterioration, characterized by the following features.

31. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The oils and fats are classified into first type and second type based on the fatty acid composition of the oils and fats. The first oil type mentioned above is, The oil is a type of oil in which the oleic acid content is greater than the linoleic acid content. The aforementioned second type of oil is, The oil type having a composition in which the oleic acid content in the oil is less than or equal to the linoleic acid content, The aforementioned degradation index calculation process is: A linear equation represented by the following formula (35), which includes α1, set to a value corresponding to the first oil type, as the linear coefficient α of the formula (31), and β1, set to a value corresponding to the first oil type, as the constant β of the formula (31), or a quadratic equation represented by the following formula (36), which includes γ1, set to a value corresponding to the first oil type, as the quadratic coefficient γ of the formula (32), δ1, set to a value corresponding to the first oil type, as the linear coefficient δ of the formula (32), and ε1, set to a value corresponding to the first oil type, as the constant ε of the formula (32), Di2n=α1×(Di1n)+β1...(35) α1: The linear coefficient of Di1n β1: Constant Di2n=γ1×(Di1n) 2 +δ1×(Di1n)+ε1...(36) γ1: Quadratic coefficient of Di1n δ1: The linear coefficient of Di1n ε1: Constant The correlation formula is a linear equation represented by the following formula (37), which includes α2, set as the linear coefficient α of formula (31) to a value corresponding to the second oil type, and β2, set as the constant β of formula (31) to a value corresponding to the second oil type, or a quadratic equation represented by the following formula (38), which includes γ2, set as the quadratic coefficient γ of formula (32) to a value corresponding to the second oil type, δ2, set as the linear coefficient δ of formula (32) to a value corresponding to the second oil type, and ε2, set as the constant ε of formula (32) to a value corresponding to the second oil type. Di2n=α2×(Di1n)+β2...(37) α2: The linear coefficient of Di1n β2: Constant Di2n=γ2×(Di1n) 2 +δ2×(Di1n)+ε2...(38) γ2: Quadratic coefficient of Di1n δ²: First coefficient of Di1n ε²: constant If the oil is the first type of oil, the second deterioration index is calculated using formula (35) or formula (36). If the oil is the second type of oil, the second deterioration index is calculated using formula (37) or formula (38). A program for detecting the degree of oil and fat deterioration, characterized by the following features.

32. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into third-class and fourth-class based on their iodine value. The aforementioned third type of oil is, The oil is of a type in which the iodine value of the oil is less than a predetermined iodine value threshold, The fourth type of oil is, The oil is of a type in which the iodine value of the oil is equal to or greater than the predetermined iodine value threshold, The aforementioned degradation index calculation process is: A linear equation represented by the following formula (39), which includes α3, set to a value corresponding to the third oil type as the linear coefficient α of formula (31), and β3, set to a value corresponding to the third oil type as the constant β of formula (31), or a quadratic equation represented by the following formula (40), which includes γ3, set to a value corresponding to the third oil type as the quadratic coefficient γ of formula (32), δ3, set to a value corresponding to the third oil type as the linear coefficient δ of formula (32), and ε3, set to a value corresponding to the third oil type as the constant ε of formula (32), Di2n=α3×(Di1n)+β3...(39) α3: Linear coefficient of Di1n β3: Constant Di2n=γ3×(Di1n) 2 +δ3×(Di1n)+ε3...(40) γ3: Quadratic coefficient of Di1n δ³: First-order coefficient of Di1n ε³: constant The correlation formula is a linear equation represented by the following formula (41), which includes α4, set as the linear coefficient α of formula (31) to a value corresponding to the fourth oil type, and β4, set as the constant β of formula (31) to a value corresponding to the fourth oil type, or a quadratic equation represented by the following formula (42), which includes γ4, set as the quadratic coefficient γ of formula (32) to a value corresponding to the fourth oil type, δ4, set as the linear coefficient δ of formula (32) to a value corresponding to the fourth oil type, and ε4, set as the constant ε of formula (32) to a value corresponding to the fourth oil type. Di2n=α4×(Di1n)+β4...(41) α4: Linear coefficient of Di1n β4: Constant Di2n=γ4×(Di1n) 2 +δ4×(Di1n)+ε4...(42) γ4: Quadratic coefficient of Di1n δ₄: ​​Linear coefficient of Di₁n ε₄: constant If the oil is the third type of oil, the second deterioration index is calculated using formula (39) or formula (40). If the oil is the fourth type of oil, the second deterioration index is calculated using formula (41) or formula (42). A program for detecting the degree of oil and fat deterioration, characterized by the following features.

33. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into five types and six types based on their CDM value. The fifth type of oil is, The oil type is such that the CDM value of the oil is equal to or greater than a predetermined CDM threshold. The sixth type of oil is, The oil type is such that the CDM value of the oil is less than the predetermined CDM threshold, The aforementioned degradation index calculation process is: A linear equation represented by the following formula (43) which includes α5, set as the linear coefficient α of formula (31) to a value corresponding to the fifth oil type, and β5, set as the constant β of formula (31) to a value corresponding to the fifth oil type, or a quadratic equation represented by the following formula (44) which includes γ5, set as the quadratic coefficient γ of formula (32) to a value corresponding to the fifth oil type, δ5, set as the linear coefficient δ of formula (32) to a value corresponding to the fifth oil type, and ε5, set as the constant ε of formula (32) to a value corresponding to the fifth oil type, Di2n=α5×(Di1n)+β5...(43) α5: The linear coefficient of Di1n β5: Constant Di2n=γ5×(Di1n) 2 +δ5×(Di1n)+ε5...(44) γ5: Quadratic coefficient of Di1n δ5: First-order coefficient of Di1n ε5: constant The correlation formula is a linear equation represented by the following formula (45) which includes α6, set as the linear coefficient α of formula (31) to a value corresponding to the sixth oil type, and β6, set as the constant β of formula (31) to a value corresponding to the sixth oil type, or a quadratic equation represented by the following formula (46) which includes γ6, set as the quadratic coefficient γ of formula (32) to a value corresponding to the sixth oil type, δ6, set as the linear coefficient δ of formula (32) to a value corresponding to the sixth oil type, and ε6, set as the constant ε of formula (32) to a value corresponding to the sixth oil type. Di2n=α6×(Di1n)+β6...(45) α6: The linear coefficient of Di1n β6: Constant Di2n=γ6×(Di1n) 2 +δ6×(Di1n)+ε6...(46) γ6: Quadratic coefficient of Di1n δ6: First-order coefficient of Di1n ε₆: constant If the oil is the fifth type of oil, the second deterioration index is calculated using formula (43) or formula (44). If the oil is the sixth type of oil, the second deterioration index is calculated using formula (45) or formula (46). A program for detecting the degree of oil and fat deterioration, characterized by the following features.

34. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oils and fats are The aforementioned oils and fats are classified into seventh and eighth oil types based on the lipid molecular species present. The seventh type of oil is, The oil is such that the content of lipid molecular species in the oil is greater than a predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is less than or equal to a predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is less than or equal to a predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is less than or equal to a predetermined decrease rate threshold. The eighth oil type mentioned above is, The oil is such that the content of lipid molecular species in the oil is below the predetermined content threshold, the rate of increase of the diacylglycerol content in the oil due to heating is greater than the predetermined first increase rate threshold, the rate of increase of the free fatty acid content in the oil due to heating is greater than the predetermined second increase rate threshold, and the rate of decrease of the triacylglycerol content in the oil due to heating is greater than the predetermined decrease rate threshold. The aforementioned degradation index calculation process is: A linear equation represented by the following formula (47), which includes α7, set as the linear coefficient α of formula (31) to a value corresponding to the seventh oil type, and β7, set as the constant β of formula (31) to a value corresponding to the seventh oil type, or a quadratic equation represented by the following formula (48), which includes γ7, set as the quadratic coefficient γ of formula (32) to a value corresponding to the seventh oil type, δ7, set as the linear coefficient δ of formula (32) to a value corresponding to the seventh oil type, and ε7, set as the constant ε of formula (32) to a value corresponding to the seventh oil type, Di2n=α7×(Di1n)+β7...(47) α7: The linear coefficient of Di1n β7: Constant Di2n=γ7×(Di1n) 2 +δ7×(Di1n)+ε7...(48) γ7: Quadratic coefficient of Di1n δ7: First-order coefficient of Di1n ε7: Constant The correlation formula is a linear equation represented by the following formula (49) which includes α8, set as the linear coefficient α of formula (31) to a value corresponding to the eighth oil type, and β8, set as the constant β of formula (31) to a value corresponding to the eighth oil type, or a quadratic equation represented by the following formula (50) which includes γ8, set as the quadratic coefficient γ of formula (32) to a value corresponding to the eighth oil type, δ8, set as the linear coefficient δ of formula (32) to a value corresponding to the eighth oil type, and ε8, set as the constant ε of formula (32) to a value corresponding to the eighth oil type. Di2n=α8×(Di1n)+β8...(49) α8: The linear coefficient of Di1n β8: Constant Di2n=γ8×(Di1n) 2 +δ8×(Di1n)+ε8...(50) γ8: Quadratic coefficient of Di1n δ8: Linear coefficient of Di1n ε₁: constant If the oil is the seventh type of oil, the second deterioration index is calculated using formula (47) or formula (48). If the oil is the eighth type of oil, the second deterioration index is calculated using formula (49) or formula (50). A program for detecting the degree of oil and fat deterioration, characterized by the following features.

35. A program for detecting the degree of deterioration of oils and fats, A data acquisition process to acquire a measured value of a first deterioration index, which is a deterioration index of the oil and fat and is defined based on a substance produced by heating the oil and fat, A deterioration index calculation process that calculates the second deterioration index from the measured value of the first deterioration index obtained by the data acquisition process, using the correlation between the first deterioration index and the second deterioration index, which is a deterioration index of the oil other than the first deterioration index. The computer is instructed to perform a detection result output process that outputs the second deterioration index calculated by the deterioration index calculation process as the detection result of the degree of deterioration of the oil and fat, The aforementioned correlation is The correlation formula is obtained by expressing the second degradation index as a polynomial of the first degradation index. Let the first degradation index be Di1. Let the second degradation index be Di2. If n is the arbitrary heating time of the oil and fat, It will be a linear equation represented by the following formula (31), or a quadratic equation represented by the following formula (32). Di2n=α×(Di1n)+β...(31) α: linear coefficient of Di1n β: constant Di2n=γ×(Di1n) 2 +δ×(Di1n)+ε...(32) γ: Quadratic coefficient of Di1n δ: linear coefficient of Di1n ε: constant The aforementioned oil is edible oil used for cooking food ingredients. When the deterioration index calculation process calculates the color of the edible oil as the second deterioration index, the linear coefficient α and constant β included in formula (31), and the quadratic coefficient γ, linear coefficient δ, and constant ε included in formula (32) are each set to values ​​corresponding to the type of food being fried using the edible oil. A program for detecting the degree of oil and fat deterioration, characterized by the following features.

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