Diagnostic device, diagnostic system using same, and program to be executed by computer

Abstract

This diagnostic device, on the basis of a plurality of integrated values ITG1_Low to ITGn_Low, ITG1-Middle to ITGn_Middle, and ITG1_High to ITGn _ High in a plurality of prescribed potential sections calculated using the current-potential characteristics of three cyclic voltammograms measured while changing a potential according to potential scanning speeds Vr_Low, Vr_Middle, and Vr_High, calculates the sums L(+)_sum, M(+)_sum, H(+)_sum of integrated values in positive potential sections, the sum H(–)_sum of integrated values in a negative prescribed potential section, and the sums L(all)_sum, M(all)_sum, H(all)_sum of integrated values in all prescribed potential sections to diagnose the taste of an object being analyzed.

Description

Diagnostic device, diagnostic system using the same, and program for causing a computer to execute the program

[0001] The present invention relates to a diagnostic device, a diagnostic system using the same, and a program to be executed by a computer.

[0002] Techniques for analyzing solutions such as soft drinks, alcoholic beverages, tap water, urine, and blood include Fourier Transform Infrared Spectroscopy (FTIR), gas chromatography, taste sensors, and Raman spectroscopy (Non-Patent Documents 1 to 3).

[0003] However, these devices are large and expensive, have portability issues, and require specialized knowledge to handle the devices due to their complexity.

[0004] In addition, individual sensors that measure temperature, humidity, total acidity (the total acidity of all types of acids in a solution), alcohol content, etc. can be used for on-site measurements, but many of them cannot be used alone to determine the state of a solution, so multiple sensors must be used in combination.

[0005] Electrochemical sensors can simplify measurement systems, are small, inexpensive, and highly portable, and have great potential as an in-situ analytical technique for solutions in general.

[0006] H. Yu, Y. Zhang, J. Zhao, and H. Tian, ​​“Taste characteristics of Chinese bayberry juice characterized by sensory evaluation, chromatography analysis, and an electronic tongue,” J Food Sci Technol 55(5), 1624-1631 (2018). GF Abreu, FM Borem, LFC Oliveira, MR Almeida, and APC Alves, “Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage,” Food Chem 287, 241-248 (2019).R. Ferrer-Gallego, JM Hernandez-Hierro, JC Rivas-Gonzalo, and MT Escribano-Bailon, “Evaluation of sensory parameters of grapes using near infrared spectroscopy,” J Food Eng 118(3), 333-339 (2013).

[0007] However, it is difficult to diagnose the taste of alcoholic beverages using an electrochemical sensor system.

[0008] Therefore, according to an embodiment of the present invention, a diagnostic device capable of diagnosing the taste of alcoholic beverages and the like based on a cyclic voltammogram of the alcoholic beverages and the like is provided.

[0009] Furthermore, according to an embodiment of the present invention, there is provided a diagnostic system including a diagnostic device capable of diagnosing the taste of alcoholic beverages and the like based on a cyclic voltammogram of the alcoholic beverages and the like.

[0010] Furthermore, according to an embodiment of the present invention, there is provided a program for causing a computer to diagnose the taste of alcoholic beverages and the like based on a cyclic voltammogram of the alcoholic beverages and the like.

[0011] (Configuration 1) According to an embodiment of the present invention, a diagnostic device includes a first arithmetic unit and a taste diagnostic unit. The first arithmetic unit calculates a first sum (L(+)_sum) which is a sum of first integral values ​​in a predetermined positive potential interval based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of a first cyclic voltammogram measured while changing the potential at a first potential scanning rate, calculates a second sum (M(+)_sum) which is a sum of second integral values ​​in a predetermined positive potential interval based on a plurality of second integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of a second cyclic voltammogram measured while changing the potential at a second potential scanning rate faster than the first potential scanning rate, and calculates a second sum (M(+)_sum) which is a sum of second integral values ​​in a predetermined positive potential interval based on a plurality of second integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of a third cyclic voltammogram measured while changing the potential at a third potential scanning rate faster than the second potential scanning rate. A third sum (H(+)_sum) which is the sum of the third integral values ​​in a positive predetermined potential interval is calculated based on a plurality of third integral values ​​in a constant potential interval; a fourth sum (H(-)_sum) which is the sum of the third integral values ​​in a negative predetermined potential interval is calculated based on a plurality of third integral values ​​in a plurality of predetermined potential intervals; a fifth sum (L(all))_sum) which is the sum of the first integral values ​​in all predetermined potential intervals is calculated based on a plurality of first integral values ​​in a plurality of predetermined potential intervals; a sixth sum (M(all))_sum) which is the sum of the second integral values ​​in all predetermined potential intervals is calculated based on a plurality of second integral values ​​in a plurality of predetermined potential intervals; and a seventh sum (H(all))_sum) which is the sum of the third integral values ​​in all predetermined potential intervals is calculated based on a plurality of third integral values ​​in a plurality of predetermined potential intervals. The taste diagnosis unit diagnoses the taste of the first object to be analyzed based on the first sum (L(+)_sum), the second sum (M(+)_sum), the third sum (H(+)_sum), the fourth sum (H(-)_sum), the fifth sum (L(all))_sum), the sixth sum (M(all))_sum and the seventh sum (H(all))_sum).

[0012] (Configuration 2) In configuration 1, the first calculation unit further calculates a first factor (Body index (+)), which is a factor attributable to the diffusion coefficients of the components of the first analyte when a positive potential is applied to the first analyte, based on the first sum (L(+)_sum), the second sum (M(+)_sum), and the third sum (H(+)_sum), and calculates a second factor (Body index (all)), which is a factor attributable to the diffusion coefficients of the components of the first analyte when positive and negative potentials are applied to the first analyte, based on the fifth sum (L(all))_sum), the sixth sum (M(all))_sum), and the seventh sum (H(all))_sum). The taste diagnosis unit diagnoses the "astringency" of the first analyte based on the first factor (Body index (+)), diagnoses the "aftertaste" of the first analyte based on the second factor (Body index (all)), diagnoses the "sweetness" of the first analyte based on the third sum (H(+)_sum), diagnoses the "aroma" of the first analyte based on the fourth sum (H(-)_sum), and diagnoses the "bitterness" of the first analyte based on the "astringency" of the first analyte and the "sweetness" of the first analyte.

[0013] (Configuration 3) In configuration 2, the taste diagnostic unit adds a coefficient k １ The multiplication result is diagnosed as the "astringency" of the first analyte, and the coefficient k is applied to the second factor (Body index (all)). ２ The multiplication result is diagnosed as the "afterglow" of the first analysis object, and the third sum (H(+)_sum) is multiplied by the coefficient k ３ The result of division by the coefficient k ４ The multiplication result is diagnosed as the "sweetness" of the first analyte, and the fourth sum (H(-)_sum) is multiplied by the coefficient k ５ The result of division by the coefficient k ６ The multiplication result is diagnosed as the "aroma" of the first analysis object, and the "astringency" of the first analysis object is calculated by multiplying the coefficient k ７ The coefficient k is applied to the "sweetness" of the first analyte from the multiplication result. ８The subtraction result obtained by multiplying the above by 1 and subtracting the multiplied result is diagnosed as the "bitterness" of the first analyte.

[0014] (Configuration 4) In Configuration 3, the taste diagnostic unit performs a regression analysis using the first factor (Body index (+)) as an explanatory variable and "astringency" as a target variable to obtain a regression equation, and calculates a coefficient k １ The second factor (Body index (all)) is used as an explanatory variable, and a regression analysis is performed using "aftertaste" as a target variable to obtain a regression equation. In the obtained regression equation, the value multiplied by the second factor (Body index (all)), which is an explanatory variable, is determined as the coefficient k ２ The third sum (H(+)_sum) is used as an explanatory variable, and regression analysis is performed using "sweetness" as a response variable to determine a regression equation. In the regression equation thus determined, the value by which the explanatory variable (= the third sum (H(+)_sum)) is divided is determined as the coefficient k ３ The value multiplied by the explanatory variable (= the third sum (H(+)_sum)) is the coefficient k ４ The fourth sum (H(-)_sum) is used as an explanatory variable, and a regression analysis is performed using "aroma" as a response variable to determine a regression equation. In the regression equation thus determined, the value by which the explanatory variable (fourth sum (H(-)_sum)) is divided is determined as the coefficient k ５ The value multiplied by the explanatory variable (fourth sum (H(-)_sum)) is the coefficient k ６ The value is determined as a coefficient k. A regression equation is obtained by performing a regression analysis with "astringency" and "sweetness" as explanatory variables and "bitterness" as a target variable. ７ The value is multiplied by the "sweetness" coefficient k ８ is determined as the value of

[0015] (Configuration 5) In Configuration 3, when the “astringency”, “aftertaste”, “sweetness”, “aroma” and “bitterness” of v (v is an integer of 1 or more) first analytes are diagnosed, the coefficient k １ Value of coefficient k ８ and update the value of the updated coefficient k １ Value of coefficient k８ The values ​​of "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" of the first analyte are evaluated.

[0016] (Configuration 6) In configuration 1, the plurality of first integral values ​​are n １ １ (n １ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point is not zero, and then adding "1" to the integer. １ ２ (n １ ２ <n １ １ ) first integral values, n １ ３ (n １ ３ <n １ ２ ) first integral values, ..., and n １ ｂ (n １ ｂ <n １ ｂ－１ , b is an integer equal to or greater than 2) is any one of the first integral values, and the plurality of second integral values ​​are n ２ １ (n ２ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point is not zero, and then adding "1" to the integer. ２ ２ (n ２ ２ <n ２ １ ) second integral values, n ２ ３ (n ２ ３ <n ２ ２ ) second integral values, ..., and n２ ｂ (n ２ ｂ <n ２ ｂ－１ , b is an integer equal to or greater than 2), and the plurality of third integral values ​​are n ３ １ (n ３ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point is not zero, and then adding "1" to the integer. ３ ２ (n ３ ２ <n ３ １ ) third integral values, n ３ ３ (n ３ ３ <n ３ ２ ) third integral values, ..., and n ３ ｂ (n ３ ｂ <n ３ｂ－１ , b is an integer equal to or greater than 2) third integral values.

[0017] (Configuration 7) In Configuration 1, the first arithmetic unit further calculates an eighth sum (L(-)_sum_th) which is a sum of first integral values ​​in a plurality of negative predetermined potential intervals consisting of negative potentials equal to or less than a threshold value, based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the first cyclic voltammogram, and calculates a ninth sum (M(-)_sum_th) which is a sum of second integral values ​​in a plurality of negative predetermined potential intervals consisting of negative potentials equal to or less than a threshold value, based on a plurality of second integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the second cyclic voltammogram, A tenth sum (H(-)_sum_th) is calculated, which is the sum of the third integral values ​​in a plurality of negative predetermined potential intervals consisting of negative potentials equal to or less than a threshold, based on the plurality of third integral values ​​in a plurality of negative predetermined potential intervals calculated using the current-potential characteristics of the third cyclic voltammogram, and a third factor (Body index (-)_th) is calculated, which is a factor attributable to the diffusion coefficient of a component of the second analyte when a negative potential equal to or less than a threshold is applied to the second analyte, based on the eighth sum (L(-)_sum_th), the ninth sum (M(-)_sum_th), and the tenth sum (H(-)_sum_th). The taste diagnosis unit further diagnoses the "astringency" of the second analyte based on the third factor (Body index (-)_th).

[0018] (Configuration 8) In configuration 7, the taste diagnostic unit adds a coefficient k to the third factor (Body index (-)_th). ９ The result of the multiplication is diagnosed as the "astringency" of the second analysis object.

[0019] (Configuration 9) In Configuration 8, the taste diagnostic unit performs a regression analysis using the third factor (Body index (-)_th) as an explanatory variable and “astringency” as a response variable to obtain a regression equation, and in the obtained regression equation, a value multiplied by the third factor (Body index (-)_th) is used as a coefficient k ９ is determined as the value of

[0020] (Configuration 10) In Configuration 8, the taste diagnosis unit diagnoses the "astringency" of v (v is an integer of 1 or more) second analytes, and calculates a coefficient k ９ and update the value of the updated coefficient k ９ The value of is used to diagnose the "astringency" of the second analyte.

[0021] (Configuration 11) In configuration 7, the sum of the first integral values ​​in a plurality of predetermined negative potential sections consisting of negative potentials equal to or less than a threshold value is w １ １ (w １ １ is the sum of the first integral values, w １ ２ (w １ ２ <w １ １ ) sum of the first integral values, w １ ３ (w １ ３ <w １ ２ ) sum of the first integral values, ..., and w １ ｂ (w １ ｂ <w １ ｂ－１ , b is an integer equal to or greater than 2), and the sum of the second integral values ​​in a plurality of negative predetermined potential sections consisting of negative potentials equal to or less than the threshold is w ２ １ (w ２ １ is the sum of the number of second integral values, w ２ ２ (w ２ ２ <w ２ １) sum of the second integral values, w ２ ３ (w ２ ３ <w ２ ２ ) sum of the second integral values, ..., and w ２ ｂ (w ２ ｂ <w ２ ｂ－１ , b is an integer equal to or greater than 2), and the sum of the third integral values ​​in a plurality of predetermined negative potential sections consisting of negative potentials equal to or less than the threshold is w ３ １ (w ３ １ is the sum of the third integral values, w ３ ２ (w ３ ２ <w ３ １ ) the sum of the third integral values, w ３ ３ (w ３ ３ <w ３ ２ ) sum of the third integral values, ..., and w ３ ｂ (w ３ ｂ <w ３ ｂ－１ , b is an integer equal to or greater than 2) of the third integral values.

[0022] (Configuration 12) In Configuration 1, the diagnostic device further includes a second arithmetic unit that calculates a plurality of first integral values ​​in a plurality of predetermined potential intervals based on the current-potential characteristics of the first cyclic voltammogram, calculates a plurality of second integral values ​​in a plurality of predetermined potential intervals based on the current-potential characteristics of the second cyclic voltammogram, and calculates a plurality of third integral values ​​in a plurality of predetermined potential intervals based on the current-potential characteristics of the third cyclic voltammogram. The first calculation unit calculates a first sum (L(+)_sum) and a fifth sum (L(all))_sum) based on the plurality of first integral values ​​calculated by the second calculation unit, calculates a second sum (M(+)_sum) and a sixth sum (M(all))_sum) based on the plurality of second integral values ​​calculated by the second calculation unit, and calculates a third sum (H(+)_sum), a fourth sum (H(-)_sum) and a seventh sum (H(all))_sum) based on the plurality of third integral values ​​calculated by the second calculation unit.

[0023] (Configuration 13) In configuration 12, the diagnostic device further includes a creating unit. The creating unit creates, as a feature of the first analysis object or the second analysis object, a curve indicating the dependence of the plurality of summed integral values ​​on the plurality of predetermined potential sections based on the plurality of predetermined potential sections and the plurality of summed integral values ​​respectively associated with the plurality of predetermined potential sections. The second calculation unit further calculates a summed integral value that is the sum of the first integral value, the second integral value, and the third integral value in one predetermined potential section for all of the plurality of predetermined potential sections to calculate the plurality of summed integral values, and outputs the plurality of predetermined potential sections and the plurality of summed integral values ​​respectively associated with the plurality of predetermined potential sections to the creating unit.

[0024] (Configuration 14) In configuration 13, the calculation data includes a plurality of predetermined potential sections and a plurality of total integral values ​​respectively corresponding to the plurality of predetermined potential sections. The determination unit determines whether P (P is an integer equal to or greater than 2) pieces of calculation data include P [multiple total integral values] that are different from each other. The creation unit creates P curves when it determines that the P [multiple total integral values] are different from each other.

[0025] (Configuration 15) According to another embodiment of the present invention, a diagnostic system includes the diagnostic device according to any one of configurations 1 to 14.

[0026] (Configuration 16) Furthermore, according to an embodiment of the present invention, the program includes a first arithmetic unit that calculates a first sum (L(+)_sum) which is a sum of first integral values ​​in a predetermined positive potential interval based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of a first cyclic voltammogram measured while changing the potential at a first potential scan rate; calculates a second sum (M(+)_sum) which is a sum of second integral values ​​in a predetermined positive potential interval based on a plurality of second integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of a second cyclic voltammogram measured while changing the potential at a second potential scan rate that is faster than the first potential scan rate; and calculates a second sum (M(+)_sum) which is a sum of second integral values ​​in a predetermined positive potential interval based on a plurality of second integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of a third cyclic voltammogram measured while changing the potential at a third potential scan rate that is faster than the second potential scan rate. a first step of calculating a third sum (H(+)_sum) which is the sum of the third integral values ​​in a positive predetermined potential section based on a plurality of third integral values ​​in the plurality of predetermined potential sections, a fourth sum (H(-)_sum) which is the sum of the third integral values ​​in a negative predetermined potential section based on a plurality of third integral values ​​in the plurality of predetermined potential sections, a fifth sum (L(all))_sum) which is the sum of the first integral values ​​in all predetermined potential sections based on a plurality of first integral values ​​in the plurality of predetermined potential sections, a sixth sum (M(all))_sum) which is the sum of the second integral values ​​in all predetermined potential sections based on a plurality of second integral values ​​in the plurality of predetermined potential sections, and a seventh sum (H(all))_sum) which is the sum of the third integral values ​​in all predetermined potential sections based on a plurality of third integral values ​​in the plurality of predetermined potential sections; and a second step in which the taste diagnosis unit diagnoses the taste of the first object to be analyzed based on the first sum (L(+)_sum), the second sum (M(+)_sum), the third sum (H(+)_sum), the fourth sum (H(-)_sum), the fifth sum (L(all))_sum), the sixth sum (M(all))_sum, and the seventh sum (H(all))_sum).

[0027] (Configuration 17) In configuration 16, in the first step, the first calculation unit further calculates a first factor (Body index (+)), which is a factor attributable to the diffusion coefficients of the components of the first analyte when a positive potential is applied to the first analyte, based on the first sum (L(+)_sum), the second sum (M(+)_sum), and the third sum (H(+)_sum), and calculates a second factor (Body index (all)), which is a factor attributable to the diffusion coefficients of the components of the first analyte when positive and negative potentials are applied to the first analyte, based on the fifth sum (L(all))_sum), the sixth sum (M(all))_sum), and the seventh sum (H(all))_sum). In the second step, the taste diagnosis unit diagnoses the "astringency" of the first analyte based on the first factor (Body index (+)), diagnoses the "aftertaste" of the first analyte based on the second factor (Body index (all)), diagnoses the "sweetness" of the first analyte based on the third sum (H(+)_sum), diagnoses the "aroma" of the first analyte based on the fourth sum (H(-)_sum), and diagnoses the "bitterness" of the first analyte based on the "astringency" of the first analyte and the "sweetness" of the first analyte.

[0028] (Configuration 18) In configuration 17, the taste diagnostic unit, in the second step, adds a coefficient k １ The multiplication result is diagnosed as the "astringency" of the first analyte, and the coefficient k is applied to the second factor (Body index (all)). ２ The multiplication result is diagnosed as the "afterglow" of the first analysis object, and the third sum (H(+)_sum) is multiplied by the coefficient k ３ The result of division by the coefficient k ４ The multiplication result is diagnosed as the "sweetness" of the first analysis object, and the fourth sum (H(-)_sum) is multiplied by the coefficient k ５ The result of division by the coefficient k ６ The multiplication result is diagnosed as the "aroma" of the first analysis object, and the "astringency" of the first analysis object is determined by the coefficient k ７The coefficient k is applied to the "sweetness" of the first analyte from the multiplication result. ８ The subtraction result obtained by multiplying the above by 1 and subtracting the multiplied result is diagnosed as the "bitterness" of the first analyte.

[0029] (Configuration 19) In configuration 18, in the second step, the taste diagnostic unit performs a regression analysis using the first factor (Body index (+)) as an explanatory variable and "astringency" as a response variable to obtain a regression equation, and defines a value obtained by multiplying the first factor (Body index (+)) in the obtained regression equation as a coefficient k １ The second factor (Body index (all)) is used as an explanatory variable, and a regression analysis is performed using "aftertaste" as a target variable to obtain a regression equation. In the obtained regression equation, the value multiplied by the second factor (Body index (all)), which is an explanatory variable, is determined as the coefficient k ２ The third sum (H(+)_sum) is used as an explanatory variable, and regression analysis is performed using "sweetness" as a response variable to determine a regression equation. In the regression equation thus determined, the value by which the explanatory variable (= the third sum (H(+)_sum)) is divided is determined as the coefficient k ３ The value multiplied by the explanatory variable (= the third sum (H(+)_sum)) is the coefficient k ４ The fourth sum (H(-)_sum) is used as an explanatory variable, and a regression analysis is performed using "aroma" as a response variable to determine a regression equation. In the regression equation thus determined, the value by which the explanatory variable (fourth sum (H(-)_sum)) is divided is determined as the coefficient k ５ The value multiplied by the explanatory variable (fourth sum (H(-)_sum)) is the coefficient k ６ The value is determined as a coefficient k. A regression equation is obtained by performing a regression analysis with "astringency" and "sweetness" as explanatory variables and "bitterness" as a target variable. ７ The value is multiplied by the "sweetness" coefficient k ８ is determined as the value of

[0030] (Configuration 20) In configuration 18, in the second step, the taste diagnosis unit diagnoses the “astringency”, “aftertaste”, “sweetness”, “aroma” and “bitterness” of v (v is an integer of 1 or more) first analytes, and calculates a coefficient k １ Value of coefficient k ８ and update the value of the updated coefficient k １ Value of coefficient k ８ The values ​​of "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" of the first analyte are evaluated.

[0031] (Configuration 21) In configuration 16, the plurality of first integral values ​​are n １ １ (n １ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point is not zero, and then adding "1" to the integer. １ ２ (n １ ２ <n １ １ ) first integral values, n １ ３ (n １ ３ <n １ ２ ) first integral values, ..., and n １ ｂ (n １ ｂ <n １ ｂ－１ , b is an integer equal to or greater than 2) is any one of the first integral values, and the plurality of second integral values ​​are n ２ １ (n ２ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point is not zero, and then adding "1" to the integer. ２ ２ (n２ ２ <n ２ １ ) second integral values, n ２ ３ (n ２ ３ <n ２ ２ ) second integral values, ..., and n ２ ｂ (n ２ ｂ <n ２ ｂ－１ , b is an integer equal to or greater than 2), and the plurality of third integral values ​​are n ３ １ (n ３ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point is not zero, and then adding "1" to the integer. ３ ２ (n ３ ２ <n ３ １ ) third integral values, n ３ ３ (n ３ ３ <n ３ ２ ) third integral values, ..., and n ３ ｂ (n ３ ｂ <n ３ ｂ－１ , b is an integer equal to or greater than 2) third integral values.

[0032] (Configuration 22) In configuration 16, in the first step, the first arithmetic unit further calculates an eighth sum (L(-)_sum_th) which is a sum of first integral values ​​in a plurality of negative predetermined potential intervals consisting of negative potentials equal to or less than a threshold value, based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the first cyclic voltammogram, and a ninth sum (M(-)_sum_th) which is a sum of second integral values ​​in a plurality of negative predetermined potential intervals consisting of negative potentials equal to or less than a threshold value, based on a plurality of second integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the second cyclic voltammogram. a tenth sum (H(-)_sum_th) which is the sum of the third integral values ​​in a plurality of predetermined potential intervals consisting of negative potentials equal to or less than a threshold value plus the third integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the third cyclic voltammogram; and a third factor (Body index (-)_th) which is a factor attributable to the diffusion coefficient of a component of the second analyte when a negative potential equal to or less than a threshold value is applied to the second analyte, based on the eighth sum (L(-)_sum_th), the ninth sum (M(-)_sum_th) and the tenth sum (H(-)_sum_th). In the second step, the taste diagnosis unit further diagnoses the "astringency" of the second analyte based on the third factor (Body index (-)_th).

[0033] (Configuration 23) In configuration 22, the taste diagnosis unit, in the second step, adds a coefficient k ９ The result of the multiplication is diagnosed as the "astringency" of the second analysis object.

[0034] (Configuration 24) In configuration 23, in the second step, the taste diagnosis unit performs a regression analysis using the third factor (Body index (-)_th) as an explanatory variable and "astringency" as a response variable to obtain a regression equation, and in the obtained regression equation, a value multiplied by the third factor (Body index (-)_th) is defined as a coefficient k ９ is determined as the value of

[0035] (Configuration 25) In Configuration 23, the taste diagnosis unit diagnoses the "astringency" of v (v is an integer of 1 or more) second analysis objects in the second step, and calculates a coefficient k ９ and update the value of the updated coefficient k ９ The value of is used to diagnose the "astringency" of the second analyte.

[0036] (Configuration 26) In configuration 22, the sum of the first integral values ​​in a plurality of predetermined negative potential sections consisting of negative potentials equal to or less than a threshold value is w １ １ (w １ １ is the sum of the first integral values, w １ ２ (w １ ２ <w １ １ ) sum of the first integral values, w １ ３ (w １ ３ <w １ ２ ) sum of the first integral values, ..., and w １ ｂ (w １ ｂ <w １ ｂ－１ , b is an integer equal to or greater than 2), and the sum of the second integral values ​​in a plurality of negative predetermined potential sections consisting of negative potentials equal to or less than the threshold is w ２ １ (w ２ １ is the sum of the number of second integral values, w ２ ２ (w ２ ２<w ２ １ ) sum of the second integral values, w ２ ３ (w ２ ３ <w ２ ２ ) sum of the second integral values, ..., and w ２ ｂ (w ２ ｂ <w ２ ｂ－１ , b is an integer equal to or greater than 2), and the sum of the third integral values ​​in a plurality of predetermined negative potential sections consisting of negative potentials equal to or less than the threshold is w ３ １ (w ３ １ is the sum of the third integral values, w ３ ２ (w ３ ２ <w ３ １ ) the sum of the third integral values, w ３ ３ (w ３ ３ <w ３ ２ ) sum of the third integral values, ..., and w ３ ｂ (w ３ ｂ <w ３ ｂ－１ , b is an integer equal to or greater than 2) of the third integral values.

[0037] (Configuration 27) In Configuration 16, the second arithmetic unit further causes the computer to execute a third step of calculating a plurality of first integral values ​​in a plurality of predetermined potential intervals based on the current-potential characteristics of the first cyclic voltammogram, calculating a plurality of second integral values ​​in a plurality of predetermined potential intervals based on the current-potential characteristics of the second cyclic voltammogram, and calculating a plurality of third integral values ​​in a plurality of predetermined potential intervals based on the current-potential characteristics of the third cyclic voltammogram; In the first step, the first arithmetic unit calculates a first sum (L(+)_sum) and a fifth sum (L(all))_sum) based on the plurality of first integral values ​​calculated by the second arithmetic unit, calculates a second sum (M(+)_sum) and a sixth sum (M(all))_sum) based on the plurality of second integral values ​​calculated by the second arithmetic unit, and calculates a third sum (H(+)_sum), a fourth sum (H(-)_sum) and a seventh sum (H(all))_sum) based on the plurality of third integral values ​​calculated by the second arithmetic unit.

[0038] (Configuration 28) In configuration 27, the creation unit further causes the computer to execute a fourth step of creating, as a feature of the first analysis object or the second analysis object, a curve showing the dependence of the multiple sum integral values ​​on multiple predetermined potential intervals based on the multiple predetermined potential intervals and the multiple sum integral values ​​respectively associated with the multiple predetermined potential intervals, and the second calculation unit further calculates, in the third step, a sum integral value that is the sum of the first integral value, the second integral value, and the third integral value in one predetermined potential interval for all of the multiple predetermined potential intervals to calculate the multiple sum integral values, and outputs the multiple predetermined potential intervals and the multiple sum integral values ​​respectively associated with the multiple predetermined potential intervals to the creation unit.

[0039] (Configuration 29) In configuration 28, the calculation data includes a plurality of predetermined potential intervals and a plurality of sum integral values ​​respectively corresponding to the plurality of predetermined potential intervals, the judgment unit further causes the computer to execute a fifth step of judging whether P (P is an integer equal to or greater than 2) pieces of calculation data [plural sum integral values] differ from each other, and the creation unit creates P curves in the fourth step when the judgment unit judges in the fifth step that the P [plural sum integral values] differ from each other.

[0040] According to the embodiment of the present invention, the diagnostic device can diagnose the taste of alcoholic beverages and the like based on a cyclic voltammogram of the alcoholic beverages and the like.

[0041] 7 is a schematic diagram of a diagnostic system according to a first embodiment of the present invention. It is a schematic diagram of a sensor device 1 shown in FIG. 1. It is a perspective view of a measuring instrument 12 shown in FIG. 2. It is a schematic diagram of a measuring instrument 12 shown in FIG. 2. It is a schematic diagram showing a timing chart of a potential supplied to a sensor 11 shown in FIG. 2. It is a schematic diagram of measurement data MRS. It is a schematic diagram of a diagnostic device 2 shown in FIG. 1. It is a schematic diagram of an analysis / diagnosis unit 21 shown in FIG. 7. It is a first conceptual diagram for explaining a method for calculating an integral value. It is a second conceptual diagram for explaining a method for calculating an integral value. It is a diagram showing a part of a cyclic voltammogram. It is a diagram for explaining a method for creating a curve CUR showing the relationship between a plurality of classes Cls and a plurality of integral values ​​ITG ... ｒ＿Ｌｏｗ , V ｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ15. A conceptual diagram showing the class dependency of integrals created based on cyclic voltammograms measured while changing the temperature. A conceptual diagram of training data. A conceptual diagram of a correspondence table showing the correspondence between coefficients and training data. A conceptual diagram of updated training data. A conceptual diagram of a correspondence table TBL1_up1 obtained by updating the correspondence table TBL1 shown in FIG. 15. A conceptual diagram for diagnosing the taste (astringency, aftertaste, sweetness, aroma, bitterness) of shochu. A diagram showing integral spectra for shochu samples No. 1 to No. 3 shown in Table 2. A diagram showing integral spectra for shochu samples No. 4 to No. 6 shown in Table 2. A diagram showing integral spectra for shochu samples No. 7 to No. 9 shown in Table 2. A diagram showing integral spectra for shochu samples No. 10 to No. 12 shown in Table 2. A diagram showing integral spectra for shochu samples No. 12 to No. 14 shown in Table 2. A diagram showing integral spectra for shochu samples No. 15 and No. 16 shown in Table 2. 29 is a diagram showing the integrated spectrum for shochu No. 16. It is a diagram showing the integrated spectrum for shochu samples No. 17 to No. 20 shown in Table 2. It is a first diagram showing the results of determining whether or not curves k1 to k20 shown in FIGS. 19 to 25 are different from one another. It is a second diagram showing the results of determining whether or not curves k1 to k20 shown in FIGS. 19 to 25 are different from one another. It is a third diagram showing the results of determining whether or not curves k1 to k20 shown in FIGS. 19 to 25 are different from one another. It is a diagram showing the results of diagnosing "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" for shochu No. 1 to No. 20 shown in Table 2. It is a diagram showing other diagnostic results of diagnosing "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" for shochu No. 1 to No. 20 shown in Table 2. It is a diagram showing the results of diagnosing "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" for shochu No. 1 to No. 20 shown in Table 2. 30. FIG. 31 is a diagram showing the differences between the diagnostic result values ​​for Shochu No. 20 and the diagnostic result values ​​for Shochu No. 1 to No. 20 shown in FIG. 30. FIG. 32 is a diagram showing the integral value spectra for Crimson Seedless (skin + body), Green Seedless (skin + body), and Shine Muscat (skin + body). FIG. 33 is a diagram showing the integral value spectra for Crimson Seedless (body), Green Seedless (body), and Shine Muscat (body).32 and 33. This figure shows the results of determining whether the curves k21 to k26 shown in FIGS. 32 and 33 are different from one another. This figure shows the results of the diagnosis of "astringency" for Green Seedless (flesh only), Crimson Seedless (flesh only), Shine Muscat (flesh only), Green Seedless (flesh + skin), Crimson Seedless (flesh + skin), and Shine Muscat (flesh + skin). This figure shows the integral spectra for shochu samples No. 1 to No. 3 shown in Table 2 when the number of integrals is 138. This figure shows the integral spectra for shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 138. This figure shows the integral spectra for shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 138. This figure shows the integral spectra for shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 138. 36 to 42 . This figure shows integral spectra for shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 138. This figure shows integral spectra for shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 138. This figure shows integral spectra for shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 138. This figure is a first diagram showing the results of determining whether or not curves k27 to k46 shown in FIGS. 36 to 42 differ from one another. This figure is a second diagram showing the results of determining whether or not curves k27 to k46 shown in FIGS. 36 to 42 differ from one another. This figure is a third diagram showing the results of determining whether or not curves k27 to k46 shown in FIGS. 36 to 42 differ from one another. This figure shows integral spectra for shochu samples No. 1 to No. 3 shown in Table 2 when the number of integrals is 70. 1 shows the integral spectra of shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 70; 2 shows the integral spectra of shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 70; and 3 shows the integral spectra of shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 70.46 to 52 。 This figure shows the integral spectra of shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 70. This figure shows the integral spectra of shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 70. This figure shows the integral spectra of shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 70. This figure shows the first diagram showing the results of determining whether or not curves k47 to k66 shown in Figures 46 to 52 are different from each other. This figure shows the second diagram showing the results of determining whether or not curves k47 to k66 shown in Figures 46 to 52 are different from each other. This figure shows the third diagram showing the results of determining whether or not curves k47 to k66 shown in Figures 46 to 52 are different from each other. This figure shows the integral spectra of shochu samples No. 1 to No. 3 shown in Table 2 when the number of integrals is 36. 1 shows integral spectra for shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 36; 2 shows integral spectra for shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 36; 3 shows integral spectra for shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 36; 4 shows integral spectra for shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 36; 5 shows integral spectra for shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 36; 6 shows integral spectra for shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 36. 62. A first diagram showing the results of determining whether or not the curves k67 to k86 shown in Figures 56 to 62 differ from one another. A second diagram showing the results of determining whether or not the curves k67 to k86 shown in Figures 56 to 62 differ from one another. A third diagram showing the results of determining whether or not the curves k67 to k86 shown in Figures 56 to 62 differ from one another. A diagram showing the integral value spectra for shochu samples No. 1 to No. 3 shown in Table 2 when the number of integral values ​​is 18.1 shows integral spectra for shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 18; 2 shows integral spectra for shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 18; 3 shows integral spectra for shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 18; 4 shows integral spectra for shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 18; 5 shows integral spectra for shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 18; 6 shows integral spectra for shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 18. 72。 This figure shows the first diagram showing the results of determining whether or not the curves k87 to k106 shown in Figures 66 to 72 are different from one another. This figure shows the second diagram showing the results of determining whether or not the curves k87 to k106 shown in Figures 66 to 72 are different from one another. This figure shows the third diagram showing the results of determining whether or not the curves k87 to k106 shown in Figures 66 to 72 are different from one another. This figure shows the correspondence between the classes and integrals when the number of integrals is 138 and the classes and integrals when the number of integrals is 276. This figure shows the integral spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integrals in a predetermined potential range of -1362 mV to -2501 mV is 74. 78 is a diagram showing the integral spectra of Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only) when the number of integrals in a predetermined potential section in the range of −1362 mV to −2501 mV is 74. It is a diagram showing the results of determining whether or not the curves k107 to k112 shown in FIGS. 77 and 78 are different from each other. It is a diagram showing the integral spectra of Crimson Seedless (skin+flesh), Green Seedless (skin+flesh), and Shine Muscat (skin+flesh) when the number of integrals in a predetermined potential section in the range of −1362 mV to −2501 mV is 37.80 and 81. This figure shows the integral spectra of Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only) when the number of integrals in a predetermined potential section ranging from -1362 mV to -2501 mV is 37. This figure shows the results of determining whether or not curves k113 to k118 shown in FIGS. 80 and 81 are different from each other. This figure shows the integral spectra of Crimson Seedless (skin+flesh), Green Seedless (skin+flesh), and Shine Muscat (skin+flesh) when the number of integrals in a predetermined potential section ranging from -1362 mV to -2501 mV is 19. This figure shows the integral spectra of Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only) when the number of integrals in a predetermined potential section ranging from -1362 mV to -2501 mV is 19. 90. This figure shows the results of determining whether or not the curves k119 to k124 shown in FIGS. 83 and 84 are different from one another. This figure shows the integral spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integrals in a predetermined potential section ranging from -1362 mV to -2501 mV is 9. This figure shows the integral spectra of Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only) when the number of integrals in a predetermined potential section ranging from -1362 mV to -2501 mV is 9. This figure shows the results of determining whether or not the curves k125 to k130 shown in FIGS. 86 and 87 are different from one another. This figure shows the correspondence between the class and integral when the number of integrals is 37 and the class and integral when the number of integrals is 74. This figure is a flowchart for explaining the operation of the diagnostic system 10 shown in FIG. 1. This figure is a flowchart for explaining the detailed operation of step S7 of FIG. 90. Fig. 92 is a flowchart for explaining detailed operations of step S73 in Fig. 91. Fig. 92 is a flowchart for explaining detailed operations of step S72 in Fig. 91. Fig. 92 is a flowchart for explaining detailed operations of step S75 in Fig. 91. Fig. 92 is a flowchart for explaining detailed operations of step S76 in Fig. 91. Fig. 92 is a flowchart for explaining detailed operations of step S77 in Fig. 91.97. A flowchart for explaining the detailed operation of step S8 of FIG. 90. A flowchart for explaining the detailed operation of step S82 of FIG. 97. A flowchart for explaining the detailed operation of step S830 of FIG. 98. Another flowchart for explaining the detailed operation of step S830 of FIG. 98. A flowchart for explaining the detailed operation of step S83 of FIG. 97. Another flowchart for explaining the detailed operation of step S83 of FIG. 97. A diagram showing the correspondence relationship of classes in a predetermined positive potential section when the number of integral values ​​when the analysis object is "shochu" is 18, 20, 32, 36, 56, 70, 92, 138, and 276. A diagram showing the correspondence relationship of classes in a predetermined negative potential section when the number of integral values ​​when the analysis object is "shochu" is 18, 20, 32, 36, 56, 70, 92, 138, and 276. FIG. 10 is a diagram showing the correspondence between classes when the number of integral values ​​when the object to be analyzed is "grapes" is 7, 9, 11, 13, 15, 19, 25, 37, and 74. Ｐ C ２ A set of two curves CUR ｉ , C.U.R. ｊ 91 is a conceptual diagram showing a determination result indicating whether the analysis data ALY_D in step S74 of FIG. ｕｎｉ From index data IDX ｕｎｉ 91. It is a conceptual diagram showing the update of the P pieces of analysis data ALY_D in step S78 of FIG. １ ~ ALY_D Ｐ P index data IDX １ ~IDX Ｐ117. A conceptual diagram showing an update to the diagnostic system shown in FIG. 117. A conceptual diagram showing a diagnosis result of taste. A schematic diagram of a diagnostic system according to embodiment 2. A schematic diagram of the terminal device 3 shown in FIG. 110. A schematic diagram of the diagnostic device 2A shown in FIG. 110. A schematic diagram of the analysis / diagnosis unit 21A shown in FIG. 112. A first flowchart for explaining the operation of the diagnostic system 10A shown in FIG. 110. A second flowchart for explaining the operation of the diagnostic system 10A shown in FIG. 110. A diagram for explaining the arrangement locations of the sensor device 1, the terminal device 3, and the diagnostic device 2A. A schematic diagram of a diagnostic system according to embodiment 3. A schematic diagram of the terminal device 3A shown in FIG. 117. A schematic diagram of the analysis device 2C shown in FIG. 117. A schematic diagram of the diagnostic device 2B shown in FIG. 117. A first flowchart for explaining the operation of the diagnostic system 10B shown in FIG. 117. A second flowchart for explaining the operation of the diagnostic system 10B shown in FIG. 117. A diagram for explaining the arrangement locations of the sensor device 1, the terminal device 3A, the analysis device 2C, and the diagnostic device 2B. FIG. 124 is another schematic diagram of the measuring instrument 12 shown in FIG. 4. FIG. 125 is another schematic diagram of the diagnostic device 2 shown in FIG. 7. FIG. 126 is a schematic diagram of the analysis / diagnosis circuit 21B shown in FIG. 125. FIG. 127 is another schematic diagram of the terminal device 3 shown in FIG. 111. FIG. 128 is another schematic diagram of the analysis / diagnosis unit 21A shown in FIG. 113. FIG. 129 is another schematic diagram of the terminal device 3A shown in FIG. 118. FIG. 130 is another schematic diagram of the analysis device 2C shown in FIG. 131. FIG. 132 is another schematic diagram of the diagnostic device 2B shown in FIG. 132.

[0042] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals and description thereof will not be repeated.

[0043] 1 is a schematic diagram of a diagnostic system according to a first embodiment of the present invention. Referring to FIG. 1, a diagnostic system 10 according to the first embodiment of the present invention includes a sensor device 1 and a diagnostic device 2.

[0044] The diagnostic system 10 is installed in, for example, restaurants such as Japanese restaurants, Chinese restaurants, and Western restaurants, sake breweries that brew shochu, and liquor stores that sell shochu.

[0045] The sensor device 1 measures the measurement data of the cyclic voltammogram of an object to be analyzed, for example, shochu or grapes (grape juice), using the cyclic voltammetry method, and transmits the measurement data of the measured cyclic voltammogram CVG to the diagnostic device 2 via wireless communication or wired communication.

[0046] The cyclic voltammetry (CV) method is a measurement method in which electrodes are placed in a static solution, the current that flows when the potential is repeatedly swept, and the resulting current-potential curve (cyclic voltammogram CVG) is analyzed to examine redox properties, etc.

[0047] The cyclic voltammogram CVG is a current-potential curve measured by cyclic voltammetry, and the measurement data of the cyclic voltammogram CVG includes current-potential characteristics (IV) that associate the current I with the potential V.

[0048] When transmitting the measurement data of the cyclic voltammogram CVG to the diagnostic device 2 by wireless communication, the sensor device 1 transmits the measurement data of the cyclic voltammogram CVG to the diagnostic device 2 by wireless communication, for example, via Bluetooth (registered trademark).

[0049] Furthermore, when transmitting measurement data of the cyclic voltammogram CVG to the diagnostic device 2 by wired communication, the sensor device 1 is connected to the diagnostic device 2 by a cable and transmits the measurement data to the diagnostic device 2 via the cable.

[0050] The diagnostic device 2 receives the measurement data of the cyclic voltammogram CVG by wireless communication or wired communication from the sensor device 1. Then, the diagnostic device 2 calculates, for all predetermined potential intervals, integral values ​​of the current-potential characteristics (IV) included in the measurement data based on the measurement data of the cyclic voltammogram CVG, using a method described below, to calculate a plurality of integral values ​​in the plurality of predetermined potential intervals, creates a curve CUR indicating the dependency of the integral value on the predetermined potential interval based on the calculated plurality of integral values ​​in the plurality of predetermined potential intervals as an [index curve that serves as an index when identifying the object to be analyzed], and diagnoses the taste of shochu or grapes (grape juice) based on the plurality of integral values ​​in the plurality of predetermined potential intervals, and displays the diagnosis result.

[0051] Fig. 2 is a schematic diagram of the sensor device 1 shown in Fig. 1. Fig. 3 is a perspective view of the measuring instrument 12 shown in Fig. 2.

[0052] 2, the sensor device 1 includes a sensor 11 and a measuring instrument 12. The sensor 11 includes a substrate 111, a working electrode 112, a counter electrode 113, a reference electrode 114, and wires 115-117.

[0053] 2, an xy plane is defined. The substrate 111 has, for example, a flat plate shape and is disposed along the xy plane.

[0054] The wirings 115 to 117 are arranged along the x-axis direction (first direction) on the upper surface of the substrate 111. The wiring 116 is arranged along the x-axis direction (first direction) at a predetermined interval (e.g., 2 to 3 mm) from the wiring 115 in the y-axis direction (second direction orthogonal to the first direction). The wiring 117 is arranged along the x-axis direction (first direction) at a predetermined interval (e.g., 2 to 3 mm) from the wiring 116 in the y-axis direction (second direction orthogonal to the first direction).

[0055] The working electrode 112 is disposed on one end of the wiring 115 opposite the measuring instrument 12 side and is electrically connected to the wiring 115. The counter electrode 113 is disposed on one end of the wiring 116 opposite the measuring instrument 12 side and is electrically connected to the wiring 116. The reference electrode 114 is disposed on one end of the wiring 117 opposite the measuring instrument 12 side and is electrically connected to the wiring 117.

[0056] The substrate 111 is made of, for example, a printed circuit board (PCB), a plastic plate, or a glass epoxy substrate, and has, for example, a width of 12 mm, a length of 80 mm, and a thickness of 1 mm. The working electrode 112 is made of, for example, boron (B)-doped diamond (BDD), a carbon electrode, glassy carbon (glassy diamond), gold (Au), or platinum (Pt). The counter electrode 113 is made of, for example, gold (Au). The reference electrode 114 is made of, for example, gold (Au) or Ag / AgCl.

[0057] When the working electrode 12 is made of diamond, the diamond may be a single crystal diamond or a polycrystalline diamond, but polycrystalline diamond is preferred. In this case, it is more preferable that the dangling bonds on the outermost surface of the polycrystalline diamond are terminated with hydrogen.

[0058] The working electrode 112 is, for example, 3×3 mm ２ The counter electrode 113 has a rectangular planar shape with an area of, for example, 3×3 mm ２ The reference electrode 114 has a rectangular planar shape with an area of, for example, 1×2 mm ２ It has a rectangular planar shape with an area of ​​.

[0059] When the working electrode 112 is made of diamond or gold, the working electrode 112 has a circular planar shape with a diameter of 3.5 mm, for example.

[0060] Furthermore, when the working electrode 112 is made of glassy carbon, the cyclic voltammogram CVG can be measured over a wide range.

[0061] The working electrode 112 is an electrode that exchanges electrons with the analyte. The counter electrode 113 is an electrode that returns to the system a current value that is the same as the current value generated at the working electrode 112. The reference electrode 114 is an electrode that serves as a reference when determining the potential of the working electrode 112.

[0062] An analysis object consisting of shochu or grapes (grape juice) is supplied to a region where a working electrode 112, a counter electrode 113, and a reference electrode 114 are arranged.

[0063] Referring to FIG. 3, measuring device 12 has a recess 121A into which a portion of the other end of sensor 11 is inserted.

[0064] When the sensor 11 is electrically connected to the measuring instrument 12, a portion of the other end of the sensor 11 in the x-axis direction (first direction) is inserted into the recess 121A of the measuring instrument 12. This electrically connects the wiring 115 to 117 of the sensor 11 to the measuring instrument 12. When the sensor 11 is not electrically connected to the measuring instrument 12, a portion of the other end of the sensor 11 in the x-axis direction (first direction) is pulled out of the recess 121A of the measuring instrument 12.

[0065] Therefore, by attaching or detaching a portion of the other end of the sensor 11 in the x-axis direction (first direction) to or from the recess 121A of the measuring instrument 12, the sensor 11 can be electrically connected to or electrically disconnected from the measuring instrument 12.

[0066] In this embodiment of the present invention, the sensor 11 is used to measure the cyclic voltammogram CVG of an object to be analyzed, is attachable to and detachable from the measuring instrument 12 that measures the cyclic voltammogram CVG, and is discarded every time the measurement of the cyclic voltammogram CVG is performed. In other words, the sensor 11 is a disposable sensor.

[0067] In this way, the sensor 11 is discarded each time a cyclic voltammogram measurement is performed, so there is no need to regenerate the electrodes (working electrode 112, counter electrode 113, and reference electrode 114) by physical polishing or other methods, or to pre-treat the sample (object to be analyzed).

[0068] Fig. 4 is a schematic diagram of the measuring instrument 12 shown in Fig. 2. Fig. 5 is a schematic diagram showing a timing chart of the potential supplied to the sensor 11 shown in Fig. 2.

[0069] Referring to FIG. 4, the measuring instrument 12 includes a supplying unit 121 , a measuring unit 122 , and a transmitting unit 123 .

[0070] The supply unit 121 is electrically connected to the working electrode 112 via the wiring 115. The supply unit 121 receives a potential scan range and a potential scan rate input by a user of the sensor device 1. The supply unit 121 then supplies a potential within the potential scan range to the working electrode 112 via the wiring 115 while changing the potential at a predetermined scan rate.

[0071] Here, users of the sensor device 1 are, for example, staff at restaurants such as Japanese restaurants, Chinese restaurants, and Western restaurants, master brewers at sake breweries, and staff at liquor stores.

[0072] The measurement unit 122 is electrically connected to the working electrode 112, the counter electrode 113, and the reference electrode 114 by wiring 115, 116, and 117, respectively, and measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, measures the current value I from the counter electrode 113, and creates measurement data MRS including current-potential characteristics (IV) that mutually correspond the measured potential V and current value I.

[0073] The potential scanning rate is, for example, 0.3 V / sec, 0.5 V / sec, or 0.6 V / sec, and the potential scanning range is, for example, −2.5 V to +2.5 V.

[0074] Referring to FIG. 5, in the period from time t1 to time t2, the supply unit 121 supplies the potential V in the range of 0 V to +2.5 V to the working electrode 112 while changing the potential V at a predetermined scanning speed.

[0075] Thereafter, during the period from time t2 to time t3, the supply unit 121 supplies the potential V in the range of +2.5 V to 0 V to the working electrode 112 while changing the potential V at a predetermined scanning speed.

[0076] Subsequently, during the period from time t3 to time t4, the supply unit 121 supplies the potential V in the range of 0 V to −2.5 V to the working electrode 112 while changing the potential V at a predetermined scanning speed.

[0077] Furthermore, during the period from time t4 to time t5, the supply unit 121 supplies the potential V in the range of −2.5 V to 0 V to the working electrode 112 while changing the potential V at a predetermined scanning speed.

[0078] In this way, the supply unit 121 supplies the triangular wave potential V to the working electrode 112 .

[0079] In this embodiment of the present invention, the supply unit 121 supplies the working electrode 112 with a potential V in a potential scan range of −2.5 V to +2.5 V while changing the potential V at a scan rate of 0.3 V / sec, and the measurement unit 122 measures the potential V of the working electrode 112 using the potential of the reference electrode 114 as a reference, measures the current value I from the counter electrode 113, and creates measurement data MRS_Low including a current-potential characteristic (I-V)_Low that correlates the measured potential V with the current value I.

[0080] Furthermore, the supply unit 121 supplies the working electrode 112 with a potential V in a potential scan range of −2.5 V to +2.5 V while changing the potential V at a scan rate of 0.5 V / sec, and the measurement unit 122 measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, measures the current value I from the counter electrode 113, and creates measurement data MRS_Middle including a current-potential characteristic (I-V)_Middle that correlates the measured potential V and the current value I.

[0081] Furthermore, the supply unit 121 supplies the potential V in a potential scan range of −2.5 V to +2.5 V to the working electrode 112 while changing the potential V at a scan rate of 0.6 V / sec, and the measurement unit 122 measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, measures the current value I from the counter electrode 113, and creates measurement data MRS_High including a current-potential characteristic (I-V)_High that correlates the measured potential V and the current value I.

[0082] Then, the measurement unit 122 generates measurement data MRS including the measurement data MRS_Low, the measurement data MRS_Middle, and the measurement data MRS_High, and outputs the generated measurement data MRS to the transmission unit 123 .

[0083] The transmission unit 123 receives the measurement data MRS from the measurement unit 122 and transmits the received measurement data MRS to the diagnostic device 2 by wireless communication or wired communication.

[0084] 6 is a schematic diagram of the measurement data MRS. Referring to FIG. 6, the measurement data MRS includes the name of the analysis object, the type of the analysis object, and measurement data MRS_Low, MRS_Middle, and MRS_High.

[0085] The name of the object to be analyzed may be, for example, shochu or grapes. When the name of the object to be analyzed is shochu, the type of the object to be analyzed may be, for example, sweet potato shochu, barley shochu, rice shochu, etc. When the name of the object to be analyzed is "grapes," the type of the object to be analyzed may be, for example, green seedless, crimson seedless, shine muscat, etc.

[0086] The measurement data MRS_Low is the potential scanning speed V ｒ＿Ｌｏｗ and the current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) and the current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) is the potential V Ｌｏｗ and the current value I Ｌｏｗ The configuration is such that the above are associated with each other.

[0087] Potential V Ｌｏｗ is V １＿Ｌｏｗ ~V ｄ＿Ｌｏｗ and the current value I Ｌｏｗ I １＿Ｌｏｗ ~I ｄ＿Ｌｏｗ The current value I １＿Ｌｏｗ ~I ｄ＿Ｌｏｗ are the potential V １＿Ｌｏｗ ~V ｄ＿Ｌｏｗ can be associated with

[0088] And the potential V １＿Ｌｏｗ ~V ｄ＿Ｌｏｗ is the potential scanning speed "Vｒ＿Ｌｏｗ " is the potential of the working electrode 112 measured with the potential of the reference electrode 114 as a standard, and the current value I １＿Ｌｏｗ ~I ｄ＿Ｌｏｗ is the potential scanning speed "V ｒ＿Ｌｏｗ " is the current value I from the opposing electrode 113 when

[0089] The measurement data MRS_Middle is the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ and the current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) and the current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) is the potential V Ｍｉｄｄｌｅ and the current value I Ｍｉｄｄｌｅ The configuration is such that the above are associated with each other.

[0090] Potential V Ｍｉｄｄｌｅ is V １＿Ｍｉｄｄｌｅ ~V ｄ＿Ｍｉｄｄｌｅ and the current value I Ｍｉｄｄｌｅ I １＿Ｍｉｄｄｌｅ ~I ｄ＿Ｍｉｄｄｌｅ The current value I １＿Ｍｉｄｄｌｅ ~I ｄ＿Ｍｉｄｄｌｅ are the potential V １＿Ｍｉｄｄｌｅ ~V ｄ＿Ｍｉｄｄｌｅ can be associated with

[0091] And the potential V １＿Ｍｉｄｄｌｅ ~V ｄ＿Ｍｉｄｄｌｅ is the potential scanning speed "V ｒ＿Ｍｉｄｄｌｅ " is the potential of the working electrode 112 measured with the potential of the reference electrode 114 as a standard, and the current value I １＿Ｍｉｄｄｌｅ ~I ｄ＿Ｍｉｄｄｌｅ is the potential scanning speed "V ｒ＿Ｍｉｄｄｌｅ " is the current value I from the opposing electrode 113 when

[0092] The measurement data MRS_High is the potential scanning speed V ｒ＿Ｈｉｇｈ and the current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) and the current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) is the potential V Ｈｉｇｈ and the current value I Ｈｉｇｈ The configuration is such that the above are associated with each other.

[0093] Potential V Ｈｉｇｈ is V１＿Ｈｉｇｈ ~V ｄ＿Ｈｉｇｈ and the current value I Ｈｉｇｈ I １＿Ｈｉｇｈ ~I ｄ＿Ｈｉｇｈ The current value I １＿Ｈｉｇｈ ~I ｄ＿Ｈｉｇｈ are the potential V １＿Ｈｉｇｈ ~V ｄ＿Ｈｉｇｈ can be associated with

[0094] And the potential V １＿Ｈｉｇｈ ~V ｄ＿Ｈｉｇｈ is the potential scanning speed "V ｒ＿Ｈｉｇｈ " is the potential of the working electrode 112 measured with the potential of the reference electrode 114 as a standard, and the current value I １＿Ｈｉｇｈ ~I ｄ＿Ｈｉｇｈ is the potential scanning speed "V ｒ＿Ｈｉｇｈ " is the current value I from the opposing electrode 113 when

[0095] Potential V Ｌｏｗ When the scanning range of the potential V is −2.5 V to +2.5 V, １＿Ｌｏｗ , V ２＿Ｌｏｗ , V ３＿Ｌｏｗ , V ４＿Ｌｏｗ , ..., V ｄ－２＿Ｌｏｗ , V ｄ－１＿Ｌｏｗ , V ｄ＿Ｌｏｗ are respectively 0 V, 1 mV, 2 mV, 3 mV, ..., 2499 mV, 2500 mV, 2499 mV, ..., 2 mV, 1 mV, 0 mV, -1 mV, -2 mV, ..., -2499 mV, -2500 mV, -2499 mV, ..., -2 mV, -1 mV, and 0 V. That is, the potential V １＿Ｌｏｗ , V ２＿Ｌｏｗ , V ３＿Ｌｏｗ , V ４＿Ｌｏｗ , ..., V ｄ－２＿Ｌｏｗ , V ｄ－１＿Ｌｏｗ , V ｄ＿Ｌｏｗ is made up of potentials changed by unit potential (=1 mV). As a result, d represents twice the total number of unit potentials in the scanning range of potential V.

[0096] Potential V １＿Ｍｉｄｄｌｅ ~V ｄ＿Ｍｉｄｄｌｅ and potential V １＿Ｈｉｇｈ ~V ｄ＿Ｈｉｇｈ The same applies to.

[0097] The measurement unit 122 receives from the user of the sensor device 1 the name of the object to be analyzed, the type of the object to be analyzed, and the potential scanning speed V ｒ＿Ｌｏｗ , V ｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ and the potential scanning speed V ｒ＿Ｌｏｗ The current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) and the potential scanning rate V ｒ＿Ｍｉｄｄｌｅ The current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) and the potential scanning rate V ｒ＿Ｈｉｇｈ The current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ Then, the measurement unit 122 measures the name of the object to be analyzed, the type of the object to be analyzed, the potential scanning speed V ｒ＿Ｌｏｗ , V ｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ , and the current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ), (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ), (I Ｈｉｇｈ -V Ｈｉｇｈ ) and outputs the created measurement data MRS to the transmission unit 123.

[0098] The transmission unit 123 receives the measurement data MRS from the measurement unit 122 and transmits the received measurement data MRS to the diagnostic device 2 via wired or wireless communication.

[0099] When transmitting the measurement data MRS to the diagnostic device 2 by wireless communication, the transmission unit 123 transmits the measurement data MRS to the diagnostic device 2 by, for example, Bluetooth (registered trademark).

[0100] Furthermore, when the transmission unit 123 transmits the measurement data MRS to the diagnostic device 2 by wired communication, the transmission unit 123 is connected to the diagnostic device 2 by a cable.

[0101] When the sensor device 1 measures P cyclic voltammograms of P (P is an integer of 2 or more) analysis targets by cyclic voltammetry, the measurement unit 122 of the sensor device 1 creates each of the P pieces of measurement data MRS_1 to MRS_P by the above-mentioned method, and the transmission unit 123 of the sensor device 1 transmits the P pieces of measurement data MRS_1 to MRS_P created by the measurement unit 122 to the diagnostic device 2 by wired communication or wireless communication. In this case, each of the P pieces of measurement data MRS_1 to MRS_P has the same configuration as the measurement data MRS shown in FIG.

[0102] Fig. 7 is a schematic diagram of the diagnostic device 2 shown in Fig. 1. Referring to Fig. 7, the diagnostic device 2 includes an analysis / diagnosis unit 21 and a database 22.

[0103] The analysis / diagnosis unit 21 receives the measurement data MRS from the measuring instrument 12 (transmission unit 123) of the sensor device 1 by wireless communication or wired communication.

[0104] Then, the analysis / diagnosis unit 21 diagnoses the taste of the object to be analyzed (shochu or grapes) based on the measurement data MRS using a method described below, stores the diagnosed taste diagnosis result in the database 22 in association with the object to be analyzed (shochu or grapes), and displays the diagnostic result of the taste of the object to be analyzed (shochu or grapes).

[0105] The database 22 stores the results of the taste diagnosis in association with the object to be analyzed (shochu or grapes).

[0106] Fig. 8 is a schematic diagram of the analysis / diagnosis unit 21 shown in Fig. 7. Referring to Fig. 8, the analysis / diagnosis unit 21 includes a receiving unit 211, a control unit 212, calculation units 213 and 216, a determination unit 214, a creation unit 215, a taste diagnosis unit 217, a display unit 218, and a reception unit 219.

[0107] The receiving unit 211 receives the measurement data MRS from the measuring instrument 12 (transmitting unit 123 ) of the sensor device 1 by wireless communication or wired communication, and outputs the received measurement data MRS to the control unit 212 .

[0108] Here, the measurement data MRS may be one piece of measurement data or a plurality of pieces of measurement data.

[0109] The control unit 212 has a built-in timer. When the control unit 212 receives one piece of measurement data MRS_uni from the receiving unit 211, the control unit 212 refers to the timer and calculates the time t ｕｎｉ and identification information ID for identifying the measurement data MRS_uni ｕｎｉ The measurement data MRS_uni has the same structure as the measurement data MRS shown in FIG.

[0110] Then, the control unit 212 calculates the name of the object to be analyzed, ALY_Na. ｕｎｉ and the type of the object to be analyzed ALY_Kd ｕｎｉ and the potential scanning speed V ｒ＿Ｌｏｗ＿ｕｎｉ and the current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ and the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ＿ｕｎｉ and the current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ and the potential scanning speed V ｒ＿Ｈｉｇｈ＿ｕｎｉ and the current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ are detected from the measurement data MRS_uni.

[0111] Thereafter, the control unit 212 controls the scanning speed V ｒ＿Ｌｏｗ＿ｕｎｉ and current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ and the associated measurement data MRS_Low_uni={V ｒ＿Ｌｏｗ＿ｕｎｉ :(I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ} and the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ＿ｕｎｉ and current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ and the associated measurement data MRS_Middle_uni={V ｒ＿Ｍｉｄｄｌｅ＿ｕｎｉ :(V Ｍｉｄｄｌｅ -VＭｉｄｄｌｅ ) ｕｎｉ} and the potential scanning speed V ｒ＿Ｈｉｇｈ＿ｕｎｉ and current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ and the associated measurement data MRS_High_uni={V ｒ＿Ｈｉｇｈ＿ｕｎｉ :(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ} and create.

[0112] Then, the control unit 212 detects the time t ｕｎｉ , identification information ID ｕｎｉ , name of the object to be analyzed ALY_Na ｕｎｉ , type of object to be analyzed ALY_Kd ｕｎｉ , measurement data MRS_Low_uni={V ｒ＿Ｌｏｗ＿ｕｎｉ :(I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ}, measurement data MRS_Middle_uni={V ｒ＿Ｍｉｄｄｌｅ＿ｕｎｉ :(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ} and measurement data MRS_High_uni={V ｒ＿Ｈｉｇｈ＿ｕｎｉ :(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ}, and the analysis data ALY_D ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / MRS_Low_uni / MRS_Middle_uni / MRS_HighH_uni].

[0113] Then, the control unit 212 generates the analysis data ALY_D ｕｎｉ is stored in the database 22 and the analysis data ALY_D ｕｎｉ to the arithmetic unit 213.

[0114] Furthermore, when the control unit 212 receives P pieces of measurement data MRS_1 to MRS_P (i.e., a plurality of pieces of measurement data) from the receiving unit 211, the control unit 212 references the timer and calculates the time t １ ~tＰ and P pieces of identification information ID for identifying each of the P pieces of measurement data MRS_1 to MRS_P. １ ~ID Ｐ Here, each of the P pieces of measurement data MRS_1 to MRS_P has the same structure as the measurement data MRS shown in FIG.

[0115] Then, the control unit 212 calculates the name of the object to be analyzed, ALY_Na. ｐ and the type of the object to be analyzed ALY_Kd ｐ and the potential scanning speed V ｒ＿Ｌｏｗ＿ｐ and the current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｐ and the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ＿ｐ and the current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ and the potential scanning speed V ｒ＿Ｈｉｇｈ＿ｐ and the current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ and are detected from the measurement data MRS_p (p is any of 1 to P) for all of the P measurement data MRS_1 to MRS_P (that is, a plurality of measurement data).

[0116] Thereafter, the control unit 212 controls the scanning speed V ｒ＿Ｌｏｗ＿ｐ and current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｐ and the associated measurement data MRS_Low_p={V ｒ＿Ｌｏｗ＿ｐ :(I Ｌｏｗ -V Ｌｏｗ ) ｐ} and the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ＿ｐ and current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ and the associated measurement data MRS_Middle_p={V ｒ＿Ｍｉｄｄｌｅ＿ｐ :(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ} and the potential scanning speed V ｒ＿Ｈｉｇｈ＿ｐ and current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐand the associated measurement data MRS_High_p={V ｒ＿Ｈｉｇｈ＿ｐ :(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ} is created for all p=1 to P to generate P measurement data MRS_Low_1={V ｒ＿Ｌｏｗ＿１ :(I Ｌｏｗ -V Ｌｏｗ ) １}~MRS_Low_P={V ｒ＿Ｌｏｗ＿Ｐ :(I Ｌｏｗ -V Ｌｏｗ ) Ｐ}, P measurement data MRS_Middle_1={V ｒ＿Ｍｉｄｄｌｅ＿１ :(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) １}~MRS_Middle_P={V ｒ＿Ｍｉｄｄｌｅ＿Ｐ :(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) Ｐ} and P MRS_High_1={V ｒ＿Ｈｉｇｈ＿１ :(I Ｈｉｇｈ -V Ｈｉｇｈ ) １}~MRS_High_P={V ｒ＿Ｈｉｇｈ＿Ｐ :(I Ｈｉｇｈ -V Ｈｉｇｈ ) Ｐ} to create a

[0117] Then, the control unit 212 detects the time t ｐ , identification information ID ｐ , name of the object to be analyzed ALY_Na ｐ , type of object to be analyzed ALY_Kd ｐ , measurement data MRS_Low_p={V ｒ＿Ｌｏｗ＿ｐ :(I Ｌｏｗ -V Ｌｏｗ ) ｐ}, measurement data MRS_Middle_p={V ｒ＿Ｍｉｄｄｌｅ＿ｐ :(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ} and measurement data MRS_High_p={V ｒ＿Ｈｉｇｈ＿ｐ :(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ} (p is any one of 1 to P) are associated with each other. ｐ = [tｐ / ID ｐ / ALY_Na ｐ / ALY_Kd ｐ / measurement data MRS_Low_p / measurement data MRS_Middle_p / measurement data MRS_High_p] is created for all p=1 to P, and P pieces of analysis data ALY_D １ = [t １ / ID １ / ALY_Na １ / ALY_Kd １ / Measurement data MRS_Low_1 / Measurement data MRS_Middle_1 / Measurement data MRS_High_1] to ALY_D Ｐ = [t Ｐ / ID Ｐ / ALY_Na Ｐ / ALY_Kd Ｐ / measurement data MRS_Low_P / measurement data MRS_Middle_P / measurement data MRS_High_P] is created.

[0118] Then, the control unit 212 generates P pieces of analysis data ALY_D １ ~ ALY_D Ｐ are stored in the database 22, and P pieces of analysis data ALY_D １ ~ ALY_D Ｐ to the arithmetic unit 213.

[0119] Furthermore, the control unit 212 generates the analysis data ALY_D ｕｎｉ to the arithmetic unit 213, and then the identification information ID ｕｎｉ and calculation data CAL ｕｎｉ and curve CUR ｕｎｉ The analysis result ALY_RLS is a result of associating ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] from the creation unit 215, the analysis result ALY_RLS ｕｎｉ From identification information ID ｕｎｉ , calculation data CAL ｕｎｉ and the curve CUR ｕｎｉ and the detected identification information ID ｕｎｉ Analysis data ALY_D having the same identification information asｕｎｉ is read from the database 22.

[0120] Then, the control unit 212 reads out the analysis data ALY_D from the database 22. ｕｎｉ Calculation data CAL ｕｎｉ and curve CUR ｕｎｉ is stored as analysis data ALY_D ｕｎｉ The index data IDX ｕｎｉ The updated index data IDX ｕｎｉ to the arithmetic unit 216.

[0121] Thereafter, the control unit 212 receives the index data IDX ｕｎｉ The analysis data ALY_D ｕｎｉ Instead, the information is stored in the database 22.

[0122] Furthermore, the control unit 212 generates P pieces of analysis data ALY_D １ ~ ALY_D Ｐ to the arithmetic unit 213, and then P pieces of identification information ID １ ~ID Ｐ and P calculation data CAL １ ~CAL Ｐ and P curves CUR １ ~CUR Ｐ P analysis results ALY_RLS １ = [ID １ / CAL １ / CUR １ ]~ALY_RLS Ｐ = [ID Ｐ / CAL Ｐ / CUR Ｐ ] and P curves CUR １ ~CUR Ｐ When the determination result JDGR indicating whether or not the analysis results ALY_RLS are different from each other is received from the creation unit 215, the analysis result ALY_RLS is ｐ = [ID ｐ / CAL ｐ / CUR ｐ ] (p is one of 1 to P) to the identification information ID ｐ , calculation data CAL ｐ and the curve CUR ｐ and the detected identification information IDｐ Analysis data ALY_D having the same identification information as ｐ is read from the database 22 for all p=1 to P.

[0123] Then, the control unit 212 reads out the analysis data ALY_D from the database 22. ｐ Calculation data CAL ｐ and the curve CUR ｐ is stored as analysis data ALY_D ｐ The index data IDX ｐ This update is performed for all p=1 to P.

[0124] Thereafter, the control unit 212 outputs P index data IDX １ ~IDX Ｐ to the arithmetic unit 216.

[0125] Then, the control unit 212 receives P index data IDX １ ~IDX Ｐ Each of the P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Instead, the information is stored in the database 22.

[0126] Then, the control unit 212 calculates the P index data IDX １ ~ Index data IDX Ｐ The determination result JDGR is stored in the database 22 in association with the above.

[0127] Furthermore, the control unit 212 stores the index data IDX ｕｎｉ to the calculation unit 216, and then the diagnostic result JDR, which is the result of diagnosing the taste (astringency, aftertaste, sweetness, aroma, and bitterness) of the object to be analyzed (shochu or grapes), is output. ｕｎｉ When the taste diagnosis unit 217 receives the diagnosis result JDR ｕｎｉ The index data IDX ｕｎｉ are stored in the database 22 in association with each other.

[0128] Furthermore, the control unit 212 generates P index data IDX １ ~IDX Ｐto the arithmetic unit 216, and then P diagnosis results JDR １ ~JDR Ｐ When the taste diagnosis unit 217 receives the P diagnosis results JDR １ ~JDR Ｐ Each of the P index data IDX １ ~IDX Ｐ are stored in the database 22 in association with each other.

[0129] Furthermore, the control unit 212 determines the name of the object to be analyzed, ALY_Na ｕｎｉ And the name ALY_Na ｕｎｉ The diagnosis result JDR associated with ｕｎｉ A request RQT to display ｕｎｉ When the name of the analysis object ALY_Na is received from the reception unit 219, ｕｎｉ Based on the above, the name of the analyte ALY_Na ｕｎｉ The diagnosis result JDR associated with ｕｎｉ is detected from the database 22, and the name of the object to be analyzed is ALY_Na ｕｎｉ The diagnosis was JDR ｕｎｉ and output to the display unit 218.

[0130] The control unit 212 also calculates the P names ALY_Na of the P analysis objects. １ ~ALY_Na Ｐ q (q is an integer satisfying 1≦q≦P) names ALY_Na １ ~ALY_Na ｑ and q names ALY_Na １ ~ALY_Na ｑ q diagnostic results JDR respectively associated with １ ~JDR ｑ A request RQT to display ｑ When the receiving unit 219 receives the q names ALY_Na １ ~ALY_Na ｑ Based on the above, q names ALY_Na of q analytes are １ ~ALY_Na ｑ and q names ALY_Na of q analytes. １~ALY_Na ｑ q diagnostic results JDR respectively associated with １ ~JDR ｑ and q names ALY_Na of the q analysis objects are detected from the database 22. １ ~ALY_Na ｑ and q diagnostic results JDR １ ~JDR ｑ and output to the display unit 218.

[0131] In this case, the request RQT ｑ In the above, q names ALY_Na of q analytes are １ ~ALY_Na ｑ Instead of q types ALY_Kd of q analytes １ ~ALY_Kd ｑ may also be used.

[0132] The calculation unit 213 calculates the analysis data ALY_D ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / MRS_Low_uni={Low_uni:(I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ} / MRS_Middle_uni={Middle_uni:(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ} / MRS_High_uni={High_uni:(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ}] from the control unit 212.

[0133] Then, the calculation unit 213 calculates the analysis data ALY_D ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / MRS_Low_uni={Low_uni:(I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ} / MRS_Middle_uni={Middle_uni:(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ) ｕｎｉ} / MRS_High_uni={High_uni:(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ}] current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ , (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ , (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ}, the integral value ITG of the cyclic voltammogram CVG is calculated for each predetermined potential interval V_ITV by a method to be described later.

[0134] If the number of predetermined potential sections V_ITV is n, the calculation unit 213 sets the first predetermined potential section V_ITV as "class Cls １ ", and the second predetermined potential section V_ITV is set to "Class Cls ２ ", and so on. Similarly, the (n-1)th predetermined potential section V_ITV is set to "Class Cls ｎ－１ ", and the n-th predetermined potential section V_ITV is set to "Class Cls ｎ "

[0135] Then, the calculation unit 213 calculates the measurement data MRS_Low_uni={Low_uni:(I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ} current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ Based on this, the integral value in the first predetermined potential section V_ITV is calculated as ITG １＿Ｌｏｗ and the integral value in the second predetermined potential section V_ITV is ITG ２＿Ｌｏｗ ... and so on, similarly, the integral value in the (n-1)th predetermined potential section V_ITV is set to ITG （ｎ－１）＿Ｌｏｗ The integral value in the n-th predetermined potential section V_ITV is ITG ｎ＿Ｌｏｗ Let's say.

[0136] Furthermore, the calculation unit 213 calculates the measurement data MRS_Middle_uni={Middle_uni:(I Ｍｉｄｄｌｅ-V Ｍｉｄｄｌｅ ) ｕｎｉ} current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ Based on this, the integral value in the first predetermined potential section V_ITV is calculated as ITG １＿Ｍｉｄｄｌｅ and the integral value in the second predetermined potential section V_ITV is ITG ２＿Ｍｉｄｄｌｅ ... and so on, similarly, the integral value in the (n-1)th predetermined potential section V_ITV is set to ITG ｎ－１＿Ｍｉｄｄｌｅ The integral value in the n-th predetermined potential section V_ITV is ITG ｎ＿Ｍｉｄｄｌｅ Let's say.

[0137] Furthermore, the calculation unit 213 calculates the measurement data MRS_High_uni={High_uni:(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ} current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ Based on this, the integral value in the first predetermined potential section V_ITV is calculated as ITG １＿Ｈｉｇｈ and the integral value in the second predetermined potential section V_ITV is ITG ２＿Ｈｉｇｈ ... and so on, similarly, the integral value in the (n-1)th predetermined potential section V_ITV is set to ITG ｎ－１＿Ｈｉｇｈ The integral value in the n-th predetermined potential section V_ITV is ITG ｎ＿Ｈｉｇｈ Let's say.

[0138] Then, the calculation unit 213 calculates the class Cls １ The integral value ITG １＿Ｌｏｗ , ITG １＿Ｍｉｄｄｌｅ , ITG １＿Ｈｉｇｈ Add up the total integral value ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ Calculate the class Cls ２ The integral value ITG ２＿Ｌｏｗ , ITG ２＿Ｍｉｄｄｌｅ , ITG ２＿Ｈｉｇｈ Add up the total integral value ITG ２＿Ｌｏｗ +ITG ２＿Ｍｉｄｄｌｅ +ITG ２＿Ｈｉｇｈ Calculate the following in the same way: ｎ The integral value ITGｎ＿Ｌｏｗ , ITG ｎ＿Ｍｉｄｄｌｅ , ITG ｎ＿Ｈｉｇｈ Add up the total integral value ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ Calculate.

[0139] Then, the calculation unit 213 calculates n classes Cls １ ~Cls ｎ and n classes Cls １ ~Cls ｎ n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ , ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ , ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ and n classes Cls １ ~Cls ｎ The sum of n integral values ​​(ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ ) ~ (ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ ) and calculation data CAL ｕｎｉ Create a.

[0140] Thereafter, the calculation unit 213 calculates the calculation data CAL ｕｎｉ and identification information ID ｕｎｉ The calculation result CAL_RLS in which ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ Then, the calculation unit 213 creates the calculation result CAL_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ ] and a signal S_u indicating that there is one operation data item to the creation unit 215.

[0141] The calculation unit 213 also calculates P pieces of analysis data ALY_D １ = [t １ / ID １ / ALY_Na １ / ALY_Kd １ / MRS_Low_1={Low_1:(I Ｌｏｗ -VＬｏｗ ) １} / MRS_Middle_1={Middle_1:(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) １} / MRS_High_1={High_1:(I Ｈｉｇｈ -V Ｈｉｇｈ ) １ ]~ALY_D Ｐ = [t Ｐ / ID Ｐ / ALY_Na Ｐ / ALY_Kd Ｐ / MRS_Low_P={Low_P:(I Ｌｏｗ -V Ｌｏｗ ) Ｐ} / MRS_Middle_P={Middle_P:(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) Ｐ} / {MRS_High_P=High_P:(I Ｈｉｇｈ -V Ｈｉｇｈ ) Ｐ}] from the control unit 212.

[0142] Then, the calculation unit 213 calculates the analysis data ALY_D ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / MRS_Low_uni={Low_uni:(I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ} / MRS_Middle_uni={Middle_uni:(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ} / MRS_High_uni={High_uni:(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ}] from the control unit 212, the analysis data ALY_D ｐ = [t ｐ / ID ｐ / ALY_Na ｐ / ALY_Kd ｐ / MRS_Low_p={Low_p:(I Ｌｏｗ -V Ｌｏｗ ) ｐ} / MRS_Middle_p={Middle_p:(I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ} / {MRS_High_p=High_p:(I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ}] current-potential characteristics {(I Ｌｏｗ -V Ｌｏｗ ) ｐ , (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ , (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ} (p is any one of 1 to P), based on １ ~Cls ｎ and n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ , ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ , ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ and the sum of n integral values ​​(ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ ) ~ (ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ ) and the corresponding calculation data CAL ｐ The creation of P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Calculation data CAL is performed for all P data. １ ~CAL Ｐ Create a.

[0143] Then, the calculation unit 213 calculates P pieces of identification information ID １ ~ID Ｐ and P calculation data CAL １ ~CAL Ｐ P calculation results CAL_RLS １ = [ID １ / CAL １ ]~CAL_RLS Ｐ = [ID Ｐ / CAL Ｐ ] to the determination unit 214 and the creation unit 215 .

[0144] In this way, the calculation unit 213 calculates one calculation data CAL ｕｎｉ When the calculation result CAL_RLS is created, ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ ] to the creation unit 215 only, and P calculation results CAL_RLS １ = [ID １ / CAL １ ]~CAL_RLS Ｐ = [ID Ｐ / CAL Ｐ ] (i.e., a plurality of calculation results) are created, P calculation results CAL_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) to the determination unit 214 and the creation unit 215.

[0145] As mentioned above, the calculation data CAL ｕｎｉ is n classes Cls １ ~Cls ｎ and n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ , ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ , ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ and the sum of n integral values ​​(ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ ) ~ (ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ ) and n classes Cls １ ~Cls ｎ Each of these consists of a predetermined potential section V_ITV, so the calculation data CAL ｕｎｉ is n predetermined potential sections V_ITV １ ~V_ITV ｎ and n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ , ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ , ITG １＿Ｈｉｇｈ ~ITGn ＿Ｈｉｇｈ and the sum of n integral values ​​(ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG１＿Ｈｉｇｈ ) ~ (ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ ) and P pieces of calculation data CAL １ ~CAL Ｐ The same is true for each of the above.

[0146] The decision unit 214 calculates the P calculation results CAL_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) from the calculation unit 213. Then, the determination unit 214 determines the P calculation results CAL_RLS １ ~CAL_RLS Ｐ P pieces of calculation data CAL included in １ ~CAL Ｐ Detect.

[0147] Then, the determination unit 214 calculates the P pieces of calculation data CAL １ ~CAL Ｐ Based on Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ (i≠j), where Ｐ C ２ is P pieces of calculation data CAL １ ~CAL Ｐ Two different calculation data CAL ｉ , CAL ｊ Two different calculation data CAL when extracting (i≠j) ｉ , CAL ｊ This is the number of combinations where (i≠j).

[0148] Then, the decision unit 214 calculates the two calculation data CAL ｉ , CAL ｊ Based on (i≠j), the calculation data CAL ｉ The multiple integral values ​​and calculation data CAL included in ｊ Calculate the difference for each class Cls with the multiple integral values ​​included in Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊThis is carried out for all (i≠j).

[0149] The determination unit 214 calculates the calculation data CAL by the following method. ｉ The multiple integral values ​​and calculation data CAL included in ｊ The difference between the plurality of integral values ​​included in each class Cls is calculated.

[0150] The determination unit 214 calculates the calculation data CAL ｉ A plurality of integral values ​​ITG included in １＿ｉ ~ITG ｎ＿ｉ and calculation data CAL ｊ A plurality of integral values ​​ITG included in １＿ｊ ~ITG ｎ＿ｊ Based on this, one class Cls is determined by the following formula: ｋ The integral value ITG ｋ＿ｉ and the integral value ITG ｋ＿ｊ Difference DF ｋ Calculate the following.

[0151]

[0152] The difference DF calculated by the formula (1) ｋ The unit is "%".

[0153] Then, the decision unit 214 determines one class Cls by the formula (1). ｋ The difference DF of the integral value in ｋ The calculation of n classes Cls １ ~Cls ｎ Execute for all of the n differential DFs １ ~DF ｎ Calculate.

[0154] Then, the decision unit 214 calculates n differences DF １ ~DF ｎ Standard deviation σ ＤＦ Calculate.

[0155] Then, the determination unit 214 calculates the standard deviation σ of the differences. ＤＦ is the threshold σ ｔｈ (=6%), the two calculation data CAL ｉ , CAL ｊ (i≠j) is judged to be different, and the standard deviation of the difference σＤＦ is the threshold σ ｔｈ (=6%) or less, the two calculation data CAL ｉ , CAL ｊ (i≠j) is determined to be not different. ｔｈ (=6%) is held in advance.

[0156] The decision unit 214 calculates the two calculation data CAL by the above-mentioned method. ｉ , CAL ｊ (i≠j) is determined to be different or not. Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ Execute for all (i≠j) Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ Two calculation data CAL of each pair (i≠j) ｉ , CAL ｊ It is determined whether (i≠j) is different.

[0157] In addition, two calculation data CAL ｉ , CAL ｊ The determination that (i≠j) is not different means that the two calculation data CAL ｉ , CAL ｊ This corresponds to determining that (i≠j) cannot be distinguished, and the two calculation data CAL ｉ , CAL ｊ The determination that (i≠j) is different means that the two calculation data CAL ｉ , CAL ｊ This corresponds to determining that (i≠j) can be determined.

[0158] The determination unit 214 determines, by the method described above, Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ Two calculation data CAL of each pair (i≠j) ｉ , CAL ｊThe determination unit 214 determines whether or not (i≠j) is different, and creates the determination result shown in Table 1. The determination unit 214 then outputs the determination result shown in Table 1 to the creation unit 215.

[0159]

[0160] P calculation data CAL １ ~CAL Ｐ are not different from each other, １ ~CAL Ｐ This corresponds to determining that the P calculation data CAL １ ~CAL Ｐ are different from each other, the P pieces of calculation data CAL １ ~CAL Ｐ This corresponds to determining that the two images can be distinguished from each other.

[0161] The creation unit 215 generates one calculation result CAL_RLS ｕｎｉ and a signal S_u indicating that there is one calculation data item from the calculation unit 213, the calculation data CAL is calculated by the method described later. ｕｎｉ Based on the curve CUR ｕｎｉ Create a.

[0162] The creation unit 215 also creates P calculation results CAL_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) from the calculation unit 213 and P calculation data CAL １ ~CAL Ｐ When the judgment unit 214 receives the judgment result JDGR (the judgment result shown in Table 1) indicating whether or not the P pieces of calculation data CAL are different, the P pieces of calculation data CAL are calculated by the method described later. １ ~CAL Ｐ Based on these, P curves CUR １ ~CUR Ｐ Create a.

[0163] The creation unit 215 creates one curve CUR ｕｎｉ When the calculation result CAL_RLS is created, ｕｎｉ Curve CUR ｕｎｉ Add the analysis result ALY_RLSｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] is created, and the created analysis result ALY_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] to the control unit 212.

[0164] On the other hand, the creation unit 215 creates P curves CUR １ ~CUR Ｐ When P calculation results CAL_RLS are created, １ ~CAL_RLS Ｐ P curves CUR １ ~CUR Ｐ Add P analysis results ALY_RLS １ = [ID １ / CAL １ / CUR １ ]~ALY_RLS Ｐ = [ID Ｐ / CAL Ｐ / CUR Ｐ ] and P analysis results ALY_RLS １ ~ ALY_RLS Ｐ The determination result JDGR (the determination result shown in Table 1) is output to the control unit 212 .

[0165] The calculation unit 216 calculates the index data IDX. ｕｎｉ from the control unit 212. Then, the calculation unit 216 receives the calculation data CAL ｕｎｉ The index data IDX ｕｎｉ The detected calculation data CAL ｕｎｉ Based on this, Body Index (+) ｕｎｉ , Body index (all) ｕｎｉ , Body index (-)_th ｕｎｉ , the sum of the integral values ​​H(+)_sum ｕｎｉ and the sum of the integral values ​​H(-)_sum ｕｎｉ Calculate.

[0166] Then, the calculation unit 216 calculates the name of the object to be analyzed ALY_Na. ｕｎｉ , Body index (+)ｕｎｉ , Body index (all) ｕｎｉ , Body index (-)_th ｕｎｉ , the sum of the integral values ​​H(+)_sum ｕｎｉ and the sum of the integral values ​​H(-)_sum ｕｎｉ index data INDX_D ｕｎｉ = [ALY_Na ｕｎｉ / Body index (+) ｕｎｉ / Body index (all) ｕｎｉ / Body index (-)_th ｕｎｉ / H(+)_sum ｕｎｉ / H(-)_sum ｕｎｉ ] and the created index data INDX_D ｕｎｉ = [ALY_Na ｕｎｉ / Body index (+) ｕｎｉ / Body index (all) ｕｎｉ / Body index (-)_th ｕｎｉ / H(+)_sum ｕｎｉ / H(-)_sum ｕｎｉ ] is output to the taste diagnosis unit 217.

[0167] The calculation unit 216 also calculates P index data IDX １ ~IDX Ｐ The calculation unit 216 receives P calculation data CAL from the control unit 212. １ ~CAL Ｐ Each of the P index data IDX １ ~IDX Ｐ Detect from.

[0168] Then, the calculation unit 216 calculates the calculation data CAL ｐ Based on this, Body Index (+) ｐ , Body index (all) ｐ , Body index (-)_th ｐ , the sum of the integral values ​​H(+)_sum ｐ and the sum of the integral values ​​H(-)_sum ｐ (p=1 to P) is calculated.

[0169] Then, the calculation unit 216 calculates the name of the object to be analyzed ALY_Na. ｐ , Body index (+)ｐ , Body index (all) ｐ , Body index (-)_th ｐ , the sum of the integral values ​​H(+)_sum ｐ and the sum of the integral values ​​H(-)_sum ｐ The index data INX_D ｐ = [ALY_Na ｐ / Body index (+) ｐ / Body index (all) ｐ / Body index (-)_th ｐ / H(+)_sum ｐ / H(-)_sum ｐ ] is created for all p=1 to P, and P index data INDX_D １ = [ALY_Na １ / Body index (+) １ / Body index (all) １ / Body index (-)_th １ / H(+)_sum １ / H(-)_sum １ ]~INDX_D Ｐ = [ALY_Na Ｐ / Body index (+) Ｐ / Body index (all) Ｐ / Body index (-)_th Ｐ / H(+)_sum Ｐ / H(-)_sum Ｐ ] to create a

[0170] Then, the calculation unit 216 generates P pieces of exponent data INDX_D １ = [ALY_Na １ / Body index (+) １ / Body index (all) １ / Body index (-)_th １ / H(+)_sum １ / H(-)_sum １ ]~INDX_D Ｐ = [ALY_Na Ｐ / Body index (+) Ｐ / Body index (all) Ｐ / Body index (-)_th Ｐ / H(+)_sum Ｐ / H(-)_sum Ｐ ] is output to the taste diagnosis unit 217.

[0171] The taste diagnosis unit 217 generates index data INDX_D ｕｎｉ = [ALY_Na ｕｎｉ / Body index (+) ｕｎｉ / Body index (all) ｕｎｉ / Body index (-)_th ｕｎｉ / H(+)_sum ｕｎｉ / H(-)_sum ｕｎｉ ] is received from the arithmetic unit 216 .

[0172] Then, the taste diagnosis unit 217 calculates the index data INDX_D ｕｎｉ Name of the object to be analyzed ALY_Na ｕｎｉ , Body index (+) ｕｎｉ , Body index (all) ｕｎｉ , Body index (-)_th ｕｎｉ , the sum of the integral values ​​H(+)_sum ｕｎｉ and the sum of the integral values ​​H(-)_sum ｕｎｉ Detect.

[0173] Then, the taste diagnostic unit 217 calculates the Body index (+) ｕｎｉ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｕｎｉ The "astringency" of the object to be analyzed is expressed as the index value ASTG ｕｎｉ It is diagnosed as being

[0174] The taste diagnostic unit 217 also calculates the Body Index (all). ｕｎｉ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｕｎｉ The "aftertaste" of the object to be analyzed having the index value LNGS ｕｎｉ It is diagnosed as being

[0175] Furthermore, the taste diagnosis unit 217 calculates the sum of the integral values ​​H(+)_sum ｕｎｉ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｕｎｉ The "sweetness" of the object to be analyzed is the index value SWT ｕｎｉ It is diagnosed as being

[0176] Furthermore, the taste diagnosis unit 217 calculates the sum of the integral values ​​H(-)_sum ｕｎｉ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｕｎｉ The "aroma" of the object to be analyzed has the index value SCT ｕｎｉ It is diagnosed as being

[0177] Furthermore, the taste diagnosis unit 217 calculates the index value ASTG for "astringency". ｕｎｉ and the sweetness index value SWT ｕｎｉ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｕｎｉ The "bitterness" of the object to be analyzed is the index value BIT ｕｎｉ It is diagnosed as being

[0178] Then, the taste diagnosis unit 217 determines the name of the object to be analyzed, ALY_Na ｕｎｉ and the "astringency" index value ASTG ｕｎｉ and the index value of "afterglow" LNGS ｕｎｉ and the sweetness index value SWT ｕｎｉ and the "fragrance" index value SCT ｕｎｉ and the index value of "bitterness" BIT ｕｎｉ JDR: Taste diagnosis results that correlate with each other ｕｎｉ = [ALY_Na ｕｎｉ / ASTG ｕｎｉ / LNGS ｕｎｉ / SWT ｕｎｉ / SCT ｕｎｉ / BIT ｕｎｉ ] and the taste diagnosis results JDR ｕｎｉ = [ALY_Na ｕｎｉ / ASTG ｕｎｉ / LNGS ｕｎｉ / SWT ｕｎｉ / SCT ｕｎｉ / BIT ｕｎｉ ] to the control unit 212 and the display unit 218.

[0179] The taste diagnosis unit 217 uses the name ALY_Na of the object to be analyzed. ｕｎｉ When is "grape", Body index (-)_th ｕｎｉ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｕｎｉ(=grapes) the "astringency" of the object to be analyzed is the index value ASTG ｔｈ＿ｕｎｉ It is diagnosed as being

[0180] Then, the taste diagnosis unit 217 determines the name of the object to be analyzed, ALY_Na ｕｎｉ (=grapes) and the "astringency" index value ASTG ｔｈ＿ｕｎｉ JDR: Diagnostic results associated with ｔｈ＿ｕｎｉ = [ALY_Na ｕｎｉ (=grape) / ASTG ｔｈ＿ｕｎｉ ] and the diagnostic results JDR ｔｈ＿ｕｎｉ = [ALY_Na ｕｎｉ (=grape) / ASTG ｔｈ＿ｕｎｉ ] to the control unit 212 and the display unit 218.

[0181] On the other hand, the taste diagnosis unit 217 generates P index data INDX_D １ = [ALY_Na １ / Body index (+) １ / Body index (all) １ / Body index (-)_th １ / H(+)_sum １ / H(-)_sum １ ]~INDX_D Ｐ = [ALY_Na Ｐ / Body index (+) Ｐ / Body index (all) Ｐ / Body index (-)_th Ｐ / H(+)_sum Ｐ / H(-)_sum Ｐ ] from the arithmetic unit 216, the index data INDX_D ｐ = [ALY_Na ｐ / Body index (+) ｐ / Body index (all) ｐ / Body index (-)_th ｐ / H(+)_sum ｐ / H(-)_sum ｐ ] to the name of the object to be analyzed ALY_Na ｐ , Body index (+) ｐ , Body index (all) ｐ , Body index (-)_th ｐ, the sum of the integral values ​​H(+)_sum ｐ and the sum of the integral values ​​H(-)_sum ｐ Detect.

[0182] Then, the taste diagnostic unit 217 calculates the Body index (+) ｐ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｐ The "astringency" of the object to be analyzed is expressed as the index value ASTG ｐ It is diagnosed as being

[0183] The taste diagnostic unit 217 also calculates the Body Index (all). ｐ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｐ The "aftertaste" of the object to be analyzed having the index value LNGS ｐ It is diagnosed as being

[0184] Furthermore, the taste diagnosis unit 217 calculates the sum of the integral values ​​H(+)_sum ｐ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｐ The "sweetness" of the object to be analyzed is the index value SWT ｐ It is diagnosed as being

[0185] Furthermore, the taste diagnosis unit 217 calculates the sum of the integral values ​​H(-)_sum ｐ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｐ The "aroma" of the object to be analyzed has the index value SCT ｐ It is diagnosed as being

[0186] Furthermore, the taste diagnostic unit 217 calculates the index value ASTR of "astringency" ｐ and the sweetness index value SWT ｐ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｐ The "bitterness" of the object to be analyzed is the index value BIT ｐ It is diagnosed as being

[0187] Then, the taste diagnosis unit 217 determines the name of the object to be analyzed, ALY_Na ｐ and the "astringency" index value ASTG ｐ and the index value of "afterglow" LNGS ｐand the sweetness index value SWT ｐ and the "fragrance" index value SCT ｐ and the index value of "bitterness" BIT ｐ JDR: Taste diagnosis results that correlate with each other ｐ = [ALY_Na ｐ / ASTG ｐ / LNGS ｐ / SWT ｐ / SCT ｐ / BIT ｐ ] to create a

[0188] The taste diagnosis unit 217 executes the above-described operation for all p=1 to P to obtain P taste diagnosis results JDR １ = [ALY_Na １ / ASTG １ / LNGS １ / SWT １ / SCT １ / BIT １ ]~JDR Ｐ = [ALY_Na Ｐ / ASTG Ｐ / LNGS Ｐ / SWT Ｐ / SCT Ｐ / BIT Ｐ ] and the P taste diagnosis results JDR １ = [ALY_Na １ / ASTG １ / LNGS １ / SWT １ / SCT １ / BIT １ ]~JDR Ｐ = [ALY_Na Ｐ / ASTG Ｐ / LNGS Ｐ / SWT Ｐ / SCT Ｐ / BIT Ｐ ] to the control unit 212 and the display unit 218.

[0189] The taste diagnosis unit 217 uses the name ALY_Na of the object to be analyzed. ｐ When is "grape", Body index (-)_th ｐ Based on this, the name of the object to be analyzed ALY_Na is obtained by the method described below. ｐThe "astringency" of the object to be analyzed having (=grapes) is the index value ASTG ｔｈ＿ｐ It is diagnosed as being

[0190] Then, the taste diagnosis unit 217 determines the name of the object to be analyzed, ALY_Na ｐ (=grapes) and the "astringency" index value ASTG ｔｈ＿ｐ JDR: Diagnostic results associated with ｔｈ＿ｐ = [ALY_Na ｐ (=grape) / ASTG ｔｈ＿ｐ ] is created for all p=1 to P, and P taste diagnosis results JDR ｔｈ＿１ = [ALY_Na １ (=grape) / ASTG ｔｈ＿１ ]~JDR ｔｈ＿Ｐ = [ALY_Na Ｐ (=grape) / ASTG ｔｈ＿Ｐ ] and the P taste diagnosis results JDR ｔｈ＿１ = [ALY_Na １ (=grape) / ASTG ｔｈ＿１ ]~JDR ｔｈ＿Ｐ = [ALY_Na Ｐ (=grape) / ASTG ｔｈ＿Ｐ ] to the control unit 212 and the display unit 218.

[0191] The display unit 218 displays the diagnosis result JDR. ｕｎｉ = [ALY_Na ｕｎｉ / ASTG ｕｎｉ / LNGS ｕｎｉ / SWT ｕｎｉ / SCT ｕｎｉ / BIT ｕｎｉ ] from the taste diagnosis unit 217, the received diagnosis result JDR ｕｎｉ = [ALY_Na ｕｎｉ / ASTG ｕｎｉ / LNGS ｕｎｉ / SWT ｕｎｉ / SCT ｕｎｉ / BIT ｕｎｉ ] is displayed.

[0192] The display unit 218 also displays the diagnosis result JDR. ｔｈ＿ｕｎｉ = [ALY_Na ｕｎｉ (=grape) / ASTG ｔｈ＿ｕｎｉ] from the taste diagnosis unit 217, the received diagnosis result JDR ｔｈ＿ｕｎｉ = [ALY_Na ｕｎｉ (=grape) / ASTG ｔｈ＿ｕｎｉ ] is displayed.

[0193] Furthermore, the display unit 218 displays the P diagnostic results JDR １ = [ALY_Na １ / ASTG １ / LNGS １ / SWT １ / SCT １ / BIT １ ]~JDR Ｐ = [ALY_Na Ｐ / ASTG Ｐ / LNGS Ｐ / SWT Ｐ / SCT Ｐ / BIT Ｐ ] from the taste diagnosis unit 217, the received P diagnosis results JDR １ = [ALY_Na １ / ASTG １ / LNGS １ / SWT １ / SCT １ / BIT １ ]~JDR Ｐ = [ALY_Na Ｐ / ASTG Ｐ / LNGS Ｐ / SWT Ｐ / SCT Ｐ / BIT Ｐ ] is displayed.

[0194] Furthermore, the display unit 218 displays the P taste diagnosis results JDR ｔｈ＿１ = [ALY_Na １ (=grape) / ASTG ｔｈ＿１ ]~JDR ｔｈ＿Ｐ = [ALY_Na Ｐ (=grape) / ASTG ｔｈ＿Ｐ ] from the taste diagnosis unit 217, the received P taste diagnosis results JDR ｔｈ＿１ = [ALY_Na １ (=grape) / ASTGt ｈ＿１ ]~JDR ｔｈ＿Ｐ = [ALY_Na Ｐ(=grape) / ASTG ｔｈ＿Ｐ ] is displayed.

[0195] The reception unit 219 receives requests from staff at restaurants such as Japanese restaurants, Chinese restaurants, and Western restaurants, brewers at sake breweries, and staff at liquor stores. ｕｎｉ Or request RQT ｑ and receives the received request RQT ｕｎｉ Or request RQT ｑ to the control unit 212.

[0196] [Calculation of the integral value] FIGS. 9 and 10 are first and second conceptual diagrams, respectively, for explaining a method for calculating the integral value.

[0197] Referring to FIG. 9 , the cyclic voltammogram CVG is measured by, for example, scanning the potential V from 0 V to +2500 mV at a predetermined scanning rate, then scanning the potential V from +2500 mV to 0 V at a predetermined scanning rate, further scanning the potential V from 0 V to −2500 mV at a predetermined scanning rate, and further scanning the potential V from −2500 mV to 0 V at a predetermined scanning rate.

[0198] As a result, in the cyclic voltammogram CVG, the solid line portion represents the current value I when the potential V is scanned in the positive direction, and the dotted line portion represents the current value I when the potential V is scanned in the negative direction.

[0199] Therefore, in the cyclic voltammogram CVG, the solid line portion represents the current value I of the oxidation wave, and the dotted line portion represents the current value I of the reduction wave.

[0200] The predetermined scanning speed is, for example, one of 0.3 V / sec, 0.5 V / sec, and 0.6 V / sec.

[0201] When calculating the integral value ITG of the cyclic voltammogram CVG, the predetermined potential interval V_ITV is, for example, [0 to 100 mV], [101 to 200 mV], [201 to 300 mV], ..., [2301 to 2400 mV], [2401 to 2500 mV], [0 to -100 mV], [-101 to -200 mV], ..., [-2301 to -2400 mV], [-2401 to -2500 mV].

[0202] FIG. 10 shows the cyclic voltammogram CVG in a predetermined potential section [V １ ~V ２ ] is an enlarged view of the portion indicated by the arrow.

[0203] Referring to FIG. 10, the calculation unit 213 calculates the voltage in the predetermined potential section [V １ ~V ２ ], when calculating the integral value ITG of the potential V １ The current value Iox_1 of the oxidation wave and the current value Ird_1 of the reduction wave are detected, and the difference between the current value Iox_1 and the current value Ird_1 (Iox_1-Ird_1) is calculated to obtain the potential V １ The intensity (Iox_1-Ird_1) of the cyclic voltammogram CVG is calculated.

[0204] Thereafter, the arithmetic unit 213 calculates the potential V １ The potential V is obtained by adding a unit potential (= 1 mV) to １ The oxidation wave current value Iox_2 and the reduction wave current value Ird_2 at +1 are detected, and the difference between the current value Iox_2 and the current value Ird_2 (=Iox_2−Ird_2) is calculated to obtain the potential V １ The intensity (Iox_2-Ird_2) of the cyclic voltammogram CVG at +1 is calculated.

[0205] Furthermore, the arithmetic unit 213 calculates the potential V １ +1 plus unit potential (=1 mV) V １ The oxidation wave current value Iox_3 and the reduction wave current value Ird_3 at +2 are detected, and the difference between the current value Iox_3 and the current value Ird_3 (Iox_3-Ird_3) is calculated to obtain the potential V １ The intensity (Iox_3-Ird_3) of the cyclic voltammogram CVG at +2 is calculated.

[0206] Similarly, the arithmetic unit 213 calculates the potential V ２ The potential V is calculated by adding a unit potential (= 1 mV) to -1. ２The current value Iox_N of the oxidation wave and the current value Ird_N of the reduction wave are detected, and the difference between the current value Iox_N and the current value Ird_N (Iox_N-Ird_N) is calculated to obtain the potential V ２ The intensity (Iox_N-Ird_N) of the cyclic voltammogram CVG is calculated.

[0207] Then, the calculation unit 213 calculates the voltage across the predetermined potential section [V １ ~V ２ ] is calculated.

[0208]

[0209] That is, the calculation unit 213 calculates the voltage in the predetermined potential section [V １ ~V ２ ], multiple intensities ((Iox_1-Ird_1), (Iox_2-Ird_2), ..., (Iox_N-Ird_N)) of the cyclic voltammogram CVG for each unit potential (=1 mV) are calculated, and the multiple calculated intensities ((Iox_1-Ird_1), (Iox_2-Ird_2), ..., (Iox_N-Ird_N)) are added to obtain a predetermined potential interval [V １ ~V ２ ], the integral value ITG of the cyclic voltammogram CVG is calculated.

[0210] Here, the predetermined potential section [V １ ~V ２ ], the potential V １ Calculating the difference (Iox_1-Ird_1) at the potential V １ +1, calculating the difference (Iox_2-Ird_2), the potential V １ +2, and calculating the difference (Iox_3-Ird_3) at the potential V ２ Calculating the difference (Iox_N-Ird_N) in this case corresponds to calculating a plurality of intensities in one predetermined potential section by performing a subtraction process of subtracting the current value of the reduction wave from the current value of the oxidation wave of a cyclic voltammogram at one unit potential in one predetermined potential section, and calculating the intensity of the cyclic voltammogram at one unit potential, for all unit potentials in one predetermined potential section.

[0211] Then, by the formula (2), the predetermined potential section [V １ ~V ２ Calculating the integral ITG in [ ] corresponds to calculating the sum of the calculated intensities as the area of ​​a cyclic voltammogram in one predetermined potential section.

[0212] In addition, the potential V １ Calculating the difference (Iox_1-Ird_1) at the potential V １ +1, calculating the difference (Iox_2-Ird_2), the potential V １ +2, and calculating the difference (Iox_3-Ird_3) at the potential V ２ Each calculation of the difference (Iox_N-Ird_N) corresponds to subtracting the current value of the reduction wave from the current value of the oxidation wave of the cyclic voltammogram at one unit potential in one predetermined potential interval to calculate the intensity of the cyclic voltammogram at one unit potential.

[0213] The calculation unit 213 calculates the potential in the predetermined potential range [V １ ~V ２ ], the sum SUM_Iox of the current values ​​Iox_1 to Iox_N of the N oxidation waves and the sum SUM_Ird of the current values ​​Ird_1 to Ird_N of the N reduction waves are calculated, and the sum SUM_Ird is subtracted from the sum SUM_Iox to obtain the predetermined potential section [V １ ~V ２ ], the integral value ITG of the cyclic voltammogram CVG may be calculated.

[0214] The calculation unit 213 calculates the voltage in the predetermined potential range [V １ ~V ２ ] by a method of calculating an integral value ITG of a cyclic voltammogram CVG in a plurality of predetermined potential intervals [0 to 100 mV], [101 to 200 mV], [201 to 300 mV], ..., [2301 to 2400 mV], [2401 to 2500 mV], [0 to -100 mV], [-101 to -200 mV], ..., [-2301 to -2400 mV], [-2401 to -2500 mV] １ , ITG２ , ITG ３ , ..., ITG ２４ , ITG ２５ , ITG ２６ , ITG ２７ , ..., ITG ４９ , ITG ５０ Calculate.

[0215] The above-described methods shown in FIGS. 9 and 10 are used to calculate the multiple integral values ​​ITG １ ~ITG ５０ By calculating the above, it is possible to correct the characteristic variations between sensors used to measure the cyclic voltammogram CVG, and to clearly show the difference in signal intensity between solutions of the object to be analyzed.

[0216] In the above formula (2), (Iox_1-Ird_1), (Iox_2-Ird_2), ..., (Iox_N-Ird_N) each represent the current value in the oxidation reaction and reduction reaction between the electrode (working electrode 112) and the object to be analyzed at each unit potential.

[0217] Furthermore, the sum (=integral value) of the subtraction results (Iox-Ird) in one specified potential section represents the total number of electrons in the oxidation reaction and reduction reaction between the electrode (working electrode) and the object to be analyzed in one specified potential section.

[0218] Furthermore, multiple integral values ​​ITG １ , ITG ２ , ITG ３ , ..., ITG ２４ , ITG ２５ , ITG ２６ , ITG ２７ , ..., ITG ４９ , ITG ５０ The curve CUR showing the class dependency of represents the dependency of the total number of electrons (=integral value) in the oxidation reaction and reduction reaction between the electrode (working electrode) and the analyte on a predetermined potential interval.

[0219] Fig. 11 shows a portion of a cyclic voltammogram. Fig. 11 shows a cyclic voltammogram having a region REG where the oxidation wave is located below the reduction wave. Fig. 11(a) shows a case where region REG is located in a positive current region, Fig. 11(b) shows a case where, in region REG, the reduction wave is located in a positive current region and the oxidation wave is located in a negative current region, and Fig. 11(c) shows a case where region REG is located in a negative current region.

[0220] Here, the current values ​​of the oxidation wave at the unit potential of region REG are Iox_1_REG, Iox_2_REG, ..., Iox_N'_REG (N' is the total number of unit potentials in region REG), and the current values ​​of the reduction wave at the unit potential of region REG are Ird_1_REG, Ird_2_REG, ..., Ird_N'_REG.

[0221] Referring to FIG. 11A, when calculating the integral value of a predetermined potential section in the region REG, the current value I ｏｘ＿１＿ＲＥＧ , I ｏｘ＿２＿ＲＥＧ , ..., I ｏｘ＿Ｎ’＿ＲＥＧ are the current values ​​I of the reduction wave, respectively. ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿ＲＥＧ Since it is smaller than the predetermined potential section in the region REG, the integral value ITG of the predetermined potential section becomes a negative value.

[0222] Referring to (b) of FIG. 11, when calculating the integral value of a predetermined potential section in the region REG, the current value I ｏｘ＿１＿ＲＥＧ , I ｏｘ＿２＿ＲＥＧ , ..., I ｏｘ＿Ｎ’＿ＲＥＧ are negative current values, and the reduction wave current value I ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿ＲＥＧ are positive current values, the integral ITG of the predetermined potential section in the region REG becomes a negative value.

[0223] Referring to (c) of FIG. 11, when calculating the integral value of a predetermined potential section in the region REG, the current value I ｏｘ＿１＿ＲＥＧ , I ｏｘ＿２＿ＲＥＧ , ..., I ｏｘ＿Ｎ’＿ＲＥＧ are negative current values, and the reduction wave current value I ｒｄ＿１＿ＲＥＧ, I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿Ｒ Each of these currents is a negative current value, and the oxidation current value I ｏｘ＿１＿ＲＥＧ , I ｏｘ＿２＿ＲＥＧ , ..., I ｏｘ＿Ｎ’＿ＲＥＧ Absolute value of |I ｏｘ＿１＿ＲＥＧ |, |I ｏｘ＿２＿ＲＥＧ |, ..., |I ｏｘ＿Ｎ’＿ＲＥＧ | are the current values ​​of the reduction waves, I ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿Ｒ Absolute value of |I ｒｄ＿１＿ＲＥＧ |, |I ｒｄ＿２＿ＲＥＧ |, ..., |I ｒｄ＿Ｎ’＿Ｒ |, the integral ITG of the predetermined potential section in the region REG becomes a negative value.

[0224] Therefore, in this embodiment of the present invention, the integral value of a predetermined potential section of a cyclic voltammogram having a region REG where the oxidation wave is located below the reduction wave becomes a negative value in the region REG.

[0225] The arithmetic unit 213 may calculate a plurality of integral values ​​in a plurality of predetermined potential intervals using a method different from the above-described method.

[0226] For example, the arithmetic unit 213 calculates a regression curve RC1 (the curve indicated by the solid line) showing the oxidation wave of the cyclic voltammogram CVG in FIG. 9 and a regression curve RC2 (the curve indicated by the dashed line) showing the reduction wave, using the potential V as an explanatory variable and the current I as a response variable, and calculates a regression curve RC2 (the curve indicated by the dashed line) showing the reduction wave of the cyclic voltammogram CVG in a predetermined potential interval [V １ -V ２ ], an integral value ITG_RC1 of the regression curve RC1 and an integral value ITG_RC2 of the regression curve RC2 are calculated, and the integral value ITG_RC2 is subtracted from the integral value ITG_RC1 to obtain a predetermined potential section [V １ -V ２ ] may be calculated for all predetermined potential intervals to calculate a plurality of integral values ​​in a plurality of predetermined potential intervals.

[0227] The arithmetic unit 213 may calculate the plurality of integral values ​​in the plurality of predetermined potential sections by any other method as long as the method can calculate the plurality of integral values ​​in the plurality of predetermined potential sections.

[0228] FIG. 12 is a diagram for explaining a method for creating a curve CUR that shows the relationship between a plurality of classes Cls and a plurality of integral values ​​ITG.

[0229] FIG. 12(a) shows one calculation data CAL ｕｎｉ 12(b) shows the curve CUR representing the relationship between the integral value and the class. ｕｎｉ Shows.

[0230] Referring to FIG. 12(a), the calculation data CAL ｕｎｉ is the name of the object to be analyzed ALY-Na ｕｎｉ and the type of analyte ALY-Kd ｕｎｉ , class Cls, and integral value ITG ＿Ｌｏｗ , ITG ＿Ｍｉｄｄｌｅ , ITG ＿Ｈｉｇｈ and the sum of the integrals ITG ＳＴ Includes:

[0231] Integrated value ITG ＿Ｌｏｗ is the potential scanning speed V ｒ＿Ｌｏｗ The integral ITG is calculated by the above-described method based on the cyclic voltammogram CVG measured by changing the potential V at a rate of 0.3 V / sec (for example, 0.3 V / sec). ＿Ｍｉｄｄｌｅ is the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ (e.g., 0.5 V / sec) based on the cyclic voltammogram CVG measured by changing the potential V, and the integral value ITG is calculated by the above-mentioned method. ＿Ｈｉｇｈ is the potential scanning speed V ｒ＿Ｈｉｇｈ The integral value is calculated by the above-described method based on the cyclic voltammogram CVG measured while changing the potential V at a rate of 0.6 V / sec (for example, 0.6 V / sec).

[0232] Class Cls is n class Cls １ ~Cls ｎ and the integral value ITG ＿Ｌｏｗ is n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗand the integral value ITG ＿Ｍｉｄｄｌｅ is n integral values ​​ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ and the integral value ITG ＿Ｈｉｇｈ is n integral values ​​ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ Here, n represents the total number of predetermined potential sections, and the potential scan range is [-V Ｓ１ ~ + V Ｓ２ ], and one predetermined potential section is V ＰＴＳ When n = (| - V Ｓ１ |+|+V Ｓ２ |) / V ＰＴＳ is.

[0233] n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ is the potential V shown in FIG. １＿Ｌｏｗ ~V ｄ＿Ｌｏｗ and the current value I １＿Ｌｏｗ ~I ｄ＿Ｌｏｗ The integral values ​​ITG are calculated by the above-mentioned method based on the n integral values ​​ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ is the potential V shown in FIG. １＿Ｍｉｄｄｌｅ ~V ｄ＿Ｍｉｄｄｌｅ and the current value I １＿Ｍｉｄｄｌｅ ~I ｄ＿Ｍｉｄｄｌｅ The integral values ​​ITG are calculated by the above-mentioned method based on the n integral values ​​ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ is the potential V shown in FIG. １＿Ｈｉｇｈ ~V ｄ＿Ｈｉｇｈ and the current value I １＿Ｈｉｇｈ ~I ｄ＿Ｈｉｇｈ is the integral value calculated by the above-mentioned method based on

[0234] Sum of integral values ​​ITG ＳＴ is the sum of n integral values ​​[ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ ]~[ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ ]consisting of

[0235] Here, the sum of n integral values ​​[ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ]~[ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ ] is the sum of n integral values ​​ITG_ ＳＭＴ１ ~ITG_ ＳＭＴｎ " is written as

[0236] n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ , n integral values ​​ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ , n integral values ​​ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ , and the sum of n integral values ​​ITG_ ＳＭＴ１ ~ITG_ ＳＭＴｎ are n classes Cls １ ~Cls ｎ can be associated with

[0237] The creation unit 215 creates one calculation data CAL ｕｎｉ and a signal S_u indicating that the number of pieces of calculation data is one, the calculation unit 213 determines that the number of pieces of calculation data CAL calculated by the calculation unit 213 is one based on the signal S_u.

[0238] The creating unit 215 then creates a set of classes (Cls １ , the sum of the integral values ​​ITG_ ＳＭＴ１ ) to the calculation data CAL ｕｎｉ Then, a set of (class Cls ２ , the sum of the integral values ​​ITG_ ＳＭＴ２ ) to the calculation data CAL ｕｎｉ Then, similarly, a set of (class Cls ｎ－１ , the sum of the integral values ​​ITG_ ＳＭＴｎ―１ ) to the calculation data CAL ｕｎｉ Detect from a set of (class Cls ｎ , the sum of the integral values ​​ITG_ ＳＭＴｎ ) to the calculation data CAL ｕｎｉ Detect from.

[0239] Then, the creation unit 215 creates a set of classes (Cls) on a graph with the horizontal axis representing the classes and the vertical axis representing the integral values. １ , the sum of the integral values ​​ITG_ ＳＭＴ１ ), one set of (class Cls２ , the sum of the integral values ​​ITG_ ＳＭＴ２ ), one set of (class Cls ３ , the sum of the integral values ​​ITG_ ＳＭＴ３ ), ..., a set of (Cls ｎ－２ , the sum of the integral values ​​ITG_ ＳＭＴｎ―２ ), one set of (class Cls ｎ－１ , the sum of the integral values ​​ITG_ ＳＭＴｎ―１ ) and a set of (classes Cls ｎ , the sum of the integral values ​​ITG ＿ＳＭＴｎ ) is plotted.

[0240] Then, the creation unit 215 creates a curve CURuni by connecting the n plotted points. In this case, the n plotted points are plotted for each class Cls, so the creation unit 215 creates a curve CURuni consisting of a smooth curve by connecting the n plotted points. ｕｎｉ can be created.

[0241] After plotting n points, the creation unit 215 uses the class Cls as an explanatory variable and calculates the sum of the integral values ​​ITG_ ＳＭＴ The curve CUR is obtained by calculating the regression curve using ｕｎｉ You may also create a

[0242] Then, the creation unit 215 creates the curve CUR ｕｎｉ is defined as an index curve, which is a curve that serves as an index when identifying the object to be analyzed.

[0243] The creation unit 215 also creates P calculation results CAL_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) from the calculation unit 213 and P calculation data CAL １ ~CAL Ｐ The determination unit 214 receives a determination result JDGR (the determination result shown in Table 1) indicating whether or not the two data are different.

[0244] Then, the creation unit 215 creates one calculation data CAL by the method described with reference to FIG. ｐ (p is any one of 1 to P) ＳＭＴCurve CUR showing the class dependency of ｐ P pieces of calculation data CAL are created. １ ~CAL Ｐ , and P curves CUR are obtained. １ ~CUR Ｐ (i.e., multiple curves CUR) are created.

[0245] In this case, P pieces of calculation data CAL １ ~CAL Ｐ (i.e., a plurality of pieces of calculation data) are each represented by the calculation data CAL shown in FIG. ｕｎｉ It has the same configuration as

[0246] In addition, the curve CUR ｕｎｉ is the sum of n integral values ​​ITG_ ＳＭＴ１ ~ITG_ ＳＭＴｎ ] of [n classes Cls １ ~Cls ｎ ] dependency. １ ~Cls ｎ Each of these curves consists of a predetermined potential section. ｕｎｉ is a curve that represents the dependency of the integral value on a predetermined potential section. Similarly, P curves CUR １ ~CUR Ｐ Each of the curves represents the dependency of the integral value on a predetermined potential interval.

[0247] The creation unit 215 creates one curve CUR ｕｎｉ When the calculation result CAL_RLS is created, ｕｎｉ Curve CUR ｕｎｉ Add the analysis result ALY_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] is created, and the created analysis result ALY_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] to the control unit 212.

[0248] On the other hand, the creation unit 215 creates P curves CUR １ ~CUR Ｐ When P calculation results CAL_RLS are created, １~CAL_RLS Ｐ P curves CUR １ ~CUR Ｐ Add P analysis results ALY_RLS １ = [ID １ / CAL １ / CUR １ ]~ALY_RLS Ｐ = [ID Ｐ / CAL Ｐ / CUR Ｐ ] and P analysis results ALY_RLS １ ~ ALY_RLS Ｐ The determination result (determination result shown in Table 1) is output to the control unit 212.

[0249] [Taste Diagnosis] (I) Taste Diagnosis of Shochu The calculation unit 216 calculates the index data IDX ｕｎｉ When it receives from the control unit 212, the calculation data CAL ｕｎｉ (See FIG. 12) as index data IDX ｕｎｉ Then, the calculation unit 216 calculates the calculation data CAL ｕｎｉ (See FIG. 12) to obtain the integral value ITG ＿Ｌｏｗ (ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ ), integral value ITG ＿Ｍｉｄｄｌｅ (ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ ) and the integral ITG ＿Ｈｉｇｈ (ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ) to detect.

[0250] FIG. 13 shows the potential scanning speed V ｒ＿Ｌｏｗ , V ｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ 1 is a conceptual diagram showing the class dependency of the integral value created based on a cyclic voltammogram measured while changing the temperature.

[0251] In FIG. 13(a), the potential scanning speed is “V ｒ＿Ｌｏｗ 13(b) is a conceptual diagram showing the class dependency of the integral value created based on the cyclic voltammogram measured by setting the potential scanning rate to "V ｒ＿Ｍｉｄｄｌｅ13(c) is a conceptual diagram showing the class dependency of the integral value created based on the cyclic voltammogram measured by setting the potential scanning rate to "V ｒ＿Ｈｉｇｈ 1 is a conceptual diagram showing the class dependency of the integral value created based on a cyclic voltammogram measured with the temperature set to ".

[0252] The arithmetic unit 216 calculates the integral value ITG ＿Ｌｏｗ (ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ ), x integral values ​​ITG in x (x is an integer satisfying x=INT(n / 2)) classes (see FIG. 13(a)) corresponding to a positive predetermined potential section are calculated. １＿Ｌｏｗ (+) ~ ITG ｘ＿Ｌｏｗ（＋） and the detected x integral values ​​ITG １＿Ｌｏｗ（＋） ~ITG ｘ＿Ｌｏｗ（＋） The sum of SMT ＿Ｌｏｗ（＋） Here, "INT(n / 2)" is the result of dividing n by 2 and converting it into an integer.

[0253] The calculation unit 216 also calculates the integral ITG ＿Ｍｉｄｄｌｅ (ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ ), x integral values ​​ITG in x classes (see FIG. 13(b)) corresponding to a predetermined positive potential section are calculated. １＿Ｍｉｄｄｌｅ（＋） ~ITG ｘ＿Ｍｉｄｄｌｅ（＋） and the detected x integral values ​​ITG １＿Ｍｉｄｄｌｅ（＋） ~ITG ｘ＿Ｍｉｄｄｌｅ（＋） The sum of SMT ＿Ｍｉｄｄｌｅ（＋） Calculate.

[0254] Furthermore, the arithmetic unit 216 calculates the integral value ITG ＿Ｈｉｇｈ (ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ), x integral values ​​ITG in x classes (see (c) of FIG. 13) corresponding to a predetermined positive potential section are calculated. １＿Ｈｉｇｈ（＋） ~ITG ｘ＿Ｈｉｇｈ（＋） and the detected x integral values ​​ITG １＿Ｈｉｇｈ（＋） ~ITG ｘ＿Ｈｉｇｈ（＋） The sum of SMT ＿Ｈｉｇｈ（＋） Calculate.

[0255] Then, the calculation unit 216 calculates the sum SMT＿Ｌｏｗ（＋） , Suwa SMT ＿Ｍｉｄｄｌｅ（＋） and Sum SMT ＿Ｈｉｇｈ（＋） Based on this, the Body Index (+) is calculated using the following formula:

[0256]

[0257] The Body Index (+) is a factor based on the diffusion coefficient of the components of the analyte when a positive potential is applied to the analyte.

[0258] The calculation unit 216 also calculates the integral ITG ＿Ｌｏｗ (ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ ), n integral values ​​ITG in n class sections (see (a) of FIG. 13) corresponding to n predetermined potential sections are calculated. １＿Ｌｏｗ（ａｌｌ） ~ITG ｎ＿Ｌｏｗ（ａｌｌ） and the detected n integral values ​​ITG １＿Ｌｏｗ（ａｌｌ） ~ITG ｎ＿Ｌｏｗ（ａｌｌ） The sum of SMT ＿Ｌｏｗ（ａｌｌ） Calculate.

[0259] Next, the arithmetic unit 216 calculates the integral value ITG ＿Ｍｉｄｄｌｅ (ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ ), n integral values ​​ITG in n class sections (see (b) of FIG. 13) corresponding to n predetermined potential sections are calculated. １＿Ｍｉｄｄｌｅ（ａｌｌ） ~ITG ｎ＿Ｍｉｄｄｌｅ（ａｌｌ） and the detected n integral values ​​ITG １＿Ｍｉｄｄｌｅ（ａｌｌ） ~ITG ｎ＿Ｍｉｄｄｌｅ（ａｌｌ） The sum of SMT ＿Ｍｉｄｄｌｅ（ａｌｌ） Calculate.

[0260] Furthermore, the arithmetic unit 216 calculates the integral value ITG ＿Ｈｉｇｈ (ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ), n integral values ​​ITG in n class sections (see (c) of FIG. 13) corresponding to n predetermined potential sections are calculated. １＿Ｈｉｇｈ（ａｌｌ） ~ITG ｎ＿Ｈｉｇｈ（ａｌｌ） and the detected n integral values ​​ITG １＿Ｈｉｇｈ（ａｌｌ） ~ITG ｎ＿Ｈｉｇｈ（ａｌｌ） The sum of SMT ＿Ｈｉｇｈ（ａｌｌ） Calculate.

[0261] Then, the calculation unit 216 calculates the sum SMT ＿Ｌｏｗ（ａｌｌ） , Suwa SMT ＿Ｍｉｄｄｌｅ（ａｌｌ） and Sum SMT ＿Ｈｉｇｈ（ａｌｌ） Based on this, the Body Index (all) is calculated using the following formula:

[0262]

[0263] The Body Index (all) is a factor based on the diffusion coefficients of the components of the analyte when a positive and a negative potential are applied to the analyte.

[0264] Furthermore, the calculation unit 216 calculates the sum SMT used to calculate the Body index (+). ＿Ｈｉｇｈ（＋） H(+)_sum(=SMT ＿Ｈｉｇｈ（＋） )

[0265] Furthermore, the arithmetic unit 216 calculates the integral value ITG ＿Ｈｉｇｈ (ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ), x integral values ​​ITG in x classes (see (c) of FIG. 13) corresponding to a predetermined negative potential section are calculated. １＿Ｈｉｇｈ（－） ~ITG ｘ＿Ｈｉｇｈ（－） and the detected x integral values ​​ITG １＿Ｈｉｇｈ（－） ~ITG ｘ＿Ｈｉｇｈ（－） The sum of SMT ＿Ｈｉｇｈ（－） is calculated as H(-)_sum.

[0266] Then, the calculation unit 216 calculates Body index (+), Body index (all), H(+)_sum (=SMT ＿Ｈｉｇｈ（＋） ) and H(-)_sum(=SMT ＿Ｈｉｇｈ（－） ) to the taste diagnosis unit 217.

[0267] The taste diagnostic unit 217 calculates Body Index (+), Body Index (all), H(+)_sum (=SMT ＿Ｈｉｇｈ（＋） ) and H(-)_sum(=SMT ＿Ｈｉｇｈ（－） ) from the arithmetic unit 216.

[0268] (1) Diagnosis of "Astringency" When the taste diagnosis unit 217 receives the Body Index (+) from the calculation unit 216, it substitutes the Body Index (+) into the following formula to calculate the result (=ASTG), and diagnoses the result as the astringency ASTG of the shochu being analyzed.

[0269]

[0270] In equation (5), the coefficient k １ is, for example, 1.5.

[0271] (2) Diagnosis of “Aftertaste” When the taste diagnosis unit 217 receives the Body index (all) from the calculation unit 216, it substitutes the Body index (all) into the following formula to calculate the result (=LNGS), and diagnoses the result as the aftertaste LNGS of the shochu, which is the object of analysis.

[0272]

[0273] In equation (6), the coefficient k ２ is, for example, 2.

[0274] (3) Diagnosis of "Sweetness" The taste diagnosis unit 217 receives H(+)_sum (=SMT ＿Ｈｉｇｈ（＋） ) and H(+)_sum (=SMT ＿Ｈｉｇｈ（＋） ) into the following formula, the calculation result (=SWT) is diagnosed as the "sweetness" of the shochu being analyzed.

[0275]

[0276] In equation (7), the coefficient k ３ is, for example, 3000, and the coefficient k ４ is, for example, 1.5.

[0277] (4) Diagnosis of "Aroma" The taste diagnosis unit 217 receives H(-)_sum (=SMT ＿Ｈｉｇｈ（－） ) is received, H(-)_sum(=SMT ＿Ｈｉｇｈ（－） ) into the following formula, the calculation result (=SCT) is diagnosed as the "aroma" of the shochu being analyzed.

[0278]

[0279] In equation (8), the coefficient k ５ is, for example, 2000, and the coefficient k ６ is, for example, 1.5.

[0280] (5) Diagnosis of “Bitterness” The taste diagnosis unit 217 substitutes the “astringency” (=ASTG in formula (5)) and “sweetness” (=SWT in formula (7)) diagnosed by the above-mentioned method into the following formula to calculate the calculation result (=BIT), and diagnoses the “bitterness” of the shochu, which is the object of analysis.

[0281]

[0282] In equation (9), the coefficient k ７ is, for example, 1.5, and the coefficient k ８ is, for example, 0.4.

[0283] The coefficient k in equation (5) １ , the coefficient k in Equation (6) ２ , the coefficient k in Eq. (7) ３ , k ４ , the coefficient k in Equation (8) ５ , k ６ , and the coefficient k in Eq. (9) ７ , k ８ is determined using a plurality of shochu samples with known astringency, aftertaste, sweetness, aroma, and bitterness as training data.

[0284] 14 is a conceptual diagram of the training data. Referring to FIG. 14, the training data is made up of training data TH1 to TH5. The training data TH1 to TH5 are stored in database 22 in advance.

[0285] The training data TH1 is the coefficient k １ The training data TH2 is training data for determining the value of the coefficient k ２ The training data TH3 is training data for determining the value of the coefficient k ３ , k ４ The training data TH4 is training data for determining the value of the coefficient k ５ , k ６ The training data TH5 is training data for determining the value of the coefficient k ７ , k８ This is training data for determining the value of

[0286] The taste diagnosis unit 217 calculates the coefficient k １ ~k ８ When determining the value of , the teacher data TH1 to TH5 are read out from the database 22.

[0287] (A) Coefficient k １ The training data TH1 includes astringency and a Body index (+). Astringency is determined by, for example, 10 a １ ~a １０ The Body index (+) is, for example, 10 b(+) １ ~b(+) １０ It consists of 10 a １ ~a １０ is known.

[0288] b(+) ｙ (y=1 to 10) is obtained as follows.

[0289] (i) The sensor device 1 scans the potential at a rate of V for one shochu y. ｒ＿Ｌｏｗ , V ｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ and the cyclic voltammogram CVG ＿Ｌｏｗ＿ｙ , CVG ＿Ｍｉｄｄｌｅ＿ｙ , CVG ＿Ｈｉｇｈ＿ｙ The measured cyclic voltammogram CVG ＿Ｌｏｗ＿ｙ , CVG ＿Ｍｉｄｄｌｅ＿ｙ , CVG ＿Ｈｉｇｈ＿ｙ is transmitted to a personal computer PC by wireless or wired communication.

[0290] (ii) The personal computer PC is used to measure cyclic voltammograms (CVG). ＿Ｌｏｗ＿ｙ Based on this, the correspondence between the class Cls and the integral value ITG [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｌｏｗ＿ｙ Get.

[0291] (iii) The personal computer PC is used to measure cyclic voltammograms (CVG). ＿Ｍｉｄｄｌｅ＿ｙBased on this, the correspondence between the class Cls and the integral value ITG [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｍｉｄｄｌｅ＿ｙ Get .

[0292] (iv) The personal computer PC is used to measure cyclic voltammograms (CVG). ＿Ｈｉｇｈ＿ｙ Based on this, the correspondence between the class Cls and the integral value ITG [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｈｉｇｈ＿ｙ Get.

[0293] (v) The personal computer PC determines the correspondence relationship [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｌｏｗ＿ｙ Based on this, (n / 2) integral values ​​corresponding to the class corresponding to the positive predetermined potential section are detected, and the sum of the detected (n / 2) integral values ​​is calculated to obtain [Sum of Low (+)] ＿ｙ Get.

[0294] (vi) The personal computer PC has a correspondence relationship [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｍｉｄｄｌｅ＿ｙ Based on this, (n / 2) integral values ​​corresponding to the class corresponding to the positive predetermined potential section are detected, and the sum of the detected (n / 2) integral values ​​is calculated to obtain [Middle (+) Sum] ＿ｙ Get.

[0295] (vii) The personal computer PC has a correspondence relationship [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｈｉｇｈ＿ｙBased on this, (n / 2) integral values ​​corresponding to the class corresponding to the positive predetermined potential section are detected, and the sum of the detected (n / 2) integral values ​​is calculated to obtain [Sum of High (+)] ＿ｙ Get.

[0296] (viii) The personal computer PC is the sum of [Low (+)] ＿ｙ , [Sum of Middle (+)] ＿ｙ and [Sum of High (+)] ＿ｙ respectively in the SMT of Equation (3). ＿Ｌｏｗ（＋） , S.M.T. ＿Ｍｉｄｄｌｅ（＋） and S.M.T. ＿Ｈｉｇｈ（＋） Substituting into b(+) ｙ Calculate.

[0297] (ix) The personal computer PC executes the above steps (i) to (viii) for all of y = 1 to 10 to obtain b(+) １ ~b(+) １０ Get.

[0298] (x) Personal computer PC is b(+) １ ~b(+) １０ When you get b(+) １ ~b(+) １０ a １ ~a １０ and stores it in the "Body index (+)" column of the teacher data TH1 in association with the above.

[0299] Then, the personal computer PC calculates the training data TH1 (a １ , b(+) １ ), (a ２ , b(+) ２ ), ..., (a ９ , b(+) ９ ), (a １０ , b(+) １０ Based on the above, a regression analysis was performed using "Body Index (+)" as an explanatory variable and "astringency" as a target variable to obtain a regression equation, and the value obtained by multiplying the explanatory variable "Body Index (+)" in the obtained regression equation was used as the coefficient k １ is determined as the value of

[0300] (B) Coefficient k ２The teacher data TH2 includes the aftertaste and the Body index (all). The aftertaste is, for example, 10 c １ ~c １０ The Body index (all) consists of, for example, 10 b(all) １ ~b(all) １０ It consists of 10 c １ ~c １０ is known.

[0301] b (all) ｙ (y=1 to 10) is obtained as follows.

[0302] (xi) The personal computer PC uses the correspondence relationship [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ ) ］＿Ｌｏｗ＿ｙ Based on this, all the integral values ​​ITG １ ~ITG ｎ Japanese SUM ａｌｌ＿Ｌｏｗ＿ｙ Calculate.

[0303] (xii) The personal computer PC uses the correspondence relationship [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｍｉｄｄｌｅ＿ｙ Based on this, all the integral values ​​ITG １ ~ITG ｎ Japanese SUM ａｌｌ＿Ｍｉｄｄｌｅ＿ｙ Calculate.

[0304] (xiii) The personal computer PC calculates the correspondence [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｈｉｇｈ＿ｙ Based on this, all the integral values ​​ITG １ ~ITG ｎ Japanese SUM ａｌｌ＿Ｈｉｇｈ＿ｙ Calculate.

[0305] (xiv) Personal computer PC is a Japanese SUM ａｌｌ＿Ｌｏｗ＿ｙ , sum sumａｌｌ＿Ｍｉｄｄｌｅ＿ｙ and Japanese SUM ａｌｌ＿Ｈｉｇｈ＿ｙ respectively in the SMT of Equation (4). ＿Ｌｏｗ（ａｌｌ） , S.M.T. ＿Ｍｉｄｄｌｅ（ａｌｌ） , S.M.T. ＿Ｈｉｇｈ（ａｌｌ） Substituting for b(all) ｙ Calculate.

[0306] (xv) The personal computer PC executes the above (xi) to (xiv) for all of y = 1 to 10 to obtain b(all). １ ~b(all) １０ Get.

[0307] (xvi) The personal computer PC is b(all) １ ~b(all) １０ When you get b(all), １ ~b(all) １０ c of the training data TH2 １ ~c １０ and stores it in the "Body index (all)" column of the training data TH2 in association with the above.

[0308] Then, the personal computer PC calculates the training data TH2 (c １ , b(all) １ ), (c ２ , b(all) ２ ), ..., (c ９ , b(all) ９ ), (c １０ , b(all) １０ ) based on the above, a regression analysis was performed using "Body Index (all)" as an explanatory variable and "aftertaste" as a response variable to obtain a regression equation, and the value obtained by multiplying the explanatory variable "Body Index (all)" in the obtained regression equation was used as the coefficient k ２ is determined as the value of

[0309] (C) Coefficient k ３ , k ４ The training data TH3 includes a sweetness and H(+)_sum. The sweetness is, for example, 10 d １ ~d １０ H(+)_sum is, for example, 10 h(+) １＿ｓｕｍ ~h(+) １０＿ｓｕｍ It consists of 10 d１ ~d １０ is known.

[0310] h(+) ｙ＿ｓｕｍ (y=1 to 10) is obtained as follows.

[0311] (xvii) The personal computer PC executes the above (vii) for all of y = 1 to 10 to obtain h(+) １＿ｓｕｍ ~h(+) １０＿ｓｕｍ Get.

[0312] (xviii) The personal computer PC is h(+) １＿ｓｕｍ ~h(+) １０＿ｓｕｍ When you get h(+) １＿ｓｕｍ ~h(+) １０＿ｓｕｍ are the teacher data TH3 d １ ~d １０ and stores it in the "H(+)_sum" column of the teacher data TH3 in association with the above.

[0313] Then, the personal computer PC calculates the training data TH3 (d １ , h(+) １＿ｓｕｍ ), (d ２ , h(+) ２＿ｓｕｍ ), ..., (d ９ , h(+) ９＿ｓｕｍ ), (d １０ , h(+) １０＿ｓｕｍ ) is used as an explanatory variable, and regression analysis is performed using "H(+)_sum" as a response variable and "sweetness" as a target variable to obtain a regression equation. In the obtained regression equation, the value by which the explanatory variable "H(+)_sum" is divided is used as the coefficient k ３ The value multiplied by the explanatory variable "H(+)_sum" is used as the coefficient k ４ is determined as the value of

[0314] (D) Coefficient k ５ , k ６ The training data TH4 includes the scent and H(-)_sum. The scent is, for example, 10 e １ ~e １０ H(-)_sum is, for example, 10 h(-) １＿ｓｕｍ ~h(-) １０＿ｓｕｍ It consists of 10 e１ ~e １０ is known.

[0315] h(-) ｙ＿ｓｕｍ (y=1 to 10) is obtained as follows.

[0316] (xix) The personal computer PC calculates the correspondence [(Cls １ -ITG １ ) ~ (Cls ｎ -ITG ｎ )] ＿Ｈｉｇｈ＿ｙ Based on this, (n / 2) integral values ​​corresponding to the class corresponding to the predetermined negative potential section are detected, and the sum of the detected (n / 2) integral values ​​is calculated to obtain h(-) ｙ＿ｓｕｍ Get.

[0317] (xx) The personal computer PC executes (xix) for all y = 1 to 10 to obtain h(-) １＿ｓｕｍ ~h(-) １０＿ｓｕｍ Get.

[0318] (xxi) The personal computer PC is h(-) １＿ｓｕｍ ~h(-) １０＿ｓｕｍ When you get h(-) １＿ｓｕｍ ~h(-) １０＿ｓｕｍ are the e of the teacher data TH4, respectively. １ ~e １０ is stored in "H(-)_sum" of the teacher data TH4 in association with the above.

[0319] Then, the personal computer PC calculates the (e １ , h(-) １＿ｓｕｍ ), (e ２ , h(-) ２＿ｓｕｍ ), ..., (e ９ , h(-) ９＿ｓｕｍ ), (e １０ , h(-) １０＿ｓｕｍ ), a regression analysis is performed using "H(-)_sum" as an explanatory variable and "aroma" as a response variable to obtain a regression equation, and the value by which the explanatory variable "H(-)_sum" is divided in the obtained regression equation is used as the coefficient k ５The value multiplied by the explanatory variable "H(-)_sum" is used as the coefficient k ６ is determined as the value of

[0320] (E) Coefficient k ７ , k ８ The training data TH5 includes “bitterness”, “astringency” and “sweetness”. “Bitterness” is, for example, determined by 10 f １ ~f １０ "Astringency" is a １ ~a １０ "Sweetness" is the value of d included in the training data TH3. １ ~d １０ It consists of:

[0321] (xxii) The personal computer PC executes a regression analysis using “astringency” and “sweetness” as explanatory variables and “bitterness” as a response variable to obtain a regression equation, and calculates the value multiplied by “astringency” (=ASTG) in the obtained regression equation as the coefficient k ７ The value multiplied by "sweetness" (= SWT) is used as the coefficient k ８ is determined as the value of

[0322] 15 is a conceptual diagram of a correspondence table showing the correspondence between coefficients and training data. Referring to FIG. 15, correspondence table TBL1 includes coefficients and training data. The coefficients and the training data are associated with each other.

[0323] The coefficient is k １ ~k ８ The teacher data consists of teacher data TH1 to TH5. The teacher data TH1 is a coefficient k １ The training data TH2 is associated with the coefficient k ２ The training data TH3 is associated with the coefficient k ３ , k ４ The training data TH4 is associated with the coefficient k ５ , k ６ The training data TH5 is associated with the coefficient k ７ , k ８ can be associated with

[0324] As a result, the correspondence table TBL1 contains the coefficient k １is determined based on the training data TH1, and the coefficient k ２ is determined based on the training data TH2, and the coefficient k ３ , k ４ is determined based on the training data TH3, and the coefficient k ５ , k ６ is determined based on the teacher data TH4, and the coefficient k ７ , k ８ is determined based on the training data TH5.

[0325] The personal computer PC calculates the coefficient k １ ~k ８ When the value of is determined, a correspondence table TBL1 is created and the created correspondence table TBL1 is transmitted to the diagnostic device 2.

[0326] The receiving unit 211 of the diagnostic device 2 receives the correspondence table TBL 1 from the personal computer PC, and outputs the received correspondence table TBL 1 to the control unit 212 .

[0327] When the control unit 212 receives the correspondence table TBL1 from the receiving unit 211, the control unit 212 stores the received correspondence table TBL1 in the database 22.

[0328] When diagnosing “astringency”, “aftertaste”, “sweetness”, “aroma” and “bitterness”, the taste diagnosis unit 217 uses the coefficient k １ ~k ８ The read coefficient k １ ~k ８ The "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" are assessed using the above-mentioned method.

[0329] Figure 16 is a conceptual diagram of updated teacher data. (a) of Figure 16 shows teacher data TH1_up1 obtained by updating teacher data TH1 shown in (a) of Figure 14. (b) of Figure 16 shows teacher data TH2_up1 obtained by updating teacher data TH2 shown in (b) of Figure 14. (c) of Figure 16 shows teacher data TH3_up1 obtained by updating teacher data TH3 shown in (c) of Figure 14. (d) of Figure 16 shows teacher data TH4_up1 obtained by updating teacher data TH4 shown in (d) of Figure 14. (e) of Figure 16 shows teacher data TH5_up1 obtained by updating teacher data TH5 shown in (e) of Figure 14.

[0330] In the teacher data TH1_up1 to TH5_up1, the "1" in "up1" represents the number of times the teacher data TH1 to TH5 have been updated.

[0331] Referring to (a) of FIG. 16, the teacher data TH1_up1 is obtained by adding “astringency a” to the teacher data TH1 shown in (a) of FIG. １＿ａｄｄ ~a ｖ＿ａｄｄ " and "Body index (+): b(+) １＿ａｄｄ ~b(+) ｖ＿ａｄｄ " is added. Here, v is an integer of 1 or more.

[0332] Referring to (b) of FIG. 16, the teacher data TH2_up1 is added to the teacher data TH2 shown in (b) of FIG. 14 by adding “resonance c １＿ａｄｄ ~c ｖ＿ａｄｄ " and "Body index (all): b (all) １＿ａｄｄ ~b(all) ｖ＿ａｄｄ " has been added.

[0333] Referring to (c) of FIG. 16, the teacher data TH3_up1 is added to the teacher data TH3 shown in (c) of FIG. 14 by adding “sweetness d １＿ａｄｄ ~d ｖ＿ａｄｄ " and "H(+)_sum:h(+) １＿ｓｕｍ＿ａｄｄ ~h(+) ｖ＿ｓｕｍ＿ａｄｄ " has been added.

[0334] Referring to (d) of FIG. 16, the teacher data TH4_up1 is added to the teacher data TH4 shown in (d) of FIG. 14 by adding “fragrance e １＿ａｄｄ ~e ｖ＿ａｄｄ " and "H(-)_sum:h(-)１＿ｓｕｍ＿ａｄｄ ~h(-) ｖ＿ｓｕｍ＿ａｄｄ " has been added.

[0335] Referring to (e) of FIG. 16, the teacher data TH5_up1 is added to the teacher data TH5 shown in (e) of FIG. 14 by adding “bitterness f １＿ａｄｄ ~f ｖ＿ａｄｄ ", "Astringency: a １＿ａｄｄ ~a ｖ＿ａｄｄ " and "Sweetness: d １＿ａｄｄ ~d ｖ＿ａｄｄ " has been added.

[0336] The taste diagnosis unit 217 calculates the coefficient k １ ~k ８ When the "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" are diagnosed using the above-mentioned method, the training data TH1 to TH5 are read out from the database 22.

[0337] Then, the taste diagnosis unit 217 judges the astringency １＿ａｄｄ ~a ｖ＿ａｄｄ " and "Body index (+): b(+) １＿ａｄｄ ~b(+) ｖ＿ａｄｄ " is added to the teacher data TH1, and the teacher data TH1 is updated to teacher data TH1_up1.

[0338] In addition, the taste diagnosis unit 217 １＿ａｄｄ ~c ｖ＿ａｄｄ " and "Body index (all): b (all) １＿ａｄｄ ~b(all) ｖ＿ａｄｄ " is added to the teacher data TH2, and the teacher data TH2 is updated to teacher data TH2_up1.

[0339] Furthermore, the taste diagnostic unit 217 can determine the sweetness １＿ａｄｄ ~d ｖ＿ａｄｄ " and "H(+)_sum:h(+) １＿ｓｕｍ＿ａｄｄ ~h(+) ｖ＿ｓｕｍ＿ａｄｄ " is added to the teacher data TH3, and the teacher data TH3 is updated to teacher data TH3_up1.

[0340] Furthermore, the taste diagnosis unit 217 is １＿ａｄｄ ~e ｖ＿ａｄｄ " and "H(-)_sum:h(-)１＿ｓｕｍ＿ａｄｄ ~h(-) ｖ＿ｓｕｍ＿ａｄｄ " is added to the teacher data TH4, and the teacher data TH4 is updated to teacher data TH4_up1.

[0341] Furthermore, the taste diagnosis unit 217 can determine the bitterness １＿ａｄｄ ~f ｖ＿ａｄｄ ", "Astringency: a １＿ａｄｄ ~a ｖ＿ａｄｄ " and "Sweetness: d １＿ａｄｄ ~d ｖ＿ａｄｄ " is added to the teacher data TH5, and the teacher data TH5 is updated to teacher data TH5_up1.

[0342] Then, the taste diagnosis unit 217 calculates the above-mentioned "(A) coefficient k １ The coefficient k is determined by the method described in "Determination of １ The value of is determined, and the coefficient k is added to the determined value. １ Update the value of

[0343] Furthermore, the taste diagnosis unit 217 calculates the above-mentioned "(B) coefficient k ２ The coefficient k is determined by the method described in "Determination of ２ The value of is determined, and the coefficient k is added to the determined value. ２ Update the value of

[0344] Furthermore, the taste diagnosis unit 217 calculates the above-mentioned “(C) coefficient k ３ , k ４ The coefficient k is determined by the method described in "Determination of ３ , k ４ The value of is determined, and the coefficient k is added to the determined value. ３ , k ４ Update the value of

[0345] Furthermore, the taste diagnosis unit 217 calculates the above-mentioned “(D) coefficient k ５ , k ６ The coefficient k is determined by the method described in "Determination of ５ , k ６ The value of is determined, and the coefficient k is added to the determined value. ５ , k ６Update the value of

[0346] Furthermore, the taste diagnosis unit 217 calculates the above-mentioned “(E) coefficient k ７ , k ８ The coefficient k is determined by the method described in "Determination of ７ , k ８ The value of is determined, and the coefficient k is added to the determined value. ７ , k ８ Update the value of

[0347] Then, the taste diagnosis unit 217 calculates the coefficient k １ ~k ８ When the values ​​of TH1_up1 to TH5_up1 are updated, the updated teacher data TH1_up1 to TH5_up1 are stored in the database 22.

[0348] FIG. 17 is a conceptual diagram of a correspondence table TBL1_up1 obtained by updating the correspondence table TBL1 shown in FIG.

[0349] Referring to FIG. 17, the correspondence table TBL1_up1 includes a coefficient k １＿ｕｐ１ ~k ８＿ｕｐ１ and teacher data TH1_up1 to TH5_up1. Coefficient k １＿ｕｐ１ ~k ８＿ｕｐ１ are updated coefficients, and the teacher data TH1_up1 to TH5_up1 are updated teacher data.

[0350] The training data TH1_up1 is a coefficient k １＿ｕｐ１ The training data TH2_up1 is associated with the coefficient k ２＿ｕｐ１ The teacher data TH3_up1 is associated with the coefficient k ３＿ｕｐ１ , k ４＿ｕｐ１ The teacher data TH4_up1 is associated with the coefficient k ５＿ｕｐ１ , k ６＿ｕｐ１ The teacher data TH5_up1 is associated with the coefficient k ７＿ｕｐ１ , k ８＿ｕｐ１ can be associated with

[0351] The taste diagnosis unit 217 calculates the coefficient k １ ~k ８ When the value of k is updated, the updated coefficient k １ ~k ８Based on the value of , the correspondence table TBL1 is updated to a correspondence table TBL1_up1, and the updated correspondence table TBL1_up1 is stored in the database 22.

[0352] Then, the taste diagnosis unit 217 calculates the coefficient k stored in the correspondence table TBL1_up1. １＿ｕｐ１ ~k ８＿ｕｐ１ The "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" of one or more new shochus are diagnosed using the above-mentioned method.

[0353] Thereafter, the taste diagnosis unit 217 updates the teacher data TH1 to TH5, and calculates the coefficient k using the updated teacher data TH1 to TH5. １ ~k ８ The values ​​of "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" of one or more new shochu samples are repeatedly updated.

[0354] (II) Grape Taste Diagnosis The calculation unit 216 calculates the calculation data CAL (= the calculation data CAL shown in FIG. 12 ｕｎｉ When the control unit 212 receives the calculation data CAL, the integral value ITG is calculated from the calculation data CAL. ＿Ｌｏｗ (ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ ), integral value ITG ＿Ｍｉｄｄｌｅ (ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ ) and the integral ITG ＿Ｈｉｇｈ (ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ) to detect.

[0355] Then, the calculation unit 216 calculates the class Cls １ ~Cls ｎ and the integral value ITG ＿Ｌｏｗ (ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ ) based on the threshold V ｔｈ A predetermined potential section (=class Cls ｔｈ ) corresponding to a plurality of integral values ​​ITG Ｕ＿Ｌｏｗ＿ｔｈ ~ITG ｎ＿Ｌｏｗ＿ｔｈ U is the threshold value V ｔｈClass Cls corresponding to a predetermined potential section including the potential V corresponding to ｔｈ Shows.

[0356] The calculation unit 216 also calculates the class Cls １ ~Cls ｎ and the integral value ITG ＿Ｍｉｄｄｌｅ (ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ ) based on the threshold V ｔｈ The following predetermined potential section (=class Cls ｔｈ ) corresponding to a plurality of integral values ​​ITG Ｕ＿Ｍｉｄｄｌｅ＿ｔｈ ~ITG ｎ＿Ｍｉｄｄｌｅ＿ｔｈ Detect.

[0357] Furthermore, the arithmetic unit 216 calculates the class Cls １ ~Cls ｎ and the integral value ITG ＿Ｈｉｇｈ (ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ) based on the threshold V ｔｈ A plurality of integral values ​​ITG corresponding to the following predetermined potential sections (=class Cls) Ｕ＿Ｈｉｇｈ＿ｔｈ ~ITG ｎ＿Ｈｉｇｈ＿ｔｈ Detect.

[0358] Multiple integral values ​​ITG Ｕ＿Ｌｏｗ＿ｔｈ ~ITG ｎ＿Ｌｏｗ＿ｔｈ , ITG Ｕ＿Ｍｉｄｄｌｅ＿ｔｈ ~ITG ｎ＿Ｍｉｄｄｌｅ＿ｔｈ , ITG Ｕ＿Ｈｉｇｈ＿ｔｈ ~ITG ｎ＿Ｈｉｇｈ＿ｔｈ The subscript "U" in ｔｈ Since it represents a predetermined potential section including the potential V corresponding to Ｕ＿Ｌｏｗ＿ｔｈ , ITG Ｕ＿Ｍｉｄｄｌｅ＿ｔｈ , ITG Ｕ＿Ｈｉｇｈ＿ｔｈ Each of these is a threshold V ｔｈ is the integral value in a predetermined potential section including the potential V corresponding to

[0359] The arithmetic unit 216 calculates a plurality of integral values ​​ITG Ｕ＿Ｌｏｗ＿ｔｈ ~ITG ｎ＿Ｌｏｗ＿ｔｈ , ITG Ｕ＿Ｍｉｄｄｌｅ＿ｔｈ ~ITG ｎ＿Ｍｉｄｄｌｅ＿ｔｈ , ITG Ｕ＿Ｈｉｇｈ＿ｔｈ ~ITG ｎ＿Ｈｉｇｈ＿ｔｈ When the detection is made, a plurality of integral values ​​ITGＵ＿Ｌｏｗ＿ｔｈ ~ITG ｎ＿Ｌｏｗ＿ｔｈ The sum L(-)_sum_th of the multiple integrals ITG Ｕ＿Ｍｉｄｄｌｅ＿ｔｈ ~ITG ｎ＿Ｍｉｄｄｌｅ＿ｔｈ The sum M(-)_sum_th of the multiple integrals ITG Ｕ＿Ｈｉｇｈ＿ｔｈ ~ITG ｎ＿Ｈｉｇｈ＿ｔｈ The sum H(-)_sum_th is calculated.

[0360] Then, the calculation unit 216 calculates the Body index (-)_th by substituting the sums of the integral values ​​L(-)_sum_th, M(-)_sum_th, and H(-)_sum_th into the following equation.

[0361]

[0362] After calculating Body index (−)_th, the arithmetic unit 216 outputs the calculated Body index (−)_th to the taste diagnosis unit 217 .

[0363] When the taste diagnosis unit 217 receives the Body index (-)_th from the calculation unit 216, it calculates the Body index (-)_th and the coefficient k ９ is substituted into the following formula to calculate the value ASTG_GRP of the "astringency" of the grapes, and the calculated value ASTG_GRP is diagnosed as the "astringency" of the grapes.

[0364]

[0365] In equation (11), the coefficient k ９ is, for example, 1.5.

[0366] Then, the taste diagnosis unit 217 calculates the coefficient k １ The coefficient k is determined in the same way as the value of ９ Determine the value of

[0367] 18 is a conceptual diagram for diagnosing the taste (astringency, aftertaste, sweetness, aroma, bitterness) of shochu. Referring to FIG. 18, the taste diagnosis unit 217 uses a plurality of shochus whose "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" are known as training data TH1 to TH5, and calculates the coefficient k １ ~k ８ The value of vle １＿０ ~vle８＿０ is determined (block BLK1).

[0368] Then, the taste diagnosis unit 217 calculates the coefficient k １ ~k ８ The value of vle １＿Ｑ ~vle ８＿Ｑ Using (Q = 0, 1, 2, ...), the v "astringency", v "aftertaste", v "sweetness", v "aroma" and v "bitterness" of the v shochu to be diagnosed are diagnosed using the method described above (block BLK2).

[0369] Thereafter, the taste diagnosis unit 217 sets Q=Q+1 (block BLK3), and updates the teacher data TH1_TH5 to teacher data TH1_up_Q to TH5_up_Q, respectively, based on the diagnosis results of the v shochus (block BLK4).

[0370] Subsequently, the taste diagnosis unit 217 uses the teacher data TH1_up_Q to TH5_up_Q to calculate the coefficient k １ ~k ８ The value of vle １＿Ｑ ~vle ８＿Ｑ Determine the coefficient k １ ~k ８ The value of vle １＿Ｑ－１ ~vle ８＿Ｑ－１ Each value is vle １＿Ｑ ~vle ８＿Ｑ (block BLK5).

[0371] After block BLK5, the taste diagnosis unit 217 repeatedly executes blocks BLK2 to BLK5.

[0372] When moving from block BLK1 to block BLK2, the coefficient k １ ~k ８ The value of vle １＿Ｑ ~vle ８＿Ｑ are the values ​​vle, respectively. １＿０ ~vle ８＿０ Therefore, the taste diagnosis unit 217 sets the coefficient k １ ~k ８ The value of vle １＿０ ~vle ８＿０The taste diagnosis unit 217 diagnoses the v "astringency", v "aftertaste", v "sweetness", v "aroma", and v "bitterness" of the v shochu samples to be diagnosed using the coefficients k １ ~k ８ The value of vle １＿０ ~vle ８＿０ is used to diagnose v "astringency", v "aftertaste", v "sweetness", v "aroma" and v "bitterness" of v shochu to be diagnosed.

[0373] After block BLK2, the taste diagnosis unit 217 sets Q=Q+1 (block BLK3), and then executes block BLK4.

[0374] In this case, since Q=Q+1=0+1=1 is set in block BLK3, the taste diagnosis unit 217 updates the teacher data TH1 to TH5 to teacher data TH1_up_1 to TH5_up_1, respectively, in block BLK4.

[0375] Then, in block BLK5, the taste diagnosis unit 217 calculates the coefficient k １ ~k ８ The value of vle １＿１ ~vle ８＿１ Determine the coefficient k １ ~k ８ The value of vle １＿０ ~vle ８＿０ Each value is vle １＿１ ~vle ８＿１ Update to.

[0376] After block BLK5, the taste diagnosis unit 217 calculates the coefficient k １ ~k ８ The value of vle １＿１ ~vle ８＿１Using the above-mentioned method, the taste diagnosis unit 217 diagnoses the v "astringency", v "aftertaste", v "sweetness", v "aroma", and v "bitterness" of the v Shochu to be diagnosed (Block BLK2). That is, in Block BLK2, the taste diagnosis unit 217 diagnoses the v "astringency", v "aftertaste", v "sweetness", v "aroma", and v "bitterness" of the v Shochu to be diagnosed (Block BLK2). １ ~k ８ The value of vle １＿１ ~vle ８＿１ is used to diagnose the v "astringency", v "aftertaste", v "sweetness", v "aroma" and v "bitterness" of the v shochu samples to be diagnosed.

[0377] After block BLK2, the taste diagnosis unit 217 sets Q=Q+1 (block BLK3), and then executes block BLK4.

[0378] In this case, since Q=Q+1=1+1=2 is set in block BLK3, the taste diagnosis unit 217 updates the teacher data TH1_up_1 to TH5_up_1 to teacher data TH1_up_2 to TH5_up_2, respectively, in block BLK4.

[0379] Then, in block BLK5, the taste diagnosis unit 217 calculates the coefficient k １ ~k ８ The value of vle １＿２ ~vle ８＿２ Determine the coefficient k １ ~k ８ The value of vle １＿１ ~vle ８＿１ Each value is vle １＿２ ~vle ８＿２ Update to.

[0380] After block BLK5, the taste diagnosis unit 217 calculates the coefficient k １ ~k ８ The value of vle １＿２ ~vle ８＿２Using the above-mentioned method, the taste diagnosis unit 217 diagnoses the v "astringency", v "aftertaste", v "sweetness", v "aroma", and v "bitterness" of the v Shochu to be diagnosed (Block BLK2). That is, in Block BLK2, the taste diagnosis unit 217 diagnoses the v "astringency", v "aftertaste", v "sweetness", v "aroma", and v "bitterness" of the v Shochu to be diagnosed (Block BLK2). １ ~k ８ The value of vle １＿２ ~vle ８＿２ is used to diagnose the v "astringency", v "aftertaste", v "sweetness", v "aroma" and v "bitterness" of the v shochu samples to be diagnosed.

[0381] After block BLK2, the taste diagnosis unit 217 sets Q=Q+1 (block BLK3), and then executes block BLK4.

[0382] In this case, since Q=Q+1=2+1=3 is set in block BLK3, the taste diagnosis unit 217 updates the teacher data TH1_up_2 to TH5_up_2 to teacher data TH1_up_3 to TH5_up_3, respectively, in block BLK4.

[0383] Thereafter, the taste diagnosis unit 217 repeatedly executes blocks BLK2 to BLK5.

[0384] Each time blocks BLK2 to BLK5 are executed, v "astringency" and v "Body index (+)" are added to teacher data TH1, v "aftertaste" and v "Body index (all)" are added to teacher data TH2, v "sweetness" and v "H(+)_sum" are added to teacher data TH3, v "aroma" and v "H(-)_sum" are added to teacher data TH4, and v "bitterness", v "astringency", and v "sweetness" are added to teacher data TH5.

[0385] Therefore, as the number of times that blocks BLK2 to BLK5 are repeatedly executed increases, the coefficient k determined by regression analysis in block BLK5 １ ~k ８ The value of vle １ ~vle ８The accuracy of the measurement can be improved.

[0386] In FIG. 18, when blocks BLK2 to BLK5 are executed for the gth time (g is an integer equal to or greater than 1), v=v Ｑ (v Ｑ is an integer of 1 or more), and blocks BLK2 to BLK5 are executed sequentially. When blocks BLK2 to BLK5 are executed for the (g+1)th time, v=v Ｒ (v Ｒ is an integer equal to or greater than 1, and v Ｑ In other words, the number of "shochu" diagnosed in the (g+1)th time (v = v Ｒ ) is the number of "shochu" diagnosed at the gth time (v = v Ｑ ) may be different from

[0387] Furthermore, when diagnosing the "astringency" of grapes, the taste diagnosis unit 217 executes the blocks BLK1 to BLK5 shown in FIG. 18 to obtain the teacher data and the coefficient k ９ The astringency of the grapes is diagnosed while updating the value.

[0388] In this case, the number of “grapes” diagnosed in the (g+1)th time (v = v Ｒ ) is the number of grapes diagnosed in the gth time (v = v Ｑ ) may be different from

[0389] Example A curve CUR created by the diagnostic device 2 will be described for a case where the analysis objects are shochu and grapes.

[0390] (1) Shochu The shochu for which the curve CUR was created is shown in Table 2.

[0391]

[0392] The measurement conditions for the cyclic voltammograms (CVG) for the shochu samples No. 1 to No. 20 shown in Table 2 are shown in Table 3.

[0393]

[0394] As shown in Table 3, the working electrode is made of diamond with a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scanning range when measuring the cyclic voltammogram is −2.5 V to +2.5 V, the predetermined potential interval when calculating the integral value (integral value extraction potential) is 18.1 mV, and the potential scanning rates are 300 mV / s, 500 mV / s, and 600 mV / s.

[0395] And, in the potential scanning rate, 300 mV / s is the potential scanning rate V ｒ＿Ｌｏｗ 500 mV / s corresponds to the potential scanning rate V ｒ＿Ｍｉｄｄｌｅ 600 mV / s corresponds to the potential scanning rate V ｒ＿Ｈｉｇｈ is equivalent to

[0396] 19 is a diagram showing the integrated spectra of the shochu samples No. 1 to No. 3 shown in Table 2.

[0397] 19, each of curves k1 to k3 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k1 represents the integrated spectrum of barley shochu sample No. 1, curve k2 represents the integrated spectrum of barley shochu sample No. 2, and curve k3 represents the integrated spectrum of barley shochu sample No. 3.

[0398] 20 is a diagram showing the integrated spectra of the shochu samples No. 4 to No. 6 shown in Table 2.

[0399] 20, each of curves k4 to k6 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k4 represents the integrated spectrum of barley shochu sample No. 4, curve k5 represents the integrated spectrum of barley shochu sample No. 5, and curve k6 represents the integrated spectrum of barley shochu sample No. 6.

[0400] 21 is a diagram showing the integrated spectra of the shochu samples No. 7 to No. 9 shown in Table 2.

[0401] 21, each of curves k7 to k9 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k7 represents the integrated spectrum of sweet potato shochu sample No. 7, curve k8 represents the integrated spectrum of sweet potato shochu sample No. 8, and curve k9 represents the integrated spectrum of sweet potato shochu sample No. 9.

[0402] 22 is a diagram showing the integrated spectra of the shochu samples No. 10 to No. 12 shown in Table 2.

[0403] 22, each of curves k10 to k12 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k10 represents the integrated spectrum of sweet potato shochu sample No. 10, curve k11 represents the integrated spectrum of sweet potato shochu sample No. 11, and curve k12 represents the integrated spectrum of sweet potato shochu sample No. 12.

[0404] 23 is a diagram showing the integrated spectra of the shochu samples No. 12 to No. 14 shown in Table 2.

[0405] 23, each of curves k12 to k14 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k12 represents the integrated spectrum of sweet potato shochu sample No. 12, curve k13 represents the integrated spectrum of sweet potato shochu sample No. 13, and curve k14 represents the integrated spectrum of sweet potato shochu sample No. 14.

[0406] 24 is a diagram showing the integral spectra of the shochu samples No. 15 and No. 16 shown in Table 2.

[0407] 24, curves k15 and k16 are curves CUR (index curves) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k15 represents the integral spectrum of rice shochu sample No. 15, and curve k16 represents the integral spectrum of rice shochu sample No. 16.

[0408] 25 is a diagram showing the integrated spectra of the shochu samples No. 17 to No. 20 shown in Table 2.

[0409] 25, each of curves k17 to k20 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 using the above-mentioned method. Curve k17 shows the integral spectrum of sample No. 17 shochu made with sake lees, curve k18 shows the integral spectrum of sample No. 18 barley shochu stored entirely for three years, entirely in barrels, and partially in cherry wood barrels, curve k19 shows the integral spectrum of sample No. 19 barley shochu stored entirely for 15 years, entirely made from koji barley, entirely in barrels, and with additional heat, and curve k20 shows sample No. 20 rice shochu stored entirely for three years and entirely in barrels.

[0410] 26 to 28 are first to third diagrams respectively showing the results of determining whether or not the curves k1 to k20 shown in FIGS. 19 to 25 are different from one another.

[0411] Figure 26 shows the results of determining whether ten curves k1 to k10 differ from one another, Figure 27 shows the results of determining whether ten curves k1 to k10 and ten curves k11 to k20 differ from one another, and Figure 28 shows the results of determining whether ten curves k11 to k20 differ from one another.

[0412] The determination of whether the 20 curves k1 to k20 are different from each other is performed by determining the number of combinations ( ２０ C ２ = 190) are different.

[0413] And two different curves CUR ｉ , C.U.R. ｊ The determination of whether (i≠j) is different is made by using the curve CUR ｉ Multiple integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ and curve CUR ｊ Multiple integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ Based on this, one class Cls ｋ The integral value ITGｋ＿ｉ and the integral value ITG ｋ＿ｊ Difference DF ｋ is calculated by equation (1) for all classes Cls １ ~Cls ｎ Execute for n differential DF １ ~DF ｎ Calculate the n calculated differences DF １ ~DF ｎ Standard deviation σ ＤＦｋ is the threshold σ ｔｈ This is done by determining whether the ratio is greater than (=6%).

[0414] Referring to FIG. 26, when two different curves are selected from the ten curves k1 to k10, the standard deviation σ of the difference between the 45 different curves is calculated. ＤＦｋ１，ｋ２ ~σDF ｋ９，ｋ１０ All of these are within the threshold σ ｔｈ (=6%).

[0415] Referring to FIG. 27, when two different curves are selected from ten curves k1 to k10 and ten curves k11 to k20, the standard deviation σ of the 100 differences between the two different curves is calculated. ＤＦｋ１，ｋ１１ ~σ ＤＦｋ１０，ｋ２０ All of these are within the threshold σ ｔｈ (=6%).

[0416] Referring to FIG. 28, when two different curves are selected from the ten curves k11 to k20, the standard deviation σ of the difference between the 45 different curves is calculated. ＤＦｋ１１，ｋ１２ ~σ ＤＦｋ１９，ｋ２０ All of these are within the threshold σ ｔｈ (=6%).

[0417] Therefore, when two different curves are selected from the 20 curves k1 to k20, the standard deviation σ of the 190 differences between the two different curves is ＤＦｋ１，ｋ２ ~σ ＤＦｋ１９，ｋ２０ All of these are within the threshold σ ｔｈ (=6%).

[0418] Therefore, the 20 curves k1 to k20 are mutually different. When it is determined that the 20 curves k1 to k20 are mutually different, the curves k1 to k20 are curves for uniquely identifying shochu No. 1 to shochu No. 20, respectively. The curves k1 to k20 are also fingerprints that represent feature quantities based on integrated values ​​for shochu No. 1 to shochu No. 20, respectively.

[0419] 29 is a diagram showing the results of assessing "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" for Shochu No. 1 to No. 20 shown in Table 2.

[0420] The values ​​of "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" for Shochu No. 1 to No. 20 shown in FIG. 29 are calculated by the taste diagnosis unit 217 using the coefficient k １ = 1.5, coefficient k ２ = 2, coefficient k ３ = 3000, coefficient k ４ = 1.5, coefficient k ５ = 2000, coefficient k ６ = 1.5, coefficient k ７ = 1.5, coefficient k ８ The results are those obtained by diagnosis using .DELTA..DELTA..DELTA..times ...

[0421] 30 is a diagram showing other diagnostic results of the "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" of Shochu No. 1 to No. 20 shown in Table 2.

[0422] The values ​​for "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" for shochu No. 1 to No. 20 shown in Figure 30 are the average values ​​of "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" assessed by seven people.

[0423] 31 is a diagram showing the differences between the respective values ​​of the diagnostic results for Shochu No. 1 to No. 20 shown in FIG. 29 and the respective values ​​of the diagnostic results for Shochu No. 1 to No. 20 shown in FIG. 30.

[0424] The differences between "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" shown in Figure 31 are the results of subtracting the values ​​of "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" shown in Figure 30 from the values ​​of "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" shown in Figure 29, respectively.

[0425] Therefore, in Figure 31, when the difference is a positive value, it indicates that the values ​​of "astringency", "lingering taste", "sweetness", "aroma" and "bitterness" diagnosed by the taste diagnosis unit 217 are greater than the values ​​of "astringency", "lingering taste", "sweetness", "aroma" and "bitterness" diagnosed by a human, and when the difference is a negative value, it indicates that the values ​​of "astringency", "lingering taste", "sweetness", "aroma" and "bitterness" diagnosed by the taste diagnosis unit 217 are smaller than the values ​​of "astringency", "lingering taste", "sweetness", "aroma" and "bitterness" diagnosed by a human.

[0426] 31, for shochu No. 1 to No. 20, the average value of the difference in "astringency" is 0.183, the average value of the difference in "aftertaste" is 0.192, the average value of the difference in "sweetness" is 0.134, the average value of the difference in "aroma" is 0.047, and the average value of the difference in "bitterness" is -0.165.

[0427] Furthermore, for Shochu No. 1 to No. 20, the standard deviation of the difference in "astringency" was 0.271, the standard deviation of the difference in "aftertaste" was 0.408, the standard deviation of the difference in "sweetness" was 0.646, the standard deviation of the difference in "aroma" was 0.393, and the standard deviation of the difference in "bitterness" was 0.419.

[0428] As a result, the average deviation of the "astringency", "lingering aftertaste", "sweetness", "aroma" and "bitterness" diagnosed by the taste diagnosis unit 217 from the "astringency", "lingering aftertaste", "sweetness", "aroma" and "bitterness" diagnosed by humans was 19.2% or less, and the variation from the average deviation was 0.646 or less.

[0429] Therefore, it was found that the taste diagnosis unit 217 can diagnose the "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" of shochu at the same level as a human being by the above-mentioned method.

[0430] (2) Grapes The grapes for which the curve CUR was created were Crimson Seedless (skin and flesh), Green Seedless (skin and flesh), Shine Muscat (skin and flesh), Crimson Seedless (flesh), Green Seedless (flesh), and Shine Muscat (flesh).

[0431] Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) are each the juice of grapes pressed with the skin and flesh together, while Crimson Seedless (flesh), Green Seedless (flesh), and Shine Muscat (flesh) are each the juice of grapes pressed with only the flesh.

[0432] The measurement conditions for the cyclic voltammograms CVG for Crimson Seedless (skin and flesh), Green Seedless (skin and flesh), Shine Muscat (skin and flesh), Crimson Seedless (flesh), Green Seedless (flesh), and Shine Muscat (flesh) were the same as those shown in Table 3.

[0433] FIG. 32 is a diagram showing the integrated spectra for Crimson Seedless (skin+flesh), Green Seedless (skin+flesh), and Shine Muscat (skin+flesh).

[0434] 32, each of curves k21 to k23 is a curve CUR (index curve) created by the above-described method by diagnostic device 2. Curve k21 represents the integral spectrum of Crimson Seedless (skin and flesh), curve k22 represents the integral spectrum of Green Seedless (skin and flesh), and curve k23 represents the integral spectrum of Shine Muscat (skin and flesh).

[0435] FIG. 33 is a diagram showing the integrated spectra of Crimson Seedless (pulp only), Green Seedless (pulp only), and Shine Muscat (pulp only).

[0436] 33, each of curves k24 to k26 is a curve CUR (index curve) created by the above-described method by diagnostic device 2. Curve k24 represents the integral spectrum of Crimson Seedless (pulp only), curve k25 represents the integral spectrum of Green Seedless (pulp only), and curve k26 represents the integral spectrum of Shine Muscat (pulp only).

[0437] FIG. 34 is a diagram showing the results of determining whether the curves k21 to k26 shown in FIGS. 32 and 33 are different from each other.

[0438] Whether or not the curves k21 to k26 are different from one another is determined by determining whether or not two different curves among the curves k21 to k26 are different for all combinations of two different curves among the curves k21 to k26.

[0439] There are 15 possible combinations of two different curves out of the six curves k21 to k26: (k21, k22), (k21, k23), (k21, k24), (k21, k25), (k21, k26), (k22, k23), (k22, k24), (k22, k25), (k22, k26), (k23, k24), (k23, k25), (k23, k26), (k24, k25), (k24, k26), and (k25, k26).

[0440] 34 , the standard deviation σDF_k21,k22 of the differences between the multiple integral values ​​on the curve k21 and the multiple integral values ​​on the curve k22 is σDF_k21,k22=58.9%, the standard deviation σDF_k21,k23 of the differences between the multiple integral values ​​on the curve k21 and the multiple integral values ​​on the curve k23 is σDF_k21,k23=31.2%, the standard deviation σDF_k21,k24 of the differences between the multiple integral values ​​on the curve k21 and the multiple integral values ​​on the curve k24 is σDF_k21,k24=62.0%, and the standard deviation σDF_k21,k22 of the differences between the multiple integral values ​​on the curve k21 and the multiple integral values ​​on the curve k25 is σDF_k21,k24=62.0%. ＤＦ＿２１，ｋ２５is σDF_k21,k25=24.1%, and the standard deviation σDF_k21,k26 of the difference between the integral value of the number on the curve k21 and the multiple integral values ​​on the curve k26 is σDF_k21,k26=62.5%.

[0441] Furthermore, the standard deviation σDF_k22,k23 of the difference between the multiple integral values ​​on curve k22 and the multiple integral values ​​on curve k23 is σDF_k22,k23 = 65.3%, the standard deviation σDF_k22,k24 of the difference between the multiple integral values ​​on curve k22 and the multiple integral values ​​on curve k24 is σDF_k22,k24 = 16.0%, the standard deviation σDF_k22,k25 of the difference between the multiple integral values ​​on curve k22 and the multiple integral values ​​on curve k25 is σDF_k22,k25 = 16.0%, and the standard deviation σDF_k22,k26 of the difference between the multiple integral values ​​on curve k22 and the multiple integral values ​​on curve k26 is σDF_k22,k26 = 23.0%.

[0442] Furthermore, the standard deviation σDF_k23,k24 of the difference between the multiple integral values ​​on curve k23 and the multiple integral values ​​on curve k24 is σDF_k23,k24 = 30.1%, the standard deviation σDF_k23,k25 of the difference between the multiple integral values ​​on curve k23 and the multiple integral values ​​on curve k25 is σDF_k23,k25 = 31.4%, and the standard deviation σDF_k23,k26 of the difference between the multiple integral values ​​on curve k23 and the multiple integral values ​​on curve k26 is σDF_k23,k26 = 24.9%.

[0443] Furthermore, the standard deviation σDF_k24,k25 of the difference between the multiple integral values ​​on curve k24 and the multiple integral values ​​on curve k25 is σDF_k24,k24 = 30.3%, and the standard deviation σDF_k24,k26 of the difference between the multiple integral values ​​on curve k24 and the multiple integral values ​​on curve k26 is σDF_k24,k26 = 22.9%.

[0444] Furthermore, the standard deviation σDF_k25,k26 of the differences between the multiple integral values ​​on the curve k25 and the multiple integral values ​​on the curve k26 is σDF_k25,k26=24.3%.

[0445] As a result, the standard deviation of the differences σDF_k21,k22 (=58.9%), the standard deviation of the differences σDF_k21,k23 (=31.2%), the standard deviation of the differences σDF_k21,k24 (=62.0%), the standard deviation of the differences σDF_k21,k25 (=24.1%), the standard deviation of the differences σDF_k21,k26 (=62.5%), the standard deviation of the differences σDF_k22,k23 (=65.3%), the standard deviation of the differences σDF_k22,k24 (=16.0%), the standard deviation of the differences σDF_k22,k2 5 (=16.0%), the standard deviation of differences σDF_k22,k26 (=23.0%), the standard deviation of differences σDF_k23,k24 (=30.1%), the standard deviation of differences σDF_k23,k25 (=31.4%), the standard deviation of differences σDF_k23,k26 (=24.9%), the standard deviation of differences σDF_k24,k25 (=30.3%), the standard deviation of differences σDF_k24,k26 (=22.9%), and the standard deviation of differences σDF_k25,k26 (=24.3%) are all within the threshold σ ｔｈ (=15%).

[0446] Therefore, the two curves k21 and k22 are different, the two curves k21 and k23 are different, the two curves k21 and k24 are different, the two curves k21 and k25 are different, the two curves k21 and k26 are different, the two curves k22 and k23 are different, the two curves k22 and k24 are different, the two curves k22 and k25 are different, the two curves k22 and k26 are different, the two curves k23 and k24 are different, the two curves k23 and k25 are different, the two curves k23 and k26 are different, the two curves k24 and k25 are different, the two curves k24 and k26 are different, and the two curves k25 and k26 are different. Therefore, the curves k21 to k26 are mutually different curves.

[0447] When it is determined that the six curves k21 to k26 are different from one another, the curves k21 to k26 are curves for uniquely identifying Crimson Seedless (flesh and skin), Green Seedless (flesh and skin), Shine Muscat (flesh and skin), Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only), respectively. The curves k21 to k26 are fingerprints representing feature quantities based on integrated values ​​for Crimson Seedless (flesh and skin), Green Seedless (flesh and skin), Shine Muscat (flesh and skin), Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only), respectively.

[0448] FIG. 35 shows the results of the diagnosis of "astringency" for Green Seedless (flesh only), Crimson Seedless (flesh only), Shine Muscat (flesh only), Green Seedless (flesh and skin), Crimson Seedless (flesh and skin), and Shine Muscat (flesh and skin).

[0449] The "saturation" shown in FIG. 35 is the threshold value V ｔｈ is set to "-1362 mV" and L(-)_sum_th, M(-)_sum_th, and H(-)_sum_th are calculated based on the integral values ​​in a predetermined potential range (=class) in the range of -1362 mV to -2501 mV.

[0450] Referring to Figure 35, the astringency of green seedless (flesh only) is 2.27, and the astringency of green seedless (flesh + skin) is 2.58.

[0451] The astringency of Crimson Seedless (flesh only) is 2.96, and the astringency of Crimson Seedless (flesh and skin) is 4.65.

[0452] Furthermore, the astringency of Shine Muscat (flesh only) is 2.06, and the astringency of Shine Muscat (flesh and skin) is 2.47.

[0453] As a result, the "astringency" value increases in the following order: Shine Muscat (flesh only), Green Seedless (flesh only), Shine Muscat (flesh + skin), Green Seedless (flesh + skin), Crimson Seedless (flesh only), and Crimson Seedless (flesh + skin).

[0454] In addition, the astringency of Green Seedless (flesh only) is less than that of Green Seedless (flesh + skin), the astringency of Crimson Seedless (flesh only) is less than that of Crimson Seedless (flesh + skin), and the astringency of Shine Muscat (flesh only) is less than that of Shine Muscat (flesh + skin).

[0455] Therefore, the astringency of Green Seedless (pulp only), Crimson Seedless (pulp only), and Shine Muscat (pulp only), which are juices made by pressing grape flesh, is less than the astringency of Green Seedless (pulp + skin), Crimson Seedless (pulp + skin), and Shine Muscat (pulp + skin), which are juices made by pressing grape flesh and skin.

[0456] Therefore, by diagnosing the astringency of grapes using the taste diagnosis unit 217, it was found that the grape skin is a factor that increases the astringency.

[0457] [Relationship between Taste Diagnosis and Number of Integral Values ​​for Shochu] (I) Number of integral values: 138 Figure 36 shows the integral spectrum for Shochu samples No. 1 to No. 3 shown in Table 2 when the number of integral values ​​is 138.

[0458] 36, each of curves k27 to k29 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k27 represents the integrated spectrum of barley shochu sample No. 1, curve k28 represents the integrated spectrum of barley shochu sample No. 2, and curve k29 represents the integrated spectrum of barley shochu sample No. 3.

[0459] 37 is a diagram showing the integral spectrum of the shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 138.

[0460] 37, each of curves k30 to k32 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k30 represents the integrated spectrum of barley shochu sample No. 4, curve k31 represents the integrated spectrum of barley shochu sample No. 5, and curve k32 represents the integrated spectrum of barley shochu sample No. 6.

[0461] 38 is a diagram showing the integral spectrum of the shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 138.

[0462] 38, each of curves k33 to k35 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k33 represents the integrated spectrum of sweet potato shochu sample No. 7, curve k34 represents the integrated spectrum of sweet potato shochu sample No. 8, and curve k35 represents the integrated spectrum of sweet potato shochu sample No. 9.

[0463] 39 is a diagram showing the integral spectrum of the shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 138.

[0464] 39, each of curves k36 to k38 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k36 represents the integrated spectrum of sweet potato shochu sample No. 10, curve k37 represents the integrated spectrum of sweet potato shochu sample No. 11, and curve k38 represents the integrated spectrum of sweet potato shochu sample No. 12.

[0465] 40 is a diagram showing the integral spectrum of the shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 138.

[0466] 40, each of curves k38 to k40 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k38 represents the integrated spectrum of sweet potato shochu sample No. 12, curve k39 represents the integrated spectrum of sweet potato shochu sample No. 13, and curve k40 represents the integrated spectrum of sweet potato shochu sample No. 14.

[0467] 41 shows the integral spectrum of the shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 138.

[0468] 41, curves k41 and k42 are curves CUR (index curves) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k41 represents the integral spectrum of rice shochu sample No. 15, and curve k42 represents the integral spectrum of rice shochu sample No. 16.

[0469] 42 is a diagram showing the integral spectrum of the shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 138.

[0470] 42, each of curves k43 to k46 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 using the above-mentioned method. Curve k43 shows the integral spectrum of sample No. 17, shochu made with sake lees, curve k44 shows the integral spectrum of sample No. 18, barley shochu stored entirely for three years, entirely in barrels, and partially in cherry wood barrels, curve k45 shows the integral spectrum of sample No. 19, barley shochu stored entirely for 15 years, entirely made from koji barley, entirely in barrels, and with additional heat, and curve k46 shows sample No. 20, rice shochu stored entirely for three years and entirely in barrels.

[0471] 36 to 42, each of class 1 to class 138 is made up of a predetermined potential section (integral value extraction potential) of 36.2 mV. Classes 1 to 69 are classes in the positive predetermined potential section, and classes 70 to 138 are classes in the negative predetermined potential section.

[0472] As a result, an integral spectrum (curve CUR similar to curves k27 to k46) was obtained in which all classes were in a predetermined potential range (integral extraction potential) of 36.2 mV. ＿Ｌｏｗ＿ｋ２７ ~CUR ＿Ｌｏｗ＿ｋ４６ , C.U.R. ＿Ｍｉｄｄｌｅ＿ｋ２７ ~CUR ＿Ｍｉｄｄｌｅ＿ｋ４６ , C.U.R. ＿Ｈｉｇｈ＿ｋ２７ ~CUR ＿Ｈｉｇｈ＿ｋ４６ ) based on the sum of the integral values ​​in the positive predetermined potential section SMT_ Ｌｏｗ（＋） , SMT_ Ｍｉｄｄｌｅ（＋） , S.M.T. ＿Ｈｉｇｈ（＋） Therefore, an integral spectrum (curve CUR similar to curves k27 to k46) consisting of a predetermined potential interval (integral extraction potential) of 36.2 mV can be calculated. ＿Ｌｏｗ＿ｋ２７ ~CUR ＿Ｌｏｗ＿ｋ４６ , C.U.R. ＿Ｍｉｄｄｌｅ＿ｋ２７ ~CUR ＿Ｍｉｄｄｌｅ＿ｋ４６ , C.U.R. ＿Ｈｉｇｈ＿ｋ２７ ~CUR ＿Ｈｉｇｈ＿ｋ４６ ) and calculate the Body Index (+) based on the above. The astringency ASTG of the shochu can be diagnosed by the formula (5). All classes are shown in the integral spectrum (curve CUR similar to curves k27 to k46) consisting of a predetermined potential range (integral extraction potential) of 36.2 mV. ＿Ｌｏｗ＿ｋ２７ ~CUR ＿Ｌｏｗ＿ｋ４６ , C.U.R. ＿Ｍｉｄｄｌｅ＿ｋ２７ ~CUR ＿Ｍｉｄｄｌｅ＿ｋ４６ , C.U.R. ＿Ｈｉｇｈ＿ｋ２７ ~CUR ＿Ｈｉｇｈ＿ｋ４６ ) based on H(+)_sum(=SMT_ Ｈｉｇｈ（＋） ) and the sweetness SWT of shochu can be diagnosed by equation (7). All classes are represented by an integral spectrum (curve CUR similar to curves k27 to k46) consisting of a predetermined potential range (integral extraction potential) of 36.2 mV. ＿Ｌｏｗ＿ｋ２７ ~CUR ＿Ｌｏｗ＿ｋ４６ , C.U.R. ＿Ｍｉｄｄｌｅ＿ｋ２７ ~CUR ＿Ｍｉｄｄｌｅ＿ｋ４６ , C.U.R. ＿Ｈｉｇｈ＿ｋ２７ ~CUR ＿Ｈｉｇｈ＿ｋ４６ ) based on H(-)_sum(=SMT_ Ｈｉｇｈ（－） ) and the aroma SCT of the shochu can be diagnosed using equation (8), and as a result, the bitterness BIT of the shochu can be diagnosed using equation (9).

[0473] 43 to 45 are first to third diagrams respectively showing the results of determining whether or not the curves k27 to k46 shown in FIGS. 36 to 42 are different from one another.

[0474] Figure 43 shows the results of determining whether ten curves k27 to k36 differ from one another, Figure 44 shows the results of determining whether ten curves k27 to k36 and ten curves k37 to k46 differ from one another, and Figure 45 shows the results of determining whether ten curves k37 to k46 differ from one another.

[0475] The determination of whether the 20 curves k27 to k46 are different from each other is performed by determining the number of combinations ( ２０ C ２ = 190) are different.

[0476] Referring to FIG. 43, when two different curves are selected from the ten curves k27 to k36, all of the 45 standard deviations σDF_k27, k28 to σDF_k35, k36 of the differences between the 45 different curves are below the threshold value σ ｔｈ (=5%).

[0477] Referring to FIG. 44, when two different curves are selected from the ten curves k27 to k36 and the ten curves k37 to k46, all of the 100 standard deviations σDF_k27, k37 to σDF_k36, k46 of the differences for all 100 different two curves are below the threshold σ ｔｈ (=5%).

[0478] Referring to FIG. 45, when two different curves are selected from the ten curves k37 to k46, all of the 45 standard deviations σDF_k37, k38 to σDF_k45, k46 of the differences for all 45 different curves are below the threshold value σ ｔｈ (=5%).

[0479] Therefore, when two different curves are selected from the 20 curves k27 to k46, all of the 190 standard deviations σDF_k27, k28 to σDF_k45, k46 of the differences for all 190 different curves are within the threshold value σ ｔｈ (=5%).

[0480] Therefore, the 20 curves k27 to k46 are mutually different. When the 20 curves k27 to k46 are determined to be mutually different, the curves k27 to k46 are curves for uniquely identifying shochu No. 1 to No. 20, respectively. When the number of integral values ​​is 138, the curves k27 to k46 are fingerprints representing the feature quantities based on the integral values ​​for shochu No. 1 to No. 20, respectively. (II) Number of integral values: 70 Figure 46 shows the integral value spectra for shochu samples No. 1 to No. 3 shown in Table 2 when the number of integral values ​​is 70.

[0481] 46, each of curves k47 to k49 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k47 represents the integrated spectrum of barley shochu sample No. 1, curve k48 represents the integrated spectrum of barley shochu sample No. 2, and curve k49 represents the integrated spectrum of barley shochu sample No. 3.

[0482] 47 is a diagram showing the integral spectrum of the shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 70.

[0483] 47, each of curves k50 to k52 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k50 represents the integrated spectrum of barley shochu sample No. 4, curve k51 represents the integrated spectrum of barley shochu sample No. 5, and curve k52 represents the integrated spectrum of barley shochu sample No. 6.

[0484] 48 is a diagram showing the integral spectrum of the shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 70.

[0485] 48, each of curves k53 to k55 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k53 represents the integrated spectrum of sweet potato shochu sample No. 7, curve k54 represents the integrated spectrum of sweet potato shochu sample No. 8, and curve k55 represents the integrated spectrum of sweet potato shochu sample No. 9.

[0486] 49 is a diagram showing the integral spectrum of the shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 70.

[0487] 49, each of curves k56 to k58 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k56 represents the integrated spectrum of sweet potato shochu sample No. 10, curve k57 represents the integrated spectrum of sweet potato shochu sample No. 11, and curve k58 represents the integrated spectrum of sweet potato shochu sample No. 12.

[0488] 50 is a diagram showing the integral spectrum of the shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 70.

[0489] 50, each of curves k58 to k60 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k58 represents the integrated spectrum of sweet potato shochu sample No. 12, curve k59 represents the integrated spectrum of sweet potato shochu sample No. 13, and curve k60 represents the integrated spectrum of sweet potato shochu sample No. 14.

[0490] 51 is a diagram showing the integral spectrum of the shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 70.

[0491] 51, curves k61 and k62 are curves CUR (index curves) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k61 represents the integrated spectrum of rice shochu sample No. 15, and curve k62 represents the integrated spectrum of rice shochu sample No. 16.

[0492] 52 is a diagram showing the integral spectrum of the shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 70.

[0493] 52, each of curves k63 to k66 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 using the above-mentioned method. Curve k63 shows the integral spectrum of sample No. 17, shochu made with sake lees, curve k64 shows the integral spectrum of sample No. 18, barley shochu stored entirely for three years, entirely in barrels, and partially in cherry wood barrels, curve k65 shows the integral spectrum of sample No. 19, barley shochu stored entirely for 15 years, entirely made from koji barley, entirely in barrels, and with additional heat, and curve k66 shows sample No. 20, rice shochu stored entirely for three years and entirely in barrels.

[0494] 46 to 52, each of classes 1 to 34 consists of a predetermined potential section (integral value extraction potential) of 72.4 mV, class 35 consists of a predetermined potential section (integral value extraction potential) of 36.2 mV, each of classes 36 to 69 consists of a predetermined potential section (integral value extraction potential) of 72.4 mV, and class 70 consists of a predetermined potential section (integral value extraction potential) of 36.2 mV. Classes 1 to 35 are classes in positive potential sections, and classes 36 to 70 are classes in negative potential sections.

[0495] The reason why class 35 has a predetermined potential range (integral value extraction potential) of 36.2 mV, which is smaller than the predetermined potential range (integral value extraction potential) of 72.4 mV in each of classes 1 to 34, is to make class 35 the "last class in the positive predetermined potential range."

[0496] That is, when class 35 is composed of the same predetermined potential section (integral value extraction potential) as the predetermined potential section (integral value extraction potential) of 72.4 mV in each of classes 1 to 34, the integral value in class 35 is the sum of the integral value in the positive predetermined potential section and the integral value in the negative predetermined potential section, and based on the integral value spectrum in which all classes are composed of the predetermined potential section (integral value extraction potential) of 72.4 mV, the sum SMT_ Ｌｏｗ（＋） , SMT_ Ｍｉｄｄｌｅ（＋） , SMT_ Ｈｉｇｈ（＋） Therefore, it is not possible to calculate the Body Index (+) based on the integral spectrum in which all classes are in the predetermined potential range (integral extraction potential) of 72.4 mV, and to diagnose the astringency ASTG of shochu using equation (5), and it is not possible to calculate H(+)_sum (=SMT_ Ｈｉｇｈ（＋） ) and the sweetness SWT of shochu cannot be diagnosed by equation (7), and H(-)_sum (=SMT_ Ｈｉｇｈ（－） ) and it becomes impossible to diagnose the aroma SCT of shochu using equation (8), and as a result, it becomes impossible to diagnose the bitterness BIT of shochu using equation (9).

[0497] 53 to 55 are first to third diagrams respectively showing the results of determining whether or not the curves k47 to k66 shown in FIGS. 46 to 52 are different from one another.

[0498] Figure 53 shows the results of determining whether ten curves k47 to k56 differ from one another, Figure 54 shows the results of determining whether ten curves k47 to k56 and ten curves k57 to k66 differ from one another, and Figure 55 shows the results of determining whether ten curves k57 to k66 differ from one another.

[0499] The determination of whether the 20 curves k47 to k66 are different from each other is performed by determining the number of combinations ( ２０ C ２ = 190) are different.

[0500] Referring to FIG. 53, when two different curves are selected from the ten curves k47 to k56, all of the 45 standard deviations σDF_k47, k48 to σDF_k55, k56 of the differences for all 45 different curves are below the threshold value σ ｔｈ (=5%).

[0501] Referring to FIG. 54, when two different curves are selected from the ten curves k47 to k56 and the ten curves k57 to k66, all of the 100 standard deviations σDF_k47, k57 to σDF_k56, k66 of the differences between the 100 different curves are below the threshold σ ｔｈ (=5%).

[0502] Referring to FIG. 55, when two different curves are selected from the ten curves k57 to k66, all of the 45 standard deviations σDF_k57, k58 to σDF_k65, k66 of the differences for all 45 different curves are below the threshold σ ｔｈ (=5%).

[0503] Therefore, when two different curves are selected from the 20 curves k47 to k66, all of the 190 standard deviations σDF_k47, k48 to σDF_k65, k66 of the differences for all 190 different curves are within the threshold value σ ｔｈ (=5%).

[0504] Therefore, the 20 curves k47 to k66 are mutually different. When it is determined that the 20 curves k47 to k66 are mutually different, the curves k47 to k66 are curves for uniquely identifying shochu No. 1 to No. 20, respectively. When the number of integral values ​​is 70, the curves k47 to k66 are fingerprints representing feature quantities based on the integral values ​​for shochu No. 1 to No. 20, respectively.

[0505] The rest of the explanation for the case where the number of integrals is 70 is the same as the explanation for the case where the number of integrals is 138. (III) Number of integrals: 36 Figure 56 is a diagram showing the integral spectra for the shochu samples No. 1 to No. 3 shown in Table 2 when the number of integrals is 36.

[0506] 56, each of curves k67 to k69 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k67 represents the integrated spectrum of barley shochu sample No. 1, curve k68 represents the integrated spectrum of barley shochu sample No. 2, and curve k69 represents the integrated spectrum of barley shochu sample No. 3.

[0507] 57 is a diagram showing the integral spectrum of the shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 36.

[0508] 57, each of curves k70 to k72 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k70 represents the integrated spectrum of barley shochu sample No. 4, curve k71 represents the integrated spectrum of barley shochu sample No. 5, and curve k72 represents the integrated spectrum of barley shochu sample No. 6.

[0509] 58 is a diagram showing the integral spectrum of the shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 36.

[0510] 58, each of curves k73 to k75 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k73 represents the integrated spectrum of sweet potato shochu sample No. 7, curve k74 represents the integrated spectrum of sweet potato shochu sample No. 8, and curve k75 represents the integrated spectrum of sweet potato shochu sample No. 9.

[0511] 59 is a diagram showing the integral spectrum of the shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 36.

[0512] 59, each of curves k76 to k78 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k76 represents the integrated spectrum of sweet potato shochu sample No. 10, curve k77 represents the integrated spectrum of sweet potato shochu sample No. 11, and curve k78 represents the integrated spectrum of sweet potato shochu sample No. 12.

[0513] 60 is a diagram showing the integral spectrum of the shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 36.

[0514] 60, each of curves k78 to k80 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k78 represents the integrated spectrum of sweet potato shochu sample No. 12, curve k79 represents the integrated spectrum of sweet potato shochu sample No. 13, and curve k80 represents the integrated spectrum of sweet potato shochu sample No. 14.

[0515] 61 shows the integral spectra of the shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 36.

[0516] 61, curves k81 and k82 are curves CUR (index curves) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k81 represents the integral spectrum of rice shochu sample No. 15, and curve k82 represents the integral spectrum of rice shochu sample No. 16.

[0517] 62 is a diagram showing the integral spectrum of the shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 36.

[0518] 62, each of curves k83 to k86 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 using the above-mentioned method. Curve k83 shows the integral spectrum of sample No. 17, shochu made with sake lees, curve k84 shows the integral spectrum of sample No. 18, barley shochu stored entirely for three years, entirely in barrels, and partially in cherry wood barrels, curve k85 shows the integral spectrum of sample No. 19, barley shochu stored entirely for 15 years, entirely made from koji barley, entirely in barrels, and with additional heat, and curve k86 shows sample No. 20, rice shochu stored entirely for three years and entirely in barrels.

[0519] 56 to 62, each of classes 1 to 17 consists of a predetermined potential section (integral value extraction potential) of 144.8 mV, class 18 consists of a predetermined potential section (integral value extraction potential) of 36.2 mV, class 19 to class 35 consists of a predetermined potential section (integral value extraction potential) of 144.8 mV, and class 36 consists of a predetermined potential section (integral value extraction potential) of 36.2 mV. Classes 1 to 18 are classes in positive potential sections, and classes 19 to 36 are classes in negative potential sections.

[0520] The reason why class 18 has a predetermined potential section (integral value extraction potential) of 36.2 mV, which is smaller than the predetermined potential section (integral value extraction potential) of 144.8 mV in each of classes 1 to 17, is to make class 18 the "last class in the positive predetermined potential section."

[0521] That is, when class 18 is composed of the same predetermined potential section (integral extraction potential) as the predetermined potential section (integral extraction potential) of 144.8 mV in each of classes 1 to 17, the integral value in class 18 is the sum of the integral value in the positive predetermined potential section and the integral value in the negative predetermined potential section, and based on the integral spectrum in which all classes are composed of the predetermined potential section (integral extraction potential) of 144.8 mV, the sum of the integral values ​​in the positive predetermined potential section SMT_ Ｌｏｗ（＋） , SMT_ Ｍｉｄｄｌｅ（＋） , SMT_ Ｈｉｇｈ（＋）Therefore, it is not possible to calculate the Body Index (+) based on the integral spectrum in which all classes are in the predetermined potential range (integral extraction potential) of 144.8 mV, and to diagnose the astringency ASTG of shochu using equation (5), and it is not possible to calculate H(+)_sum (=SMT_ Ｈｉｇｈ（＋） ) and the sweetness SWT of the shochu cannot be diagnosed by equation (7), and H(-)_sum (=SMT) is calculated based on the integral spectrum consisting of a predetermined potential section (integral extraction potential) of 144.8 mV for all classes. ＿Ｈｉｇｈ（－） ) and it becomes impossible to diagnose the aroma SCT of shochu using equation (8), and as a result, it becomes impossible to diagnose the bitterness BIT of shochu using equation (9).

[0522] 63 to 65 are first to third diagrams respectively showing the results of determining whether or not the curves k67 to k86 shown in FIGS. 56 to 62 are different from one another.

[0523] Figure 63 shows the results of determining whether ten curves k67 to k76 differ from one another, Figure 64 shows the results of determining whether ten curves k67 to k76 and ten curves k77 to k86 differ from one another, and Figure 65 shows the results of determining whether ten curves k77 to k86 differ from one another.

[0524] The determination of whether the 20 curves k67 to k86 are different from each other is performed by determining the number of combinations ( ２０ C ２ = 190) are different.

[0525] Referring to FIG. 63, when two different curves are selected from the ten curves k67 to k76, all of the 45 standard deviations σDF_k67, k68 to σDF_k75, k76 of the differences for all 45 different curves are below the threshold value σ ｔｈ (=5%).

[0526] Referring to FIG. 64, when two different curves are selected from the ten curves k67 to k76 and the ten curves k77 to k86, all of the 100 standard deviations σDF_k67, k77 to σDF_k76, k86 of the differences for all 100 different two curves are below the threshold σ ｔｈ (=5%).

[0527] Referring to FIG. 65, when two different curves are selected from the ten curves k77 to k86, all of the 45 standard deviations σDF_k77, k78 to σDF_k85, k86 of the differences for all 45 different curves are below the threshold value σ ｔｈ (=5%).

[0528] Therefore, when two different curves are selected from the 20 curves k67 to k86, all of the 190 standard deviations σDF_k67, k68 to σDF_k85, k86 of the differences for all 190 different curves are within the threshold value σ ｔｈ (=5%).

[0529] Therefore, the 20 curves k67 to k86 are mutually different. When it is determined that the 20 curves k67 to k86 are mutually different, the curves k67 to k86 are curves for uniquely identifying shochu No. 1 to No. 20, respectively. When the number of integral values ​​is 36, the curves k67 to k86 are fingerprints representing feature quantities based on the integral values ​​for shochu No. 1 to No. 20, respectively.

[0530] The rest of the explanation for the case where the number of integrals is 36 is the same as the explanation for the case where the number of integrals is 138. (IV) Number of integrals: 18 Figure 66 shows the integral spectra for the shochu samples No. 1 to No. 3 shown in Table 2 when the number of integrals is 18.

[0531] 66, each of curves k87 to k89 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k87 represents the integrated spectrum of barley shochu sample No. 1, curve k88 represents the integrated spectrum of barley shochu sample No. 2, and curve k89 represents the integrated spectrum of barley shochu sample No. 3.

[0532] 67 is a diagram showing the integral spectrum of the shochu samples No. 4 to No. 6 shown in Table 2 when the number of integrals is 18.

[0533] 67, each of curves k90 to k92 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k90 represents the integrated spectrum of barley shochu sample No. 4, curve k91 represents the integrated spectrum of barley shochu sample No. 5, and curve k92 represents the integrated spectrum of barley shochu sample No. 6.

[0534] 68 is a diagram showing the integral spectrum of the shochu samples No. 7 to No. 9 shown in Table 2 when the number of integrals is 18.

[0535] 68, each of curves k93 to k95 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k93 represents the integrated spectrum of sweet potato shochu sample No. 7, curve k94 represents the integrated spectrum of sweet potato shochu sample No. 8, and curve k95 represents the integrated spectrum of sweet potato shochu sample No. 9.

[0536] 69 is a diagram showing the integral spectrum of the shochu samples No. 10 to No. 12 shown in Table 2 when the number of integrals is 18.

[0537] 69, each of curves k96 to k98 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k96 represents the integrated spectrum of sweet potato shochu sample No. 10, curve k97 represents the integrated spectrum of sweet potato shochu sample No. 11, and curve k98 represents the integrated spectrum of sweet potato shochu sample No. 12.

[0538] 70 is a diagram showing the integral spectrum of the shochu samples No. 12 to No. 14 shown in Table 2 when the number of integrals is 18.

[0539] 70, each of curves k98 to k100 is a curve CUR (index curve) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k98 represents the integrated spectrum of sweet potato shochu sample No. 12, curve k99 represents the integrated spectrum of sweet potato shochu sample No. 13, and curve k100 represents the integrated spectrum of sweet potato shochu sample No. 14.

[0540] 71 shows the integral spectra of the shochu samples No. 15 and No. 16 shown in Table 2 when the number of integrals is 18.

[0541] 71, curves k101 and k102 are curves CUR (index curves) created by the above-described method by creation unit 215 of diagnostic device 2. Curve k101 represents the integral spectrum of rice shochu sample No. 15, and curve k102 represents the integral spectrum of rice shochu sample No. 16.

[0542] 72 is a diagram showing the integral spectrum of the shochu samples No. 17 to No. 20 shown in Table 2 when the number of integrals is 18.

[0543] 72, each of curves k103 to k106 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 using the above-mentioned method. Curve k103 shows the integral spectrum of sample No. 17, shochu made with sake lees, curve k104 shows the integral spectrum of sample No. 18, barley shochu stored entirely for three years, entirely in barrels, and partially in cherry wood barrels, curve k105 shows the integral spectrum of sample No. 19, barley shochu stored entirely for 15 years, entirely made from koji barley, entirely in barrels, and with additional heat, and curve k106 shows sample No. 20, rice shochu stored entirely for three years and entirely in barrels.

[0544] 66 to 72, each of classes 1 to 8 consists of a predetermined potential section (integral value extraction potential) of 307.7 mV, class 9 consists of a predetermined potential section (integral value extraction potential) of 36.2 mV, class 10 to class 17 consists of a predetermined potential section (integral value extraction potential) of 307.7 mV, and class 18 consists of a predetermined potential section (integral value extraction potential) of 36.2 mV. Classes 1 to 9 are classes in positive potential sections, and classes 10 to 18 are classes in negative potential sections.

[0545] The reason why class 9 has a predetermined potential section (integral value extraction potential) of 36.2 mV, which is smaller than the predetermined potential section (integral value extraction potential) of 307.7 mV in each of classes 1 to 8, is to make class 9 the "last class in the positive predetermined potential section."

[0546] That is, when class 9 is composed of the same predetermined potential section (integral extraction potential) as the predetermined potential section (integral extraction potential) of 307.7 mV in each of classes 1 to 8, the integral value in class 9 is the sum of the integral value in the positive predetermined potential section and the integral value in the negative predetermined potential section, and based on the integral spectrum in which all classes are composed of the predetermined potential section (integral extraction potential) of 307.7 mV, the sum of the integral values ​​in the positive predetermined potential section SMT_ Ｌｏｗ（＋） , SMT_ Ｍｉｄｄｌｅ（＋） , SMT_ Ｈｉｇｈ（＋）Therefore, it is not possible to calculate the Body Index (+) based on the integral spectrum in which all classes are in the predetermined potential range (integral extraction potential) of 307.7 mV, and to diagnose the astringency ASTG of shochu using equation (5), and it is not possible to calculate H(+)_sum (=SMT_ Ｈｉｇｈ（＋） ) and the sweetness SWT of shochu cannot be diagnosed by equation (7), and H(-)_sum (=SMT_ Ｈｉｇｈ（－） ) and it becomes impossible to diagnose the aroma SCT of shochu using equation (8), and as a result, it becomes impossible to diagnose the bitterness BIT of shochu using equation (9).

[0547] 73 to 75 are first to third diagrams respectively showing the results of determining whether or not the curves k87 to k106 shown in FIGS. 66 to 72 are different from one another.

[0548] Figure 73 shows the results of determining whether ten curves k87 to k96 differ from one another, Figure 74 shows the results of determining whether ten curves k87 to k96 and ten curves k97 to k106 differ from one another, and Figure 75 shows the results of determining whether ten curves k97 to k106 differ from one another.

[0549] The determination of whether the 20 curves k87 to k106 are different from each other is performed by determining the number of combinations ( ２０ C ２ = 190) are different.

[0550] Referring to FIG. 73, when two different curves are selected from the ten curves k87 to k96, all of the 45 standard deviations σDF_k87, k88 to σDF_k95, k96 of the differences for all 45 different curves are below the threshold value σ ｔｈ (=5%).

[0551] Referring to FIG. 74, when two different curves are selected from the ten curves k87 to k96 and the ten curves k97 to k106, all of the 100 standard deviations σDF_k87, k97 to σDF_k96, k106 of the differences between the 100 different curves are below the threshold σ ｔｈ (=5%).

[0552] Referring to FIG. 75, when two different curves are selected from the ten curves k97 to k106, all of the 45 standard deviations σDF_k97, k98 to σDF_k105, k106 of the differences between the 45 different curves are below the threshold value σ ｔｈ (=5%).

[0553] Therefore, when two different curves are selected from the 20 curves k87 to k106, all of the 190 standard deviations σDF_k87, k88 to σDF_k105, k106 of the differences for all 190 different curves are within the threshold σ ｔｈ (=5%).

[0554] Therefore, the 20 curves k87 to k106 are mutually different. When it is determined that the 20 curves k87 to k106 are mutually different, the curves k87 to k106 are curves for uniquely identifying shochu No. 1 to No. 20, respectively. When the number of integral values ​​is 18, the curves k87 to k106 are fingerprints representing feature quantities based on the integral values ​​for shochu No. 1 to No. 20, respectively.

[0555] Although not shown in the figure, when the number of integral values ​​is 92, 56, 32, and 20, the number of combinations when selecting two different curves from the 20 curves ( ２０ C ２ = 190), the standard deviation of the differences is within the threshold value σ ｔｈ It was confirmed that this was greater than 5%.

[0556] Other explanations for the case where the number of integral values ​​is 18 are the same as those for the case where the number of integral values ​​is 138.

[0557] As described above, the index curve for uniquely identifying No. 1 shochu is made up of multiple curves k1, k27, k47, k67, and k87 each having a different number of integral values, the index curve for uniquely identifying No. 2 shochu is made up of multiple curves k2, k28, k48, k68, and k88 each having a different number of integral values, the index curve for uniquely identifying No. 3 shochu is made up of multiple curves k3, k29, k49, k69, and k89 each having a different number of integral values, the index curve for uniquely identifying No. 4 shochu is made up of multiple curves k4, k30, k50, k70, and k90 each having a different number of integral values, and the index curve for uniquely identifying No. 5 shochu is made up of multiple curves k5, k31, k51, k71, and k91 each having a different number of integral values.

[0558] Furthermore, the index curve for uniquely identifying No. 6 shochu is made up of multiple curves k6, k32, k52, k72, and k92 each having a different number of integral values, the index curve for uniquely identifying No. 7 shochu is made up of multiple curves k7, k33, k53, k73, and k93 each having a different number of integral values, the index curve for uniquely identifying No. 8 shochu is made up of multiple curves k8, k34, k54, k74, and k94 each having a different number of integral values, the index curve for uniquely identifying No. 9 shochu is made up of multiple curves k9, k35, k55, k75, and k95 each having a different number of integral values, and the index curve for uniquely identifying No. 10 shochu is made up of multiple curves k10, k36, k56, k76, and k96 each having a different number of integral values.

[0559] Furthermore, the index curve for uniquely identifying No. 11 shochu is made up of a plurality of curves k11, k37, k57, k77, and k97 with different numbers of integral values, the index curve for uniquely identifying No. 12 shochu is made up of a plurality of curves k12, k38, k58, k78, and k98 with different numbers of integral values, the index curve for uniquely identifying No. 13 shochu is made up of a plurality of curves k13, k39, k59, k79, and k99 with different numbers of integral values, the index curve for uniquely identifying No. 14 shochu is made up of a plurality of curves k14, k40, k60, k80, and k100 with different numbers of integral values, and the index curve for uniquely identifying No. 15 shochu is made up of a plurality of curves k15, k40, k60, k80, and k100 with different numbers of integral values. The index curve for uniquely identifying the 15 shochus is made up of a plurality of curves k15, k41, k61, k81, and k101, each having a different number of integral values.

[0560] Furthermore, the index curve for uniquely identifying No. 16 shochu is made up of multiple curves k16, k42, k62, k82, and k102 with different numbers of integral values, the index curve for uniquely identifying No. 17 shochu is made up of multiple curves k17, k43, k63, k83, and k103 with different numbers of integral values, the index curve for uniquely identifying No. 18 shochu is made up of multiple curves k18, k44, k64, k84, and k104 with different numbers of integral values, and the index curve for uniquely identifying No. 19 shochu is made up of multiple curves k19, k45, k65, k85, and k105 with different numbers of integral values, and the index curve for uniquely identifying No. 19 shochu is made up of multiple curves k19, k45, k65, k85, and k105 with different numbers of integral values. The index curve for uniquely identifying the 20 shochus is made up of a plurality of curves k20, k46, k66, k86, and k106, each having a different number of integral values.

[0561] In tasting shochu, the taste diagnosis unit 217 diagnoses the astringency ASTG of the shochu using formula (5), the aftertaste LNGS of the shochu using formula (6), the sweetness SWT of the shochu using formula (7), the aroma SCT of the shochu using formula (8), and the bitterness BIT of the shochu using formula (9).

[0562] The taste diagnosis unit 217 uses the Body index (+) shown in equation (3) when diagnosing the astringency ASTG of shochu using equation (5), uses the Body index (all) when diagnosing the aftertaste LNGS of shochu using equation (6), uses H(+)_sum when diagnosing the sweetness SWT of shochu using equation (7), uses H(-)_sum when diagnosing the aroma SCT of shochu using equation (8), and uses the astringency ASTG of shochu and the sweetness SWT of shochu when diagnosing the bitterness BIT of shochu using equation (9).

[0563] The taste diagnosis unit 217 calculates the potential scanning speed V ｒ＿Ｌｏｗ The sum of integrals in the positive potential range of the integral spectrum created based on the cyclic voltammogram measured using Ｌｏｗ（＋） and the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ The sum of integrals in the positive potential range of the integral spectrum created based on the cyclic voltammogram measured using Ｍｉｄｄｌｅ（＋） and the potential scanning speed V ｒ＿Ｈｉｇｈ The sum of integrals in the positive potential range of the integral spectrum created based on the cyclic voltammogram measured using Ｈｉｇｈ（＋） The Body Index (+) is calculated using the above.

[0564] Furthermore, the taste diagnosis unit 217 uses the potential scanning speed V ｒ＿Ｌｏｗ The sum of integrals SMT_ in all potential intervals of the integral spectrum created based on the cyclic voltammogram measured using Ｌｏｗ（ａｌｌ） and the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ The sum of the integral values ​​SMT_Middle (all) in all potential intervals of the integral spectrum created based on the cyclic voltammogram measured using ｒ＿Ｈｉｇｈ The sum of the integrals SMT_ in all potential intervals of the integral spectrum created based on the cyclic voltammogram measured using Ｈｉｇｈ（ａｌｌ） and calculate Body Index (all).

[0565] FIG. 76 is a diagram showing the correspondence between the classes and integral values ​​when the number of integral values ​​is 138 and the classes and integral values ​​when the number of integral values ​​is 276.

[0566] 76(a) and (b), when the number of integral values ​​(=the number of classes) is 138, the integral value ITG in class 1 １＿１３８ is the integral ITG in classes 1 and 2 when the number of integrals (= number of classes) is 276. １＿２７６ , ITG ２＿２７６ The integral value ITG in class 2 is the sum of ２＿１３８ is the integral ITG in classes 3 and 4 when the number of integrals (= number of classes) is 276. ３＿２７６ , ITG ４＿２７６ ..., the integral value ITG in class 68 ６８＿１３８ is the integral ITG in classes 137 and 138 when the number of integrals (= number of classes) is 276. １３７＿２７６ , ITG １３８＿２７６ The integral ITG in class 69 is the sum of ６９＿１３８ is the integral ITG in classes 139 and 140 when the number of integrals (= number of classes) is 276. １３９＿２７６ , ITG １４０＿２７６ ..., the integral value ITG in class 138 １３８＿１３８ is the integral ITG in classes 275 and 276 when the number of integrals (= number of classes) is 276. ２７５＿２７６ , ITG ２７６＿２７６ It consists of the sum of

[0567] In FIG. 76(a), classes 1 to 138 are classes in the positive potential section, and the integral value ITG １＿２７６ ~ITG １３８＿２７６ is the integral value in the positive potential section, and the classes 139 to 276 are the classes in the negative potential section. The integral value ITG １３９＿２７６ ~ITG ２７６＿２７６ is the integral value in the negative potential section.

[0568] As a result, in FIG. 76(b), classes 1 to 68 are classes in the positive potential section, and the integral value ITG １＿１３８ ~ITG ６８＿１３８is the integral value in the positive potential section, and the classes 69 to 138 are the classes in the negative potential section. The integral value ITG ６９＿１３８ ~ITG １３８＿１３８ is the integral value in the negative potential section.

[0569] Then, the integral values ​​ITG in classes 1 to 68 in FIG. １＿１３８ ~ITG ６８＿１３８ The sum of the integrals ITG in classes 1 to 138 in FIG. １＿２７６ ~ITG １３８＿２７６ is equal to the sum of

[0570] In addition, the integral values ​​ITG in classes 69 to 138 in FIG. ６９＿１３８ ~ITG １３８＿１３８ The sum of the integrals ITG in classes 139 to 276 in FIG. １３９＿２７６ ~ITG ２７６＿２７６ is equal to the sum of

[0571] Furthermore, the integral values ​​ITG in classes 1 to 138 in FIG. １＿１３８ ~ITG １３８＿１３８ The sum of the integrals ITG in classes 1 to 276 in FIG. １＿２７６ ~ITG ２７６＿２７６ is equal to the sum of

[0572] Therefore, the potential scanning speed V ｒ＿Ｌｏｗ The sum of integral values ​​SMT_Low(+)_138 in the positive potential section of the integral spectrum consisting of 138 integral values ​​created based on the cyclic voltammogram measured using ｒ＿Ｌｏｗ This coincides with the sum of integrals SMT_Low(+)_276 in the positive potential section of the integral spectrum consisting of 276 integrals created based on the cyclic voltammogram measured using

[0573] potential scanning speed V ｒ＿Ｍｉｄｄｌｅ The sum of integral values ​​SMT_Middle(+)_138 in the positive potential section of the integral spectrum consisting of 138 integral values ​​created based on the cyclic voltammogram measured using ｒ＿ＨｉｇｈThe same is true for the sum of integrals SMT_High(+)_138 in the positive potential section of the integral spectrum consisting of 138 integrals created based on the cyclic voltammogram measured using

[0574] As a result, in equation (3), the Body Index (+) calculated based on the sums of the integral values ​​SMT_Low(+)_138, SMT_Middle(+)_138, and SMT_High(+)_138 is ＿１３８ is the Body Index (+) calculated based on the sum of the integrals SMT_Low(+)_276, SMT_Middle(+)_276, and SMT_High(+)_276. ＿２７６ Matches.

[0575] In addition, the potential scanning speed V ｒ＿Ｌｏｗ The sum of the integrals SMT_Low(all)_138 in all potential intervals of the integral spectrum consisting of 138 integrals created based on the cyclic voltammogram measured using ｒ＿Ｌｏｗ This coincides with the sum of integrals SMT_Low(all)_276 in all potential intervals of the integral spectrum consisting of 276 integrals created based on the cyclic voltammogram measured using

[0576] potential scanning speed V ｒ＿Ｍｉｄｄｌｅ The sum of the integral values ​​SMT_Middle(all)_138 in all potential intervals of the integral spectrum consisting of 138 integral values ​​created based on the cyclic voltammogram measured using ｒ＿Ｈｉｇｈ The same is true for the sum of integrals SMT_High(all)_138 in all potential intervals of an integral spectrum consisting of 138 integrals created based on a cyclic voltammogram measured using

[0577] As a result, in equation (4), the sum of the integrals SMT ＿Ｌｏｗ（ａｌｌ）＿１３８ , SMT_Middle(all)_138, Body Index (all) calculated using SMT_High(all)_138 ＿１３８is the Body Index (all) calculated using the sum of the integral values ​​SMT_Low(all)_276, SMT_Middle(all)_276, and SMT_High(all)_276. ＿２７６ Matches.

[0578] Furthermore, the potential scanning speed V ｒ＿Ｈｉｇｈ The sum of integrals SMT_ in the positive potential range of the integral spectrum consisting of 138 integrals created based on the cyclic voltammogram measured using Ｈｉｇｈ（＋） H(+)_sum ＿１３８ is the potential scanning speed V ｒ＿Ｈｉｇｈ The sum of integrals SMT_ in the positive potential range of the integral spectrum consisting of 276 integrals created based on the cyclic voltammogram measured using Ｈｉｇｈ（＋） H(+)_sum ＿２７６ Matches.

[0579] Furthermore, the potential scanning speed V ｒ＿Ｈｉｇｈ The sum of integrals SMT_ in the negative potential range of the integral spectrum consisting of 138 integrals created based on the cyclic voltammogram measured using Ｈｉｇｈ（－） H(-)_sum ＿１３８ is the potential scanning speed V ｒ＿Ｈｉｇｈ The sum of integrals SMT_ in the negative potential range of the integral spectrum consisting of 276 integrals created based on the cyclic voltammogram measured using Ｈｉｇｈ（－） H(-)_sum ＿２７６ Matches.

[0580] Even if the number of integral values ​​changes, the coefficient k in equations (5) to (9) １ ~k ８ Since does not change, in equation (5), Body index (+) ＿１３８ The astringency ASTG of shochu calculated using ＿１３８ is the Body Index (+) ＿２７６ The astringency ASTG of shochu calculated using ＿２７６ In equation (6), the Body index (all) ＿１３８ The lingering aftertaste of shochu calculated using LNGS ＿１３８ is Body Index (all)＿２７６ The lingering aftertaste of shochu calculated using LNGS ＿２７６ In equation (7), H(+)_sum ＿１３８ (= SMT_High(+)_138) ＿１３８ is H(+)_sum ＿２７６ (= SMT_High(+)_276) ＿２７６ In equation (8), H(-)_sum ＿１３８ (=SMT_High(-)_138) ＿１３８ is H(-)_sum ＿２７６ (=SMT_High(-)_276) ＿２７６ In equation (9), the astringency ASTG of shochu is ＿１３８ and sweetness SSWT ＿１３８ The bitterness of shochu was calculated using ＿１３８ is the astringency of shochu ASTG ＿２７６ and sweetness SWT ＿２７６ The bitterness of shochu was calculated using ＿２７６ Matches.

[0581] The same applies to the cases where the number of integral values ​​is 92, 70, 56, 36, 32, 20, or 18.

[0582] Therefore, even if the number of integral values ​​in the integral spectrum, which is the fingerprint for Shochu No. 1 to Shochu No. 20, changes, the results of the taste assessment of Shochu No. 1 to Shochu No. 20 will match the "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" shown in Figure 29.

[0583] As a result, the diagnostic device 2 detects an integral spectrum ITG having any one of 18, 20, 32, 36, 56, 70, 92, 138, and 276 integrals. ＿ＳＰＣ＿ｎ Based on this, taste assessment may be performed on Shochu No. 1 to No. 20 using the method described above.

[0584] In this case, the integral spectrum ITG ＿ＳＰＣ＿ｎ is the integral value ITG１＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ and the integral spectrum consisting of the integral ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ and the integral spectrum consisting of the integral ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ and an integrated spectrum consisting of the integral spectrum of the integral spectrum of the integral spectrum of the spectrum of the signal, where n is one of 18, 20, 32, 36, 56, 70, 92, 138, and 276.

[0585] [Relationship between Taste Diagnosis of Grapes and Number of Integral Values] As described above, in the taste diagnosis of Crimson Seed (skin+flesh), Green Seedless (skin+flesh), Shine Muscat (skin+flesh), Crimson Seedless (flesh only), Green Seedless (flesh only) and Shine Muscat (flesh only), the threshold value Vth of the potential V is set to −1362 mV, and L(−)_sum_th, M(−)_sum_th, and H(−)_sum_th are calculated based on the integral values ​​in a predetermined potential range (=class) in the range of −1362 mV to −2501 mV.

[0586] Here, we will explain the integral spectra obtained by changing the number of integrals in a predetermined potential interval (=class) in the range of -1362 mV to -2501 mV used in taste diagnosis. (I) Number of integrals: 74 Figure 77 shows the integral spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integrals in a predetermined potential interval in the range of -1362 mV to -2501 mV is 74.

[0587] 77, each of curves k107 to k109 is a curve CUR created by the diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k107 represents the integral spectrum of Crimson Seedless (skin and flesh), curve k108 represents the integral spectrum of Green Seedless (skin and flesh), and curve k109 represents the integral spectrum of Shine Muscat (skin and flesh).

[0588] FIG. 78 shows the integral spectra of Crimson Seedless (pulp only), Green Seedless (pulp only), and Shine Muscat (pulp only) when the number of integral values ​​in a predetermined potential range of −1362 mV to −2501 mV is 74.

[0589] 78, each of curves k110 to k112 is a curve CUR created by the diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k110 represents the integral spectrum of Crimson Seedless (pulp only), curve k111 represents the integral spectrum of Green Seedless (pulp only), and curve k112 represents the integral spectrum of Shine Muscat (pulp only).

[0590] In FIGS. 77 and 78, each of classes 1 to 74 consists of a predetermined potential section of 18.1 mV.

[0591] FIG. 79 is a diagram showing the results of determining whether or not the curves k107 to k112 shown in FIGS. 77 and 78 are different from each other.

[0592] Whether or not the curves k107 to k112 are different from one another is determined by determining whether or not two different curves among the curves k107 to k112 are different for all combinations of two different curves among the curves k107 to k112.

[0593] There are 15 possible combinations of two different curves out of the six curves k107 to k112: (k107, k108), (k107, k109), (k107, k110), (k107, k111), (k107, k112), (k108, k109), (k108, k110), (k108, k111), (k108, k112), (k109, k110), (k109, k111), (k109, k112), (k110, k111), (k110, k112), and (k111, k112).

[0594] 79, the minimum value of the 15 standard deviations of the differences σDF_k107, k108, σDF_k107, k109, ..., σDF_k111, k112 is 8.69% and the maximum value is 89.09%.

[0595] Therefore, all of the standard deviations σDF_k107, k108, σDF_k107, k109, ..., σDF_k111, k112 of the 15 differences are within the threshold σ ｔｈ (=8%).

[0596] Therefore, curves k107 to k112 are mutually different curves, and are fingerprints that represent feature quantities based on integral values ​​for Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), Shine Muscat (skin + flesh), Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only). (II) Number of integral values: 37 Figure 80 shows the integral value spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 37.

[0597] 80, each of curves k113 to k115 is a curve CUR created by diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k113 represents the integral spectrum of Crimson Seedless (skin and flesh), curve k114 represents the integral spectrum of Green Seedless (skin and flesh), and curve k115 represents the integral spectrum of Shine Muscat (skin and flesh).

[0598] FIG. 81 shows the integral spectra of Crimson Seedless (pulp only), Green Seedless (pulp only), and Shine Muscat (pulp only) when the number of integral values ​​in a predetermined potential range of −1362 mV to −2501 mV is 37.

[0599] 81, each of curves k116 to k118 is a curve CUR created by the diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k116 represents the integral spectrum of Crimson Seedless (pulp only), curve k117 represents the integral spectrum of Green Seedless (pulp only), and curve k118 represents the integral spectrum of Shine Muscat (pulp only).

[0600] In FIGS. 80 and 81, each of classes 1 to 37 consists of a predetermined potential section of 36.2 mV.

[0601] FIG. 82 is a diagram showing the results of determining whether or not the curves k113 to k118 shown in FIGS. 80 and 81 are different from each other.

[0602] Whether or not the curves k113 to k118 are different from one another is determined by determining whether or not two different curves among the curves k113 to k118 are different for all combinations of two different curves among the curves k113 to k118.

[0603] There are 15 possible combinations of two different curves out of the six curves k113 to k118: (k113, k114), (k113, k115), (k113, k116), (k113, k117), (k113, k118), (k114, k115), (k114, k116), (k114, k117), (k114, k118), (k115, k116), (k115, k117), (k115, k118), (k116, k117), (k116, k118), and (k117, k118).

[0604] 82, the minimum value of the 15 standard deviations of the differences σDF_k113, k114, σDF_k113, k115, . . . , σDF_k117, k118 is 8.64% and the maximum value is 115.64%.

[0605] Therefore, all of the standard deviations σDF_k113, k114, σDF_k113, k115, . . . , σDF_k117, k118 of the 15 differences are within the threshold σ ｔｈ (=8%).

[0606] Therefore, curves k113 to k118 are mutually different curves, and are fingerprints that represent feature quantities based on integral values ​​for Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), Shine Muscat (skin + flesh), Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only). (III) Number of integral values: 19 Figure 83 is a diagram showing the integral value spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 19.

[0607] 83, each of curves k119 to k121 is a curve CUR created by diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k119 represents the integral spectrum of Crimson Seedless (skin and flesh), curve k120 represents the integral spectrum of Green Seedless (skin and flesh), and curve k121 represents the integral spectrum of Shine Muscat (skin and flesh).

[0608] FIG. 84 shows the integral spectra of Crimson Seedless (pulp only), Green Seedless (pulp only), and Shine Muscat (pulp only) when the number of integrals in a predetermined potential range of −1362 mV to −2501 mV is 19.

[0609] 84, each of curves k122 to k124 is a curve CUR created by the diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k122 represents the integral spectrum of Crimson Seedless (pulp only), curve k123 represents the integral spectrum of Green Seedless (pulp only), and curve k124 represents the integral spectrum of Shine Muscat (pulp only).

[0610] In FIGS. 83 and 84, each of classes 1 to 18 is made up of a predetermined potential section of 72.4 mV, and class 19 is made up of a predetermined potential section of 36.2 mV.

[0611] FIG. 85 is a diagram showing the results of determining whether or not the curves k119 to k124 shown in FIGS. 83 and 84 are different from each other.

[0612] Whether or not the curves k119 to k124 are different from one another is determined by determining whether or not two different curves among the curves k119 to k124 are different for all combinations of two different curves among the curves k119 to k124.

[0613] There are 15 possible combinations of two different curves out of the six curves k119 to k124: (k119, k120), (k119, k121), (k119, k122), (k119, k123), (k119, k124), (k120, k121), (k120, k122), (k120, k123), (k120, k124), (k121, k122), (k121, k123), (k121, k124), (k122, k123), (k122, k124), and (k123, k124).

[0614] 85, the minimum value of the 15 standard deviations of the differences σDF_k119, k120, σDF_k119, k121, ..., σDF_k123, k124 is 8.54% and the maximum value is 120.43%.

[0615] Therefore, all of the standard deviations σDF_k119, k120, σDF_k119, k121, ..., σDF_k123, k124 of the 15 differences are within the threshold σ ｔｈ (=8%).

[0616] Therefore, curves k119 to k124 are mutually different curves, and are fingerprints that represent feature quantities based on integral values ​​for Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), Shine Muscat (skin + flesh), Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only). (IV) Number of integral values: 9 Figure 86 shows the integral value spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 9.

[0617] 86, each of curves k125 to k127 is a curve CUR created by the diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k125 represents the integral spectrum of Crimson Seedless (skin and flesh), curve k126 represents the integral spectrum of Green Seedless (skin and flesh), and curve k127 represents the integral spectrum of Shine Muscat (skin and flesh).

[0618] FIG. 87 shows the integral spectra of Crimson Seedless (pulp only), Green Seedless (pulp only), and Shine Muscat (pulp only) when the number of integrals in a predetermined potential range of −1362 mV to −2501 mV is 9.

[0619] 87, each of curves k128 to k130 is a curve CUR created by the diagnostic device 2 using the above-described method in a predetermined potential range of −1362 mV to −2501 mV. Curve k128 represents the integral spectrum of Crimson Seedless (pulp only), curve k129 represents the integral spectrum of Green Seedless (pulp only), and curve k130 represents the integral spectrum of Shine Muscat (pulp only).

[0620] In FIGS. 86 and 87, each of classes 1 to 8 consists of a predetermined potential section of 162.9 mV, and class 9 consists of a predetermined potential section of 36.2 mV.

[0621] FIG. 88 is a diagram showing the results of determining whether the curves k125 to k130 shown in FIGS. 86 and 87 are different from each other.

[0622] Whether or not the curves k125 to k130 are different from one another is determined by determining whether or not two different curves among the curves k125 to k130 are different for all combinations of two different curves among the curves k125 to k130.

[0623] There are 15 possible combinations of two different curves out of the six curves k125 to k130: (k125, k126), (k125, k127), (k125, k128), (k125, k129), (k125, k130), (k126, k127), (k126, k128), (k126, k129), (k126, k130), (k127, k128), (k127, k129), (k127, k130), (k128, k129), (k128, k130), and (k129, k130).

[0624] 88, the minimum value of the 15 standard deviations of the differences σDF_k125, k126, σDF_k125, k127, ..., σDF_k129, k130 is 8.18% and the maximum value is 129.56%.

[0625] Therefore, all of the standard deviations σDF_k125, k126, σDF_k125, k127, ..., σDF_k129, k130 of the 15 differences are within the threshold σ ｔｈ (=8%).

[0626] Therefore, curves k125 to k130 are mutually different curves, and are fingerprints that represent the features based on the integrated values ​​for Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), Shine Muscat (skin + flesh), Crimson Seedless (flesh only), Green Seedless (flesh only), and Shine Muscat (flesh only).

[0627] Although not shown in the figure, even when the number of integral values ​​in the predetermined potential range of −1362 mV to −2501 mV is 25, 15, 13, 11, and 7, the number of combinations ( ６ C ２ It was confirmed that the standard deviation of the differences was greater than the threshold value (8%) for all of the above (=15).

[0628] As described above, the index curve for uniquely identifying Crimson Seedless (skin + flesh) is made up of a plurality of curves k107, k113, k119, k125 with different numbers of integral values, the index curve for uniquely identifying Green Seedless (skin + flesh) is made up of a plurality of curves k108, k114, k120, k126 with different numbers of integral values, and the index curve for uniquely identifying Shine Muscat (skin + flesh) is made up of a plurality of curves k109, k115, k121, k127 with different numbers of integral values. The index curve for uniquely identifying Crimson Seedless (flesh only) is made up of a plurality of curves k110, k116, k122, k128 with different numbers of integral values, the index curve for uniquely identifying Green Seedless (flesh only) is made up of a plurality of curves k111, k117, k123, k129 with different numbers of integral values, and the index curve for uniquely identifying Shine Muscat (flesh only) is made up of a plurality of curves k112, k118, k124, k130 with different numbers of integral values.

[0629] As mentioned above, the "astringency" of grapes is determined by the Body index (-) shown in formula (10). ｔｈ Calculate the calculated Body Index (-) ｔｈ is calculated by substituting into equation (11).

[0630] Body index (-) ｔｈ is calculated based on the sums of the integral values ​​L(-)_sum_th, M(-)_sum_th, and H(-)_sum_th as shown in equation (10).

[0631] The sum of the integral values ​​L(-)_sum_th is calculated by multiplying the potential scanning speed by V ｒ＿ＬｏｗAmong the multiple integral values ​​calculated based on the cyclic voltammogram measured at a potential scanning rate of V, the sum of integral values ​​in a predetermined potential range of −1362 mV to −2501 mV is the sum of integral values ​​M(−)_sum_th. ｒ＿Ｍｉｄｄｌｅ Among the multiple integral values ​​calculated based on the cyclic voltammogram measured with the potential scanning rate set to , the sum of integral values ​​in a predetermined potential range of −1362 mV to −2501 mV is the sum of integral values ​​H(−)_sum_th. The sum of integral values ​​H(−)_sum_th is calculated by setting the potential scanning rate V ｒ＿Ｈｉｇｈ The integral value is the sum of the integral values ​​in a predetermined potential interval ranging from −1362 mV to −2501 mV out of the multiple integral values ​​calculated based on the cyclic voltammogram measured with the potential set to

[0632] FIG. 89 is a diagram showing the correspondence between the classes and integral values ​​when the number of integral values ​​is 37 and the classes and integral values ​​when the number of integral values ​​is 74.

[0633] Referring to FIG. 89, when the number of integral values ​​in a predetermined potential section in the range of −1362 mV to −2501 mV is 74, the integral values ​​in classes 1 to 74 are respectively ITG １＿７４ ~ITG ７４＿７４ is.

[0634] When the number of integral values ​​in the predetermined potential range of −1362 mV to −2501 mV is 37, the integral value ITG in class 1 １＿３７ is the integral ITG when the number of integrals is 74 １＿７４ , ITG ２＿７４ The integral value ITG in class 2 is the sum of ２＿３７ is the integral ITG when the number of integrals is 74 ３＿７４ , ITG ４＿７４ ..., the integral value ITG in class 36 ３６＿３７ is the integral ITG when the number of integrals is 74 ７１＿７４ , ITG ７２＿７４ The integral ITG in class 37 is the sum of ３７＿３７ is the integral ITG when the number of integrals is 74 ７３＿７４ , ITG ７４＿７４It consists of the sum of

[0635] As a result, when the number of integral values ​​is 37, the sum of the 37 integral values ​​is equal to the sum of the 74 integral values ​​when the number of integral values ​​is 74.

[0636] Then, although not shown in the figures, when the number of integral values ​​in a predetermined potential section ranging from -1362 mV to -2501 mV is 19, the sum of the 19 integral values ​​is equal to the sum of the 74 integral values ​​when the number of integral values ​​is 74, and when the number of integral values ​​in a predetermined potential section ranging from -1362 mV to -2501 mV is 9, the sum of the 9 integral values ​​is equal to the sum of the 74 integral values ​​when the number of integral values ​​is 74.

[0637] Therefore, the sum of the integral values ​​L(-)_sum_th when the number of integral values ​​in the predetermined potential range of -1362 mV to -2501 mV is 74 is ＿７４ , the sum of integral values ​​L(-)_sum_th when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 37. ＿３７ , the sum of integral values ​​L(-)_sum_th when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 19. ＿１９ and the sum of integral values ​​L(-)_sum_th when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 9. ＿９ are equal to each other.

[0638] Similarly, the sum of the integral values ​​M(-)_sum_th when the number of integral values ​​in the predetermined potential range of -1362 mV to -2501 mV is 74 is ＿７４ , the sum of integral values ​​M(-)_sum_th when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 37. ＿３７, the sum of integral values ​​M(-)_sum_th when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 19. ＿１９ and the sum of integral values ​​M(-)_sum_th when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 9. ＿９ are equal to each other, and are the sum of integral values ​​H(-)_sum_th when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 74. ＿７４ , the sum of integral values ​​H(-)_sum_th when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 37. ＿３７ , the sum of integral values ​​H(-)_sum_th when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 19. ＿１９ and the sum of integral values ​​H(-)_sum_th when the number of integral values ​​in a predetermined potential range of -1362 mV to -2501 mV is 9. ＿９ are equal to each other.

[0639] As a result, in the predetermined potential range of -1362 mV to -2501 mV, when the number of integral values ​​is 74, the Body index (-) ｔｈ＿７４ , Body index (-) when the number of integral values ​​is 37 ｔｈ＿３７ , Body index (-) when the number of integral values ​​is 19 ｔｈ＿１９ , and the Body index (-) when the number of integral values ​​is 9 ｔｈ＿９ are equal to each other.

[0640] In this case, even if the number of integral values ​​changes, the coefficient k ９does not change, the "astringency" of grapes when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 74, the "astringency" of grapes when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 37, the "astringency" of grapes when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 19, and the "astringency" of grapes when the number of integral values ​​in a predetermined potential section in the range of -1362 mV to -2501 mV is 9 are all equal to each other.

[0641] For the same reason, when the number of integral values ​​in a predetermined potential section in the range of −1362 mV to −2501 mV is 25, 15, 13, 11, and 7, the “astringency” of the grapes is the same.

[0642] Therefore, the diagnostic device 2 may perform a taste diagnosis of "grapes" using the above-described method based on an integral spectrum in which the number of integrals in a predetermined potential range of -1362 mV to -2501 mV is any of 7, 9, 11, 13, 15, 19, 25, 37, and 74.

[0643] FIG. 90 is a flowchart for explaining the operation of the diagnostic system 10 shown in FIG.

[0644] 90, when the operation of the diagnostic system 10 is started, the supply unit 121 of the sensor device 1 receives the potential scan range V_s and the potential scan rate V_s input to the measuring instrument 12 by the user of the sensor device 1. ｒ＿Ｌｏｗ , V ｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ is received (step S1).

[0645] In addition, the supply unit 121 of the sensor device 1 accepts a start signal input to the measuring instrument 12 by the user of the sensor device 1, and the measuring unit 122 of the sensor device 1 accepts an end signal input to the measuring instrument 12 by the user of the sensor device 1.

[0646] Then, upon receiving the start signal, the supply unit 121 of the sensor device 1 adjusts the potential applied to the solution (analysis target) at a scanning speed V ｒ＿Ｌｏｗ , Vｒ＿Ｍｉｄｄｌｅ , V ｒ＿Ｈｉｇｈ A potential V in the potential scan range V_s is applied to the solution while changing it between each of the potentials, and the measurement unit 122 measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, and also measures the current value I from the counter electrode 113, and measures the current-potential characteristics [I-V]_Low, [I-V]_Middle, and [I-V]_High of the cyclic voltammogram (step S2).

[0647] In this case, the supply unit 121 applies the potential V at a scanning speed V ｒ＿Ｌｏｗ A potential V in a potential scan range V_s is applied to the solution while changing the potential V in the potential scan range V_s. The measurement unit 122 measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, and also measures the current value I from the counter electrode 113, thereby measuring the current-potential characteristic [I-V]_Low of the cyclic voltammogram.

[0648] The supply unit 121 applies the potential V at a scanning speed V ｒ＿Ｍｉｄｄｌｅ A potential V in a potential scan range V_s is applied to the solution while changing the potential V, and the measurement unit 122 measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, and also measures the current value I from the counter electrode 113, thereby measuring the current-potential characteristic [IV]_Middle of the cyclic voltammogram.

[0649] Furthermore, the supply unit 121 applies the potential V at a scanning speed V ｒ＿Ｈｉｇｈ A potential V in a potential scan range V_s is applied to the solution while changing the potential V in the potential scan range V_s. The measurement unit 122 measures the potential V of the working electrode 112 based on the potential of the reference electrode 114, and also measures the current value I from the counter electrode 113 to measure the current-potential characteristic [I-V]_High of the cyclic voltammogram.

[0650] After step S2, the measurement unit 122 measures the current value I in the current-potential characteristic [IV]_Low. １＿Ｌｏｗ ~I ｄ＿Ｌｏｗ and potential V １＿Ｌｏｗ ~V ｄ＿Ｌｏｗ The correspondence relationship between the current and potential characteristics [IV]_Middle and the current value I １＿Ｍｉｄｄｌｅ ~I ｄ＿Ｍｉｄｄｌｅ and potential V １＿Ｍｉｄｄｌｅ ~Vｄ＿Ｍｉｄｄｌｅ and the current value I in the current-potential characteristic [IV]_High １＿Ｈｉｇｈ ~I ｄ＿Ｈｉｇｈ and potential V １＿Ｈｉｇｈ ~V ｄ＿Ｈｉｇｈ Measurement data MRS including the correspondence relationship is created (step S3).

[0651] Then, the measurement unit 122 determines whether or not to end the measurement of the cyclic voltammogram of the solution (step S4).

[0652] In this case, when the measurement unit 122 receives an end signal input to the measuring instrument 12 by the user of the sensor device 1, it determines to end the measurement, and when it does not receive an end signal, it determines not to end the measurement.

[0653] When it is determined in step S4 that the measurement of the cyclic voltammogram of the solution is not to be terminated, the operation of the sensor device 1 proceeds to step S1, and steps S1 to S4 are repeatedly executed until it is determined in step S4 that the measurement of the cyclic voltammogram of the solution is to be terminated.

[0654] In this case, in step S4, each time it is determined that the measurement is not to be terminated, the sensor 11 used to measure the cyclic voltammogram is discarded, and a sensor 11 not used to measure the cyclic voltammogram is attached to the measuring instrument 12, and the above-mentioned steps S1 to S4 are executed sequentially.

[0655] Then, in step S4, when it is determined that the measurement of the cyclic voltammogram of the solution is to be terminated, the measurement unit 122 of the sensor device 1 outputs m (m is an integer equal to or greater than 1) pieces of measurement data MRS_1 to MRS_m that have been created when it is determined that the measurement of the cyclic voltammogram of the solution is to be terminated to the transmission unit 123. The transmission unit 123 receives the m pieces of measurement data MRS_1 to MRS_m from the measurement unit 122, and transmits the received m pieces of measurement data MRS_1 to MRS_m to the diagnostic device 2 by wired communication or wireless communication (step S5).

[0656] The receiving unit 211 of the diagnostic device 2 receives m pieces of measurement data MRS_1 to MRS_m from the transmitting unit 123 of the sensor device 1 by wired communication or wireless communication (step S6), and outputs the received m pieces of measurement data MRS_1 to MRS_m to the control unit 212.

[0657] The control unit 212 of the diagnostic device 2 receives the m pieces of measurement data MRS_1 to MRS_m from the receiving unit 211. Then, the control unit 212 generates m pieces of analysis data ALY_D based on the m pieces of measurement data MRS_1 to MRS_m. １ ~ ALY_D ｍ and m pieces of analysis data ALY_D １ ~ ALY_D ｍ m index data IDX １ ~IDX ｍ (step S7).

[0658] Then, the control unit 212 calculates m pieces of index data IDX １ ~IDX ｍ to the diagnosis unit (which is made up of the arithmetic unit 216 and the taste diagnosis unit 217).

[0659] The diagnostic unit (consisting of the calculation unit 216 and the taste diagnostic unit 217) has m index data IDX １ ~IDX ｍ The diagnostic unit (comprising the arithmetic unit 216 and the taste diagnostic unit 217) receives the m index data IDX １ ~IDX ｍ m pieces of calculation data CAL １ ~CAL ｍ , and the detected m pieces of calculation data CAL １ ~CAL ｍ The taste of each of the m analysis objects is diagnosed based on the above (step S8). This completes the operation of the diagnostic system 10.

[0660] FIG. 91 is a flowchart for explaining the detailed operation of step S7 in FIG.

[0661] 91, after step S6 in FIG. 90, the control unit 212 determines whether or not a plurality of measurement data have been received from the receiving unit 211 (step S71).

[0662] In this case, when the control unit 212 does not receive multiple measurement data from the receiving unit 211, it determines that it has received measurement data MRS_uni from the receiving unit 211, and when it receives multiple measurement data from the receiving unit 211, it determines that it has received P pieces of measurement data MRS_1 to MRS_P (multiple measurement data MRS) from the receiving unit 211.

[0663] In step S71, when it is determined that a plurality of pieces of measurement data have not been received, the control unit 212 generates one analysis data ALY_D based on one piece of measurement data MRS_uni by the above-described method. ｕｎｉ (step S72), and the created analysis data ALY_D ｕｎｉ is stored in the database 22 and the analysis data ALY_D ｕｎｉ to the arithmetic unit 213.

[0664] The calculation unit 213 calculates the analysis data ALY_D ｕｎｉ The calculation unit 213 and the creation unit 215 then receive the analysis data ALY_D ｕｎｉ The curve CUR showing the class dependency of the integral value based on ｕｎｉ is created as an index curve (step S73).

[0665] Then, the creating unit 215 creates the identification information ID ｕｎｉ and calculation data CAL ｕｎｉ and curve CUR ｕｎｉ The analysis result ALY_RLS is a result of associating ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] is created, and the created analysis result ALY_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] to the control unit 212.

[0666] The control unit 212 receives the analysis result ALY_RLS from the creation unit 215. ｕｎｉ and the analysis result ALY_RLS ｕｎｉ Based on this, the analysis data ALY_D ｕｎｉ The index data IDX ｕｎｉ (step S74), and the updated index data IDX ｕｎｉ The analysis data ALY_D ｕｎｉ Instead, the information is stored in the database 22.

[0667] On the other hand, when it is determined in step S71 that a plurality of pieces of measurement data have been received, the control unit 212 generates P pieces of analysis data ALY_D based on the P pieces of measurement data MRS_1 to MRS_P by the above-described method. １ ~ ALY_D Ｐ (Step S75), and the P pieces of analysis data ALY_D １ ~ ALY_D Ｐ are stored in the database 22, and P pieces of analysis data ALY_D １ ~ ALY_D Ｐ to the arithmetic unit 213.

[0668] The calculation unit 213 calculates P pieces of analysis data ALY_D １ ~ ALY_D Ｐ The calculation unit 213 and the creation unit 215 then receive the P pieces of analysis data ALY_D １ ~ ALY_D Ｐ P curves CUR showing the class dependency of the integral value based on １ ~CUR Ｐ are created as P index curves for the P analysis objects (step S76).

[0669] Then, the determination unit 214 determines the P curves CUR １ ~CUR Ｐ The determination unit 215 generates a determination result (determination result shown in Table 1) indicating whether or not the two are different (step S77).

[0670] When the generating unit 215 receives the determination results (determination results shown in Table 1) from the determining unit 214, it generates P calculation results CAL_RLS １ = [ID １ / CAL １ ]~CAL_RLS Ｐ = [ID Ｐ / CAL Ｐ ] each contains P curves CUR １ ~CUR Ｐ Add P analysis results ALY_RLS １ = [ID １ / CAL １ / CUR １ ]~ALY_RLS Ｐ = [ID Ｐ / CAL Ｐ / CUR Ｐ Then, the generating unit 215 generates P analysis results ALY_RLS １ ~ ALY_RLS Ｐ The determination result (determination result shown in Table 1) is output to the control unit 212.

[0671] Then, the control unit 212 outputs the P analysis results ALY_RLS １ ~ ALY_RLS Ｐ and the determination results (determination results shown in Table 1) are received from the creation unit 215, P analysis results ALY_RLS １ ~ ALY_RLS Ｐ Based on this, P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Each of the P index data IDX １ ~IDX Ｐ (step S78), and the updated P index data IDX １ ~IDX Ｐ Each of the P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Instead, the information is stored in the database 22.

[0672] In this case, the control unit 212 calculates the analysis result ALY_RLS ｐ (p is one of 1 to P) to identification information ID ｐ and calculation data CAL ｐ and curve CURｐ and the detected identification information ID ｐ Analysis data ALY_D having the same identification information as ｐ is read from the database 22, and the read analysis data ALY_D ｐ Calculation data CAL ｐ and the curve CUR ｐ By adding the analysis data ALY_D ｐ The index data IDX ｐ The P pieces of analysis data ALY_D are updated to １ ~ ALY_D Ｐ Execute all of the above.

[0673] As a result, P pieces of analysis data ALY_D １ ~ ALY_D Ｐ are P index data IDX １ ~IDX Ｐ will be updated to.

[0674] Then, the control unit 212 calculates the P index data IDX １ ~IDX Ｐ Each of the P pieces of analysis data ALY_D １ ~ ALY_D Ｐ and stores the determination results (determination results shown in Table 1) in the database 22 instead of the P index data IDX １ ~IDX Ｐ are stored in the database 22 in association with each other.

[0675] After step S74 or step S78, the operation of the diagnostic device 2 proceeds to step S8 in FIG.

[0676] According to the flowchart shown in FIG. 91, when it is determined in step S71 that a plurality of measurement data have not been received, the diagnostic device 2 calculates one curve CUR. ｕｎｉ (see step S73), and when it is determined in step S71 that a plurality of measurement data have been received, P curves CUR １ ~CUR Ｐ and P curves CUR １ ~CUR Ｐand a determination result (determination result shown in Table 1) indicating whether or not the two are different (see steps S76 and S77).

[0677] Therefore, the diagnostic device 2 uses the curve CUR as an index curve, which is a curve that serves as an index when identifying the object to be analyzed. ｕｎｉ (or P curves CUR １ ~CUR Ｐ ) can be created.

[0678] FIG. 92 is a flowchart for explaining the detailed operation of step S73 in FIG.

[0679] In FIG. 92, the potential scanning speed V ｒ is represented by "S". And "S=1" means that the potential scanning speed V ｒ＿Ｌｏｗ "S=2" represents the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ "S=3" represents the potential scanning speed V ｒ＿Ｈｉｇｈ shall represent the following.

[0680] 92, after step S72 in FIG. 91, the arithmetic unit 213 generates one analysis data ALY_D ｕｎｉ is received from the control unit 212 (step S731).

[0681] Then, the arithmetic unit 213 sets S=1 (step S732) and k=1 (step S733), where k is an argument representing the predetermined potential section.

[0682] After step S733, the calculation unit 213 calculates the analysis data ALY_D ｕｎｉ Current-potential characteristics (I Ｓ -V Ｓ ) ｕｎｉ to a predetermined potential section V ｋ N combinations (Iox_1_k_S, Ird_1_k_S) to (Iox_N_k_S, Ird_N_k_S) of N oxidation wave current values ​​{Iox_1_k_S to Iox_N_k_S} and N reduction wave current values ​​{Ird_1_k_S to Ird_N_k_S} are detected (step S734).

[0683] Here, N is the number of potential sections V ｋrepresents the total number of unit potentials (for example, 1 mV) in the

[0684] Also, for example, one predetermined potential section V ｋ When [0 to 100 mV], the current-potential characteristic (I Ｓ -V Ｓ ) ｕｎｉ is the current value I when the potential V is scanned from 0 mV to 100 mV ０→１００ and the current value I when the potential V is scanned from 100 mV to 0 mV. １００→０ Therefore, the calculation unit 213 calculates the current value I when the potential V is scanned from 0 mV to 100 mV. ０→１００ is detected as [N current values ​​of the oxidation wave {Iox_1_k_S to Iox_N_k_S}], and the current value I when the potential V is scanned from 100 mV to 0 mV is １００→０ are detected as [N current values ​​of the reduction wave {Ird_1_k_S to Ird_N_k_S}], and N combinations (Iox_1_k_S, Ird_1_k_S) to (Iox_N_k_S, Ird_N_k_S) are detected. ｋ The same applies to the case where is outside the range of [0 to 100 mV].

[0685] After step S734, the calculation unit 23 calculates n ｕｉｐ = 1 (step S735). ｕｉｐ is one predetermined potential section V ｋ are arguments representing each of the N unit potentials in

[0686] After step S735, the calculation unit 213 calculates the current value (Iox_n ｕｉｐ _k_S) to the reduction wave current value (Ird_n ｕｉｐ _k_S) and subtract the result of the subtraction (R_sbt_n ｕｉｐ _k_S) is calculated (step S736).

[0687] Then, the calculation unit 213 calculates n ｕｉｐ It is determined whether or not .intg.=N (step S737).

[0688] In step S737, n ｕｉｐ If it is determined that n is not equal to N, the calculation unit 213ｕｉｐ = n ｕｉｐ +1 is set (step S738). After that, the operation of the diagnostic device 2 proceeds to step S736, and in step S737, n ｕｉｐ Steps S736 to S738 are repeatedly executed until it is determined that .intg. ...

[0689] Then, in step S737, n ｕｉｐ If it is determined that R_sbt_1_k_S to R_sbt_N_k_S are satisfied, the calculation unit 213 adds up the N subtraction results (R_sbt_1_k_S to R_sbt_N_k_S) and calculates the voltage V ｋ The integral value ITG ｋ＿Ｓ is calculated (step S739).

[0690] Thereafter, the calculation unit 213 calculates the voltage V ｋ is set as the class Cls_k_S, and the class Cls_k_S and the integral value ITG ｋ＿Ｓ Combination with (Cls_k_S, ITG ｋ＿Ｓ ) is created (step S740).

[0691] Then, the arithmetic unit 213 determines whether k=n (step S741), where n is the number of the predetermined potential section V ｋ The total number of

[0692] If it is determined in step S741 that k is not equal to n, the arithmetic unit 213 sets k to k+1 (step S742). Thereafter, the operation of the diagnostic device 2 proceeds to step S734, and steps S734 to S742 are repeatedly executed until it is determined in step S741 that k is equal to n.

[0693] Then, in step S741, when it is determined that k=n, the calculation unit 213 calculates the n combinations (Cls_1_S, ITG １＿Ｓ )~(Cls_n_S, ITG ｎ＿Ｓ ) is generated (step S743).

[0694] Then, the arithmetic unit 213 determines whether S=3 (step S744).

[0695] If it is determined in step S744 that S is not equal to 3, the arithmetic unit 213 sets S to S+1 (step S745). Then, the operation of the diagnostic device 2 proceeds to step S733. Then, steps S733 to S745 are repeatedly executed until it is determined in step S744 that S is equal to 3.

[0696] Then, in step S744, when it is determined that S=3, the calculation unit 213 calculates the three integral values ​​ITG in the class Cls_1. １＿１ , ITG １＿２ , ITG １＿３ Add up the total integral value ITG １＿１ +ITG １＿２ +ITG １＿３ Calculate the three integral values ​​ITG in class Cls_2 ２＿１ , ITG ２＿２ , ITG ２＿３ Add up the total integral value ITG ２＿１ +ITG ２＿２ +ITG ２＿３ Similarly, the three integral values ​​ITG in the class Cls_n are calculated. ｎ＿１ , ITG ｎ＿２ , ITG ｎ＿３ Add up the total integral value ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ Calculate.

[0697] Then, the calculation unit 213 calculates n combinations (Cls_1, ITG １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) is generated (step S746).

[0698] Then, the calculation unit 213 calculates n combinations (Cls_1, ITG １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ), and outputs the calculation result CAL_RLS=[identification information ID / calculated data CAL] in which the calculation data CAL is associated with the identification information ID of the analysis object to the creation unit 215.

[0699] The creation unit 215 receives the calculation result CAL_RLS=[identification information ID / calculated data CAL] from the calculation unit 213. Then, the creation unit 215 creates n combinations (Cls_1, ITG) from the calculation data CAL of the calculation result CAL_RLS. １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) is detected, and the detected n combinations (Cls_1, ITG １＿１ +ITG １＿２ +ITG １＿３ ), (Cl_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) is plotted to create a curve CUR showing the class dependency of the integral value (step S747).

[0700] After step S747, the operation of the diagnostic device 2 proceeds to step S74 in FIG.

[0701] In the flowchart shown in FIG. 92, the arithmetic unit 213 calculates the potential scanning speed V ｒ＿Ｌｏｗ Based on the cyclic voltammogram measured in the n classes Cls_1_1 to Cls_n_1, n integral values ​​ITG １＿１ ~ITG ｎ＿１Calculate the n combinations of class and integral value (Cls_1_1, ITG １＿１ )~(Cls_n_1, ITG ｎ＿１ ) and the potential scanning rate V ｒ＿Ｍｉｄｄｌｅ Based on the cyclic voltammogram measured in the n classes Cls_1_2 to Cls_n_2, n integral values ​​ITG １＿２ ~ITG ｎ＿２ Calculate the n combinations of class and integral value (Cls_1_2, ITG １＿２ )~(Cls_n_2, ITG ｎ＿２ ) and the potential scanning rate V ｒ＿Ｈｉｇｈ Based on the cyclic voltammogram measured in the n classes Cls_1_3 to Cls_n_3, n integral values ​​ITG １＿３ ~ITG ｎ＿３ Calculate the n combinations of class and integral value (Cls_1_3, ITG １＿３ )~(Cls_n_3, ITG ｎ＿３ ) to create a

[0702] Then, the calculation unit 213 calculates n combinations (Cls_1_1, ITG １＿１ )~(Cls_n_1, ITG ｎ＿１ ), n combinations (Cls_1_2, ITG １＿２ )~(Cls_n_2, ITG ｎ＿２ ) and n combinations (Cls_1_3, ITG １＿３ )~(Cls_n_3, ITG ｎ＿３ ) based on one class k (=predetermined potential section V ｋ ) Three integral values ​​ITG ｋ＿１ , ITG ｋ＿２ , ITG ｋ＿３ The sum of (ITG ｋ＿１ +ITG ｋ＿２ +ITG ｋ＿３ ) for all n classes to obtain n sums (ITG １＿１ +ITG １＿２ +ITG １＿３ ) ~ (ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) is calculated.

[0703] Then, the calculation unit 213 calculates n classes Cls_1 to Cls_n and n sums (ITG １＿１ +ITG １＿２ +ITG １＿３ ) ~ (ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) based on n combinations (Cls_1, ITG １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) is generated (see step S746), and the generated n combinations (Cls_1, ITG １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) to the creation unit 215.

[0704] The creation unit 215 creates n combinations (Cls_1, ITG １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３ ) from the arithmetic unit 213, and １＿１ +ITG １＿２ +ITG １＿３ ), (Cls_2, ITG ２＿１ +ITG ２＿２ +ITG ２＿３ ), ..., (Cls_n, ITG ｎ＿１ +ITG ｎ＿２ +ITG ｎ＿３) is plotted to create a curve CUR showing the class dependency of the integral value (see step S747).

[0705] FIG. 93 is a flowchart for explaining the detailed operation of step S72 in FIG.

[0706] 93, when it is determined in step S71 of FIG. 91 that a plurality of measurement data have not been received, control unit 212 of diagnostic device 2 receives measurement data MRS_uni from receiving unit 211. Then, control unit 212 refers to the timer and calculates the time t when measurement data MRS_uni is received. ｕｎｉ (step S721), and the identification information ID for identifying the measurement data MRS_uni is detected. ｕｎｉ is issued (step S722).

[0707] Then, the control unit 212 obtains the name of the object to be analyzed ALY_Na from the measurement data MRS_uni. ｕｎｉ , type of object to be analyzed ALY_Kd ｕｎｉ , potential scanning speed V ｒ＿Ｌｏｗ＿ｕｎｉ , V ｒ＿Ｍｉｄｄｌｅ＿ｕｎｉ , V ｒ＿Ｈｉｇｈ＿ｕｎｉ and current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ , (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉ , (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ is detected (step S723).

[0708] Then, the control unit 212 sets the potential scanning speed V ｒ＿Ｌｏｗ＿ｕｎｉ and current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｕｎｉ Then, measurement data MRS_Low_uni is created by associating the above with each other (step S724).

[0709] The control unit 212 also controls the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ＿ｕｎｉ and current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｕｎｉThen, measurement data MRS_Middle_uni is created in which the above are associated with each other (step S725).

[0710] Furthermore, the control unit 212 controls the scanning speed V ｒ＿Ｈｉｇｈ＿ｕｎｉ and current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｕｎｉ The measurement data MRS_High_uni is generated by associating the above with the above (step S726).

[0711] Then, the control unit 212 detects the time t ｕｎｉ , identification information ID ｕｎｉ , name of the object to be analyzed ALY_Na ｕｎｉ , type of object to be analyzed ALY_Kd ｕｎｉ and analysis data ALY_D in which the measurement data MRS_Low_uni, MRS_Middle_uni, and MRS_High_uni are associated with each other. ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / MRS_Low_uni / MRS_Middle_uni / MRS_High_uni] is created (step S727).

[0712] After step S727, the operation of the diagnostic device 2 proceeds to step S73 in FIG.

[0713] FIG. 94 is a flowchart for explaining the detailed operation of step S75 in FIG.

[0714] 94, when it is determined in step S71 of Fig. 91 that a plurality of pieces of measurement data have been received, control unit 212 of diagnostic device 2 receives P pieces of measurement data MRS_1 to MRS_P from receiving unit 211. Then, control unit 212 sets p=1 (step S751), where p is an argument representing each of the P pieces of measurement data MRS_1 to MRS_P, and p=1 to P.

[0715] After step S751, the control unit 212 refers to the timer and calculates the time t when the measurement data MRS_p is received. ｐ(step S752), and identification information ID for identifying the measurement data MRS_p ｐ is issued (step S753).

[0716] Then, the control unit 212 obtains the name ALY_Na of the object to be analyzed from the measurement data MRS_p. ｐ , type of object to be analyzed ALY_Kd ｐ , potential scanning speed V ｒ＿Ｌｏｗ＿ｐ , V ｒ＿Ｍｉｄｄｌｅ＿ｐ , V ｒ＿Ｈｉｇｈ＿ｐ and current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｐ , (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ , (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ is detected (step S754).

[0717] Then, the control unit 212 sets the potential scanning speed V ｒ＿Ｌｏｗ＿ｐ and current-potential characteristics (I Ｌｏｗ -V Ｌｏｗ ) ｐ The measurement data MRS_Low_p is generated by associating the above with the above (step S755).

[0718] The control unit 212 also controls the potential scanning speed V ｒ＿Ｍｉｄｄｌｅ＿ｐ and current-potential characteristics (I Ｍｉｄｄｌｅ -V Ｍｉｄｄｌｅ ) ｐ The measurement data MRS_Middle_p is created by associating the above with each other (step S756).

[0719] Furthermore, the control unit 212 controls the scanning speed V ｒ＿Ｈｉｇｈ＿ｐ and current-potential characteristics (I Ｈｉｇｈ -V Ｈｉｇｈ ) ｐ The measurement data MRS_High_p is generated by associating the above with the above (step S757).

[0720] Then, the control unit 212 detects the time t ｐ , identification information ID ｐ , name of the object to be analyzed ALY_Na ｐ , type of object to be analyzed ALY_Kd ｐand analysis data ALY_D in which the measurement data MRS_Low_p, MRS_Middle_p, and MRS_High_p are associated with each other. ｐ = [t ｐ / ID ｐ / ALY_Na ｐ / ALY_Kd ｐ / MRS_Low_p / MRS_Middle_p / MRS_High_p] is created (step S758).

[0721] Then, the control unit 212 determines whether p=P (step S759).

[0722] If it is determined in step S759 that p is not equal to P, control unit 212 sets p to p+1 (step S760). Thereafter, the operation of diagnostic device 2 proceeds to step S752, and steps S752 to S760 are repeatedly executed until it is determined in step S759 that p is equal to P.

[0723] If it is determined in step S759 that p=P, the operation of diagnostic device 2 proceeds to step S76 in FIG.

[0724] In the flowchart shown in FIG. 94, when it is determined in step S759 that p=P, P pieces of analysis data ALY_D １ ~ ALY_D Ｐ has been created.

[0725] FIG. 95 is a flowchart for explaining the detailed operation of step S76 in FIG.

[0726] 95, after step S75 in FIG. 91, the arithmetic unit 213 receives P pieces of analysis data ALY_D from the control unit 212. １ ~ ALY_D Ｐ (step S761).

[0727] Then, the arithmetic unit 213 sets p=1 (step S762), where p is the number of pieces of analysis data ALY_D １ ~ ALY_D Ｐ are arguments representing each of the above.

[0728] After step S762, the calculation unit 213 and the creation unit 215 generate the analysis data ALY_D ｐ 92. Based on this, steps S731 to S747 in FIG. 92 are executed in order to ｐ is created as the p-th index curve (step S763).

[0729] In this case, in step S731 of FIG. 92, "analysis data ALY_D ｕｎｉ " is "analysis data ALY_D ｐ " and in step S734 of FIG. 92, "analysis data ALY_D ｕｎｉ " is "analysis data ALY_D ｐ " and "current-potential characteristics (IV) ｕｎｉ " is "current-potential characteristics (IV) ｐ " can be read as

[0730] After step S763, the arithmetic unit 213 determines whether p=P (step S764).

[0731] If it is determined in step S764 that p is not equal to P, the arithmetic unit 213 sets p to p+1 (step S765). Thereafter, the operation of the diagnostic device 2 proceeds to step S763, and steps S763 to S765 are repeatedly executed until it is determined in step S764 that p is equal to P.

[0732] If it is determined in step S764 that p=P, the operation of diagnostic device 2 proceeds to step S77 in FIG.

[0733] According to the flowchart shown in FIG. 95, when it is determined in step S764 that p=P, P curves CUR １ ~CUR Ｐ are created as P index curves for the P analysis objects, respectively.

[0734] FIG. 96 is a flowchart for explaining the detailed operation of step S77 in FIG.

[0735] 96, after step S76 in FIG. 91, the determination unit 214 determines the P curves CUR １ ~CUR Ｐ P pieces of calculation data CAL for creating １ ~CAL Ｐ is received from the arithmetic unit 213 (step S771).

[0736] Then, the determination unit 214 calculates the P pieces of calculation data CAL １ ~CAL Ｐ From Z (Z = Ｐ C ２ ) set of two calculation data {CAL ｉ , CAL ｊ (i≠j)}_1~{CAL ｉ , CAL ｊ (i≠j)}_Z is selected (step S772). ｉ , CAL ｊ are different calculation data.

[0737] After step S772, the decision unit 214 sets z=1 (step S773), where z is the sum of the Z sets of two calculation data {CAL ｉ , CAL ｊ (i≠j)}_1~{CAL ｉ , CAL ｊ (i≠j)}_Z.

[0738] After step S773, the decision unit 214 calculates the two pieces of calculation data {CAL ｉ , CAL ｊ (i≠j)}_z, two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z are determined to be different, and the determination result JDGR ｚ is created (step S774).

[0739] In this case, the decision unit 214 determines the two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z are different.

[0740] Two calculation data {CAL ｉ, CAL ｊ (i≠j)}_z are the calculation data CAL shown in FIG. ｕｎｉ It has the same configuration as

[0741] As a result, the calculation data CAL ｉ _z is n classes Cls １ ~Cls ｎ The sum of n integral values ​​{ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ} ｉ _z~{ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ} ｉ _z. Also, calculation data CAL ｊ _z is n classes Cls １ ~Cls ｎ The sum of n integral values ​​{ITG １＿Ｌｏｗ +ITG １＿Ｍｉｄｄｌｅ +ITG １＿Ｈｉｇｈ} ｊ _z~{ITG ｎ＿Ｌｏｗ +ITG ｎ＿Ｍｉｄｄｌｅ +ITG ｎ＿Ｈｉｇｈ} ｊ Includes _z.

[0742] The decision unit 214 decides one class Cls ｇ The sum of the integral values ​​in (g = 1 to n) {ITG ｇ＿Ｌｏｗ +ITG ｇ＿Ｍｉｄｄｌｅ +ITG ｇ＿Ｈｉｇｈ} ｉ _z and the sum of the integral values ​​{ITG ｇ＿Ｌｏｗ +ITG ｇ＿Ｍｉｄｄｌｅ +ITG ｇ＿Ｈｉｇｈ} ｊ Difference DF from _z ｇ The calculation is performed for all g=1 to n to obtain n differential DFs. １ ~DF ｎ Calculate.

[0743] Then, the determination unit 214 calculates the n differences DF １ ~DF ｎ Standard deviation σ ｉ，ｊ Calculate.

[0744] Then, the determination unit 214 calculates the standard deviation σ ｉ，ｊ is the threshold σ ｔｈ When the two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z is determined to be different, and the standard deviation σ ｉ，ｊ is the threshold σ ｔｈ When the following is true, the two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z is determined to be not different.

[0745] Therefore, the judgment result JDGR ｚ is the two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z, two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z are different (◯) or the two calculation data {CAL ｉ , CAL ｊ (i≠j)}_z does not differ (x).

[0746] After step S774, the decision unit 214 decides whether z=Z (step S775). Here, Z is the sum of Z sets of two calculation data {CAL ｉ , CAL ｊ (i≠j)}_1~{CAL ｉ , CAL ｊ (i≠j)}_Z.

[0747] If it is determined in step S775 that z is not equal to Z, the determination unit 214 sets z to z+1 (step S776). Thereafter, the operation of the determination unit 214 proceeds to step S774, and steps S774 to S776 are repeatedly executed until it is determined in step S775 that z is equal to Z.

[0748] Then, in step S775, if it is determined that z=Z, the determination unit 214 outputs Z determination results JDGR １ ~JDGR Ｚ Based on this, Z sets of two calculation data {CAL ｉ , CAL ｊ(i≠j)}_1~{CAL ｉ , CAL ｊ (i≠j)}_Z. ｉ , CAL ｊ A determination result indicating whether or not {i≠j}_z is different is generated (step S777).

[0749] After step S777, the operation of the diagnostic device 2 proceeds to step S78 in FIG.

[0750] FIG. 97 is a flowchart for explaining the detailed operation of step S8 in FIG.

[0751] 97, after step S7 in FIG. 90, the arithmetic unit 216 calculates m pieces of index data IDX １ ~IDX ｍ from the control unit 212.

[0752] Then, the arithmetic unit 216 determines whether or not a plurality of index data have been received from the control unit 212 (step S81).

[0753] In this case, when the calculation unit 216 does not receive a plurality of index data from the control unit 212, the calculation unit 216 does not receive the index data IDX ｕｎｉ is received from the control unit 212, and when a plurality of index data are received from the control unit 212, P index data IDX １ ~IDX Ｐ It is determined that (plurality of index data IDX) has been received from the control unit 212.

[0754] In step S81, when it is determined that a plurality of index data have not been received from the control unit 212, the arithmetic unit 216 and the taste diagnosis unit 217 ｕｎｉ The taste of the object to be analyzed is diagnosed based on the above (step S82).

[0755] On the other hand, when it is determined in step S81 that a plurality of index data have been received from the control unit 212, the arithmetic unit 216 and the taste diagnosis unit 217 receive the P index data IDX １ ~IDX ＰThe taste of the P analysis objects is diagnosed based on the (plurality of index data IDX) (step S83).

[0756] After step S82 or step S83, the operation of the diagnostic device 2 proceeds to "End" in FIG.

[0757] FIG. 98 is a flowchart for explaining the detailed operation of step S82 in FIG.

[0758] 98, when it is determined in step S81 of FIG. 97 that a plurality of index data have not been received, the arithmetic unit 216 receives the index data IDX ｕｎｉ Calculation data CAL ｕｎｉ The detected calculation data CAL ｕｎｉ n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ (= potential scanning speed V ｒ＿Ｌｏｗ n integral values ​​ITG in n predetermined potential sections calculated from the current-potential characteristics of the cyclic voltammogram measured by scanning the potential V １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ ) is used to calculate the sum L(+)_sum of the integral values ​​in a predetermined positive potential section (step S821).

[0759] The calculation unit 216 also calculates the calculation data CAL ｕｎｉ n integral values ​​ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ (= potential scanning speed V ｒ＿Ｍｉｄｄｌｅ n integral values ​​ITG in n predetermined potential sections calculated from the current-potential characteristics of the cyclic voltammogram measured by scanning the potential V １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ ) to calculate the sum M(+)_sum of the integral values ​​in the predetermined positive potential section (step S822).

[0760] Furthermore, the calculation unit 216 calculates the calculation data CAL ｕｎｉ n integral values ​​ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ (= potential scanning speed V ｒ＿Ｈｉｇｈn integral values ​​ITG in n predetermined potential sections calculated from the current-potential characteristics of the cyclic voltammogram measured by scanning the potential V １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ ) is used to calculate the sum H(+)_sum of the integral values ​​in the predetermined positive potential section (step S823).

[0761] Furthermore, the calculation unit 216 calculates n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ The sum L(all)_sum of the integral values ​​in all predetermined potential sections is calculated based on the １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ The sum M(all)_sum of the integral values ​​in all predetermined potential sections is calculated based on the １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ The sum H(all)_sum of the integral values ​​in all predetermined potential sections is calculated based on the above (step S824).

[0762] Furthermore, the calculation unit 216 calculates n integral values ​​ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ Based on this, the sum H(-)_sum of the integral values ​​in the predetermined negative potential section is calculated (step S825).

[0763] Furthermore, the calculation unit 216 calculates n integral values ​​ITG １＿Ｈｉｇｈ ~ITG ｎ＿Ｈｉｇｈ Based on the threshold V ｔｈ The sum H(-)_sum_th of the integral values ​​in the following predetermined negative potential sections is calculated, and n integral values ​​ITG １＿Ｍｉｄｄｌｅ ~ITG ｎ＿Ｍｉｄｄｌｅ Based on the threshold V ｔｈ The sum M(-)_sum_th of the integral values ​​in the following predetermined negative potential sections is calculated, and n integral values ​​ITG １＿Ｌｏｗ ~ITG ｎ＿Ｌｏｗ Based on the threshold V ｔｈ The sum L(-)_sum_th of the integral values ​​in the following predetermined negative potential sections is calculated (step S826).

[0764] Furthermore, the arithmetic unit 216 calculates the Body index (+) by dividing the sum of the integral values ​​L(+)_sum and the sum of the integral values ​​M(+)_sum by the sum of the integral values ​​H(+)_sum (step S827).

[0765] Furthermore, the arithmetic unit 216 calculates the Body index (all) by dividing the sum of the integral values ​​L(all)_sum and the sum of the integral values ​​M(all)_sum by the sum of the integral values ​​H(all)_sum (step S828).

[0766] Furthermore, the arithmetic unit 216 calculates the Body index (-)_th by dividing the sum of the integral values ​​L(-)_sum_th and the sum of the integral values ​​M(-)_sum_th by the sum of the integral values ​​H(-)_sum_th (step S829).

[0767] Then, the arithmetic unit 216 outputs the Body index (+), the Body index (all), the sum of the integral values ​​H(+)_sum, the sum of the integral values ​​H(−)_sum, and the Body index (−)_th to the taste diagnosis unit 217 .

[0768] Taste diagnosis unit 217 receives Body index (+), Body index (all), the sum of integral values ​​H(+)_sum, the sum of integral values ​​H(−)_sum, and Body index (−)_th from calculation unit 216 .

[0769] Then, the taste diagnosis unit 217 diagnoses the "astringency," "aftertaste," "sweetness," "aroma," and "bitterness" of the object to be analyzed based on Body index (+), Body index (all), the sum of integral values ​​H(+)_sum, the sum of integral values ​​H(-)_sum, and Body index (-)_th (step S830).

[0770] In this case, the taste diagnosis unit 217 diagnoses the "astringency" of the analysis object (shochu) using Body index (+) and the "astringency" of the analysis object (grapes) using Body index (-)_th.

[0771] After step S830, the taste diagnosis unit 217 creates a diagnosis result JDR of the taste of the object to be analyzed, and outputs the created diagnosis result JDR of the taste to the control unit 212 and the display unit 218.

[0772] When the control unit 212 receives the taste diagnosis result JDR from the taste diagnosis unit 217 , it stores the received taste diagnosis result JDR in the database 22 .

[0773] Furthermore, when the display unit 218 receives the taste diagnosis result JDR from the taste diagnosis unit 217, it displays the received taste diagnosis result JDR.

[0774] After step S830, the operation of the diagnostic device 2 proceeds to "End" in FIG.

[0775] FIG. 99 is a flowchart for explaining the detailed operation of step S830 in FIG.

[0776] The flowchart shown in FIG. 99 is a flowchart for explaining the detailed operation of step S830 in FIG. 98 when the analysis target is "shochu."

[0777] 99, after step S829 in FIG. 98, the taste diagnosis unit 217 receives the Body index (+), the Body index (all), the sum of the integral values ​​H(+)_sum, and the sum of the integral values ​​H(−)_sum from the arithmetic unit 216.

[0778] Then, the taste diagnosis unit 217 calculates the Body index (+) and the coefficient k １ is substituted into formula (5) to calculate the astringency ASTG value of the object to be analyzed (shochu), and the calculated value is diagnosed as the astringency ASTG of the object to be analyzed (shochu) (step S8301).

[0779] After step S8301, the taste diagnosis unit 217 calculates the Body index (all) and the coefficient k ２ is substituted into equation (6) to calculate the value of the aftertaste LNGS of the object to be analyzed (shochu), and the calculated value is diagnosed as the aftertaste LNGS of the object to be analyzed (shochu) (step S8302).

[0780] After step S8302, the taste diagnosis unit 217 calculates the sum of the integral values ​​H(+)_sum and the coefficient k ３ , k ４ is substituted into equation (7) to calculate the sweetness SWT value, and the calculated value is diagnosed as the sweetness SWT of the object to be analyzed (shochu) (step S8303).

[0781] After step S8303, the taste diagnosis unit 217 calculates the sum of the integral values ​​H(-)_sum and the coefficient k ５ , k ６ is substituted into equation (8) to calculate the aroma SCT value, and the calculated value is diagnosed as the aroma SCT of the object to be analyzed (shochu) (step S8304).

[0782] After step S8304, the taste diagnosis unit 217 calculates the astringency ASTG, the sweetness SWT, and the coefficient k ７ , k ８ is substituted into equation (9) to calculate the bitterness BIT value, and the calculated value is diagnosed as the bitterness BIT of the object to be analyzed (shochu) (step S8305).

[0783] After step S8305, the operation of the diagnostic device 2 proceeds to "End" in FIG.

[0784] FIG. 100 is another flowchart for explaining the detailed operation of step S830 in FIG.

[0785] 100, after step S829 in FIG. 98, the taste diagnosis unit 217 receives the Body index (−)_th from the arithmetic unit 216.

[0786] Then, the taste diagnosis unit 217 calculates the Body index (-)_th and the coefficient k ９ is substituted into equation (11) to calculate the astringency ASTG value, and the calculated value is diagnosed as the astringency ASTG of the object to be analyzed (grapes) (step S8301A).

[0787] Thereafter, the operation of the diagnostic device 2 proceeds to "End" in FIG.

[0788] FIG. 101 is a flowchart for explaining the detailed operation of step S83 in FIG.

[0789] 101, when it is determined in step S81 of FIG. 97 that a plurality of index data have been received, the arithmetic unit 216 receives P index data IDX １ ~IDX Ｐ is received from the control unit 212.

[0790] Then, the calculation unit 216 sets p=1 (step S831) and calculates the index data IDX ｐ Calculation data CAL ｐ is detected (step S832).

[0791] Thereafter, the calculation unit 216 calculates the index data IDX ｐ Calculation data CAL ｐ Based on this, steps S821 to S829 in FIG. 98 are executed in sequence (step S833).

[0792] Then, the taste diagnosis unit 217 sequentially executes steps S8301 to S8305 in FIG. 99 based on the Body index (+), Body index (all), the sum of the integral values ​​H(+)_sum, and the sum of the integral values ​​H(-)_sum (step S834).

[0793] Then, the arithmetic unit 216 determines whether p=P (step S835).

[0794] If it is determined in step S835 that p is not equal to P, the arithmetic unit 216 sets p to p+1...

Claims

1. Calculate a first sum (L(+)_sum) which is a sum of the first integral values ​​in a positive predetermined potential interval based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using a current-potential characteristic of a first cyclic voltammogram measured while changing the potential at a first potential scanning rate; calculate a second sum (M(+)_sum) which is a sum of the second integral values ​​in the positive predetermined potential interval based on a plurality of second integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a second cyclic voltammogram measured while changing the potential at a second potential scanning rate faster than the first potential scanning rate; and calculate a second sum (M(+)_sum) which is a sum of the second integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a third cyclic voltammogram measured while changing the potential at a third potential scanning rate faster than the second potential scanning rate. a first arithmetic unit that calculates a third sum (H(+)_sum) which is a sum of the third integral values ​​in the positive predetermined potential intervals based on the plurality of third integral values ​​in the plurality of predetermined potential intervals, a fourth sum (H(-)_sum) which is a sum of the third integral values ​​in the negative predetermined potential intervals based on the plurality of third integral values ​​in the plurality of predetermined potential intervals, a fifth sum (L(all))_sum) which is a sum of the first integral values ​​in all of the predetermined potential intervals based on the plurality of first integral values ​​in the plurality of predetermined potential intervals, a sixth sum (M(all))_sum) which is a sum of the second integral values ​​in all of the predetermined potential intervals based on the plurality of second integral values ​​in the plurality of predetermined potential intervals, and a seventh sum (H(all))_sum) which is a sum of the third integral values ​​in all of the predetermined potential intervals based on the plurality of third integral values ​​in the plurality of predetermined potential intervals; and a taste diagnosis unit configured to diagnose the taste of a first analyte based on the first sum (L(+)_sum), the second sum (M(+)_sum), the third sum (H(+)_sum), the fourth sum (H(-)_sum), the fifth sum (L(all))_sum), the sixth sum (M(all))_sum, and the seventh sum (H(all))_sum).

2. The first arithmetic unit further calculates a first factor (Body index (+)) which is a factor attributable to a diffusion coefficient of a component of the first analyte when a positive potential is applied to the first analyte based on the first summation (L(+)_sum), the second summation (M(+)_sum), and the third summation (H(+)_sum), and calculates a second factor (Body index (all)) which is a factor attributable to a diffusion coefficient of a component of the first analyte when positive and negative potentials are applied to the first analyte based on the fifth summation (L(all))_sum), the sixth summation (M(all))_sum), and the seventh summation (H(all))_sum; 2. The diagnostic device according to claim 1, wherein the taste diagnostic unit diagnoses the "astringency" of the first analyte based on the first factor (Body index (+)), diagnoses the "aftertaste" of the first analyte based on the second factor (Body index (all)), diagnoses the "sweetness" of the first analyte based on the third sum (H(+)_sum), diagnoses the "aroma" of the first analyte based on the fourth sum (H(-)_sum), and diagnoses the "bitterness" of the first analyte based on the "astringency" of the first analyte and the "sweetness" of the first analyte.

3. The taste diagnostic unit multiplies the first factor (Body index (+)) by a coefficient k １ The multiplication result is diagnosed as the "astringency" of the first analyte, and the coefficient k is added to the second factor (Body index (all)). ２ The multiplication result is diagnosed as the "aftertaste" of the first analysis object, and the third sum (H(+)_sum) is multiplied by a coefficient k ３ The result of dividing by the coefficient k ４ The multiplication result is diagnosed as the “sweetness” of the first analyte, and the fourth sum (H(−)_sum) is multiplied by a coefficient k ５ The result of dividing by the coefficient k ６ The multiplication result is diagnosed as the "aroma" of the first analyte, and the "astringency" of the first analyte is determined by a coefficient k ７ The coefficient k is applied to the "sweetness" of the first analyte from the multiplication result. ８ 3. The diagnostic device according to claim 2, wherein the subtraction result obtained by multiplying the first analyte by the first component and subtracting the multiplication result is diagnosed as the "bitterness" of the first analyte.

4. The taste diagnostic unit performs a regression analysis using the first factor (Body index (+)) as an explanatory variable and the "astringency" as a response variable to obtain a regression equation, and calculates a value obtained by multiplying the first factor (Body index (+)) in the obtained regression equation as the coefficient k １ The second factor (Body index (all)) is used as an explanatory variable, and a regression analysis is performed using the "aftertaste" as a response variable to obtain a regression equation, and the value multiplied by the second factor (Body index (all)), which is an explanatory variable in the obtained regression equation, is determined as the coefficient k ２ The third sum (H(+)_sum) is used as an explanatory variable, and a regression analysis is performed using the “sweetness” as a response variable to obtain a regression equation, and the value by which the explanatory variable (=the third sum (H(+)_sum)) is divided in the obtained regression equation is determined as the coefficient k ３ The coefficient k is determined by multiplying the explanatory variable (=the third sum (H(+)_sum)). ４ A regression equation is determined by executing a regression analysis using the fourth sum (H(-)_sum) as an explanatory variable and the "aroma" as a response variable, and a value by which the explanatory variable (the fourth sum (H(-)_sum)) is divided in the regression equation is determined as the coefficient k ５ The coefficient k is determined by multiplying the explanatory variable (the fourth sum (H(-)_sum)). ６ A regression analysis is performed using the "astringency" and the "sweetness" as explanatory variables and the "bitterness" as a response variable to obtain a regression equation, and the value multiplied by the "astringency" in the obtained regression equation is determined as the coefficient k ７ The value multiplied by the "sweetness" is the coefficient k ８ The diagnostic device according to claim 3, wherein the value of 5. When the taste diagnosis unit diagnoses the “astringency”, the “aftertaste”, the “sweetness”, the “aroma” and the “bitterness” of v (v is an integer of 1 or more) first analysis objects, the coefficient k １ Value of the coefficient k ８ The value of the updated coefficient k １ Value of the coefficient k ８ The diagnostic device according to claim 3, wherein the "astringency", the "aftertaste", the "sweetness", the "aroma" and the "bitterness" of the first analyte are diagnosed using the values ​​of 6. The plurality of first integral values ​​are n １ １ (n １ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point of the division result is not zero, and then adding "1" to the integer. １ ２ (n １ ２ <n １ １ ) first integral values, n １ ３ (n １ ３ <n １ ２ ) first integral values, ..., and n １ ｂ (n １ ｂ <n １ ｂ－１ , b is an integer equal to or greater than 2) first integral values, ２ １ (n ２ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point of the division result is not zero, and then adding "1" to the integer. ２ ２ (n ２ ２ <n ２ １ ) second integral values, n ２ ３ (n ２ ３ <n ２ ２ ) second integral values, ..., and n ２ ｂ (n ２ ｂ <n ２ ｂ－１ , b is an integer equal to or greater than 2), and the plurality of third integral values ​​are n ３ １ (n ３ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is an addition result obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point of the division result is not zero, and then adding "1" to the integer obtained by rounding down the decimal point of the division result. ３ ２ (n ３ ２ <n ３ １ ) third integral values, n ３ ３ (n ３ ３ <n ３ ２ ) third integral values, ..., and n ３ ｂ (n ３ ｂ <n ３ ｂ－１ 2. The diagnostic device according to claim 1, wherein a is any one of a number of third integral values ​​(a, b is an integer equal to or greater than 2).

7. The first arithmetic unit further calculates an eighth sum (L(-)_sum_th) which is a sum of the first integral values ​​in a plurality of negative predetermined potential intervals consisting of a negative potential equal to or less than a threshold value based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of the first cyclic voltammogram, calculates a ninth sum (M(-)_sum_th) which is a sum of the second integral values ​​in the plurality of negative predetermined potential intervals consisting of a negative potential equal to or less than the threshold value based on a plurality of second integral values ​​in the plurality of predetermined potential intervals calculated using the current-potential characteristic of the second cyclic voltammogram, and 2. The diagnostic device according to claim 1, further comprising: a tenth sum (H(-)_sum_th) which is a sum of the third integral values ​​in the plurality of negative predetermined potential intervals consisting of negative potentials equal to or less than the threshold value, based on a plurality of third integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a cyclic voltammogram; and a third factor (Body index (-)_th) which is a factor attributable to a diffusion coefficient of a component of the second analyte when a negative potential equal to or less than the threshold value is applied to the second analyte, based on the eighth sum (L(-)_th), the ninth sum (M(-)_sum_th) and the tenth sum (H(-)_sum_th); and the taste diagnostic unit further diagnoses the "astringency" of the second analyte based on the third factor (Body index (-)_th).

8. The taste diagnostic unit multiplies the third factor (Body index (-)_th) by a coefficient k ９ The diagnostic device according to claim 7, wherein the multiplication result is diagnosed as "astringency" of the second analysis object.

9. The taste diagnostic unit performs a regression analysis using the third factor (Body index (-)_th) as an explanatory variable and the "astringency" as a response variable to obtain a regression equation, and calculates a value obtained by multiplying the third factor (Body index (-)_th) in the obtained regression equation as the coefficient k ９ The diagnostic device of claim 8, wherein the value of 10. When the taste diagnosis unit diagnoses the “astringency” of v (v is an integer of 1 or more) second analysis objects, the coefficient k ９ The value of the updated coefficient k ９ The diagnostic device according to claim 8, wherein the "astringency" of the second analyte is diagnosed using a value of 11. The sum of the first integral values ​​in the plurality of negative predetermined potential sections consisting of negative potentials below the threshold value is w １ １ (w １ １ is the sum of the first integral values, w １ ２ (w １ ２ <w １ １ ) the sum of the first integral values, w １ ３ (w13<w １ ２ ) the sum of the first integral values, ..., and w １ ｂ (w １ ｂ <w １ ｂ－１ , b is an integer equal to or greater than 2), the sum of the second integral values ​​in the plurality of negative predetermined potential sections consisting of negative potentials equal to or less than the threshold value is w ２ １ (w ２ １ is the sum of the second integral values, w ２ ２ (w ２ ２ <w ２ １ ) the sum of the second integral values, w ２ ３ (w ２ ３ <w ２ ２ ) sums of the second integral values, ..., and w ２ ｂ (w ２ ｂ <w ２ ｂ－１ , b is an integer equal to or greater than 2), the sum of the third integral values ​​in the plurality of negative predetermined potential sections consisting of negative potentials equal to or less than the threshold value is w ３ １ (w ３ １ is the sum of the third integral values, w ３ ２ (w ３ ２ <w ３ １ ) the sum of the third integral values, w ３ ３ (w ３ ３ <w ３ ２ ) the sum of the third integral values, ..., and w ３ ｂ (w ３ ｂ <w ３ ｂ－１ 8. The diagnostic device according to claim 7, wherein a is a sum of a number of third integral values ​​(a, b is an integer equal to or greater than 2).

12. A second arithmetic unit is further provided which calculates the first integral values ​​in the predetermined potential intervals based on the current-potential characteristics of the first cyclic voltammogram, calculates the second integral values ​​in the predetermined potential intervals based on the current-potential characteristics of the second cyclic voltammogram, and calculates the third integral values ​​in the predetermined potential intervals based on the current-potential characteristics of the third cyclic voltammogram, 2. The diagnostic device according to claim 1, wherein the first arithmetic unit calculates the first sum (L(+)_sum) and the fifth sum (L(all))_sum) based on the plurality of first integral values ​​calculated by the second arithmetic unit, calculates the second sum (M(+)_sum) and the sixth sum (M(all))_sum) based on the plurality of second integral values ​​calculated by the second arithmetic unit, and calculates the third sum (H(+)_sum), the fourth sum (H(-)_sum), and the seventh sum (H(all))_sum) based on the plurality of third integral values ​​calculated by the second arithmetic unit.

13. The diagnostic device according to claim 12, further comprising a creation unit that creates a curve indicating the dependence of the multiple sum integral values ​​on the multiple predetermined potential intervals based on the multiple predetermined potential intervals and the multiple sum integral values ​​respectively associated with the multiple predetermined potential intervals, as a feature of the first analysis object or the second analysis object, wherein the second calculation unit further calculates the multiple sum integral values, which is the sum of the first integral value, the second integral value and the third integral value in one of the predetermined potential intervals, for all of the multiple predetermined potential intervals, to calculate the multiple sum integral values, and outputs the multiple predetermined potential intervals and the multiple sum integral values ​​respectively associated with the multiple predetermined potential intervals to the creation unit.

14. A diagnostic device as described in claim 13, further comprising a judgment unit that judges whether or not P of the calculated data (P is an integer of 2 or more) include [the plurality of sum integral values] that are mutually different, the P of the calculated data being different from each other, and the creation unit that creates the P of the curves when the judgment unit judges that the P of the calculated data are mutually different from each other.

15. A diagnostic system comprising a diagnostic device according to any one of claims 1 to 14.

16. A first arithmetic unit calculates a first sum (L(+)_sum) which is a sum of the first integral values ​​in a positive predetermined potential interval based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using a current-potential characteristic of a first cyclic voltammogram measured while changing the potential at a first potential scanning rate, calculates a second sum (M(+)_sum) which is a sum of the second integral values ​​in the positive predetermined potential interval based on a plurality of second integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a second cyclic voltammogram measured while changing the potential at a second potential scanning rate faster than the first potential scanning rate, and calculates a plurality of third sums (M(+)_sum) which is a sum of the second integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a third cyclic voltammogram measured while changing the potential at a third potential scanning rate faster than the second potential scanning rate. a first step of calculating a third sum (H(+)_sum) which is the sum of the third integral values ​​in the positive predetermined potential interval based on the integral values ​​of H(+)_sum, calculating a fourth sum (H(-)_sum) which is the sum of the third integral values ​​in the negative predetermined potential interval based on the plurality of third integral values ​​in the plurality of predetermined potential intervals, calculating a fifth sum (L(all))_sum) which is the sum of the first integral values ​​in all of the predetermined potential intervals based on the plurality of first integral values ​​in the plurality of predetermined potential intervals, calculating a sixth sum (M(all))_sum) which is the sum of the second integral values ​​in all of the predetermined potential intervals based on the plurality of second integral values ​​in the plurality of predetermined potential intervals, and calculating a seventh sum (H(all))_sum) which is the sum of the third integral values ​​in all of the predetermined potential intervals based on the plurality of third integral values ​​in the plurality of predetermined potential intervals; a second step of a taste diagnosis unit diagnosing the taste of a first object to be analyzed based on the first sum (L(+)_sum), the second sum (M(+)_sum), the third sum (H(+)_sum), the fourth sum (H(-)_sum), the fifth sum (L(all))_sum), the sixth sum (M(all))_sum, and the seventh sum (H(all))_sum.

17. In the first step, the first arithmetic unit further calculates a first factor (Body index (+)) which is a factor attributable to a diffusion coefficient of a component of the first analyte when a positive potential is applied to the first analyte based on the first summation (L(+)_sum), the second summation (M(+)_sum), and the third summation (H(+)_sum), and calculates a second factor (Body index (all)) which is a factor attributable to a diffusion coefficient of a component of the first analyte when positive and negative potentials are applied to the first analyte based on the fifth summation (L(all))_sum), the sixth summation (M(all))_sum), and the seventh summation (H(all))_sum; 17. The program for causing a computer to execute the program of claim 16, wherein in the second step, the taste diagnosis unit diagnoses the "astringency" of the first analyte based on the first factor (Body index (+)), diagnoses the "aftertaste" of the first analyte based on the second factor (Body index (all)), assesses the "sweetness" of the first analyte based on the third sum (H(+)_sum), diagnoses the "aroma" of the first analyte based on the fourth sum (H(-)_sum), and diagnoses the "bitterness" of the first analyte based on the "astringency" of the first analyte and the "sweetness" of the first analyte.

18. In the second step, the taste diagnostic unit multiplies the first factor (Body index (+)) by a coefficient k １ The multiplication result is diagnosed as the "astringency" of the first analyte, and the coefficient k is added to the second factor (Body index (all)). ２ The multiplication result is diagnosed as the "aftertaste" of the first analysis object, and the third sum (H(+)_sum) is multiplied by a coefficient k ３ The result of dividing by the coefficient k ４ The multiplication result is diagnosed as the “sweetness” of the first analyte, and the fourth sum (H(−)_sum) is multiplied by a coefficient k ５ The result of dividing by the coefficient k ６ The multiplication result is diagnosed as the "aroma" of the first analysis object, and the "astringency" of the first analysis object is determined by the coefficient ７ The coefficient k is applied to the "sweetness" of the first analyte from the multiplication result. ８ The program for causing a computer to execute the program according to claim 17, wherein the program diagnoses a subtraction result obtained by multiplying the first analyte by the first analyte and subtracting the multiplication result as the "bitterness" of the first analyte.

19. In the second step, the taste diagnostic unit performs a regression analysis using the first factor (Body index (+)) as an explanatory variable and the "astringency" as a response variable to obtain a regression equation, and calculates a value obtained by multiplying the first factor (Body index (+)) in the obtained regression equation as the coefficient k １ The second factor (Body index (all)) is used as an explanatory variable, and a regression analysis is performed using the "aftertaste" as a response variable to obtain a regression equation, and the value multiplied by the second factor (Body index (all)), which is an explanatory variable in the obtained regression equation, is determined as the coefficient k ２ A regression equation is determined by performing a regression analysis using the third sum (H(+)_sum) as an explanatory variable and the “sweetness” as a response variable, and a value by which the explanatory variable (=the third sum (H(+)_sum)) is divided in the regression equation is determined as the coefficient k ３ The coefficient k is determined by multiplying the explanatory variable (=the third sum (H(+)_sum)). ４ A regression equation is determined by executing a regression analysis using the fourth sum (H(-)_sum) as an explanatory variable and the "aroma" as a response variable, and a value by which the explanatory variable (the fourth sum (H(-)_sum)) is divided in the regression equation is determined as the coefficient k ５ The coefficient k is determined by multiplying the explanatory variable (the fourth sum (H(-)_sum)). ６ A regression analysis is performed using the "astringency" and the "sweetness" as explanatory variables and the "bitterness" as a response variable to obtain a regression equation, and the value multiplied by the "astringency" in the obtained regression equation is determined as the coefficient k ７ The value multiplied by the "sweetness" is the coefficient k ８ The program product executed by a computer according to claim 18, wherein the program product is determined as a value of 20. In the second step, the taste diagnosis unit diagnoses the “astringency”, the “aftertaste”, the “sweetness”, the “aroma”, and the “bitterness” of the v (v is an integer of 1 or more) first analysis objects, and calculates the coefficient k １ Value of the coefficient k ８ The value of the updated coefficient k １ Value of the coefficient k ８ The program for causing a computer to execute the program according to claim 18, wherein the "astringency", the "aftertaste", the "sweetness", the "aroma" and the "bitterness" of the first analysis object are diagnosed using the values ​​of 21. The plurality of first integral values ​​are n １ １ (n １ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point of the division result is not zero, and then adding "1" to the integer. １ ２ (n １ ２ <n １ １ ) first integral values, n １ ３ (n １ ３ <n １ ２ ) first integral values, ..., and n １ ｂ (n １ ｂ <n １ ｂ－１ , b is an integer equal to or greater than 2) first integral values, ２ １ (n ２ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is the sum of the integer obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point of the division result is not zero, and then adding "1" to the integer. ２ ２ (n ２ ２ <n ２ １ ) second integral values, n ２ ３ (n ２ ３ <n ２ ２ ) second integral values, ..., and n ２ ｂ (n ２ ｂ <n ２ ｂ－１ , b is an integer equal to or greater than 2), and the plurality of third integral values ​​are n ３ １ (n ３ １ is the number of integral values ​​when the integral value is calculated using the smallest predetermined potential section, and is an addition result obtained by rounding down the decimal point of the division result obtained by dividing the positive potential section by the smallest predetermined potential section when the decimal point of the division result is not zero, and then adding "1" to the integer obtained by rounding down the decimal point of the division result. ３ ２ (n ３ ２ <n ３ １ ) third integral values, n ３ ３ (n ３ ３ <n ３ ２ ) third integral values, ..., and n ３ ｂ (n ３ ｂ <n ３ ｂ－１ 17. The program for causing a computer to execute the program according to claim 16, wherein a is any one of a number of third integral values ​​(a, b is an integer equal to or greater than 2).

22. In the first step, the first arithmetic unit further calculates an eighth sum (L(-)_sum_th) which is a sum of the first integral values ​​in a plurality of negative predetermined potential intervals consisting of a negative potential equal to or less than a threshold value, based on a plurality of first integral values ​​in a plurality of predetermined potential intervals calculated using the current-potential characteristic of the first cyclic voltammogram, and calculates a ninth sum (M(-)_sum_th) which is a sum of the second integral values ​​in a plurality of negative predetermined potential intervals consisting of a negative potential equal to or less than a threshold value, based on a plurality of second integral values ​​in the plurality of predetermined potential intervals calculated using the current-potential characteristic of the second cyclic voltammogram.

17. The program for causing a computer to execute the program according to claim 16, wherein the taste diagnosis unit further diagnoses the "astringency" of the second analyte based on the third factor (Body index (-)_th), the second step further comprising: calculating a tenth sum (H(-)_sum_th) which is a sum of the third integral values ​​in the plurality of predetermined potential intervals consisting of negative potentials equal to or less than the threshold value based on a plurality of third integral values ​​in the plurality of predetermined potential intervals calculated using the current-potential characteristic of the third cyclic voltammogram; and calculating a third factor (Body index (-)_th) which is a factor attributable to a diffusion coefficient of a component of the second analyte when a negative potential equal to or less than the threshold value is applied to the second analyte based on the eighth sum (L(-)_th), the ninth sum (M(-)_sum_th) and the tenth sum (H(-)_sum_th).

23. In the second step, the taste diagnostic unit calculates the third factor (Body index (-)_th) by the coefficient k ９ and diagnosing the multiplication result as being "astringency" of the second analysis object.

24. In the second step, the taste diagnosis unit performs a regression analysis using the third factor (Body index (-)_th) as an explanatory variable and the "astringency" as a response variable to obtain a regression equation, and calculates a value obtained by multiplying the third factor (Body index (-)_th) in the obtained regression equation as the coefficient k ９ The program product executed by a computer according to claim 23, wherein the program product is determined as a value of 25. In the second step, the taste diagnosis unit diagnoses the “astringency” of v (v is an integer of 1 or more) second analysis objects, and calculates the coefficient k ９ The value of the updated coefficient k ９ The program for causing a computer to execute the program according to claim 23, wherein the "astringency" of the second analyte is diagnosed using the value of 26. The sum of the first integral values ​​in the plurality of negative predetermined potential sections consisting of negative potentials below the threshold value is w １ １ (w １ １ is the sum of the first integral values, w １ ２ (w １ ２ <w １ １ ) the sum of the first integral values, w １ ３ (w １ ３ <w １ ２ ) the sum of the first integral values, ..., and w １ ｂ (w １ ｂ <w １ ｂ－１ , b is an integer equal to or greater than 2), the sum of the second integral values ​​in the plurality of negative predetermined potential sections consisting of negative potentials equal to or less than the threshold value is w ２ １ (w ２ １ is the sum of the second integral values, w ２ ２ (w ２ ２ <w ２ １ ) the sum of the second integral values, w ２ ３ (w ２ ３ <w ２ ２ ) sums of the second integral values, ..., and w ２ ｂ (w ２ ｂ <w ２ ｂ－１ , b is an integer equal to or greater than 2), the sum of the third integral values ​​in the plurality of negative predetermined potential sections consisting of negative potentials equal to or less than the threshold value is w ３ １ (w ３ １ is the sum of the third integral values, w ３ ２ (w ３ ２ <w ３ １ ) the sum of the third integral values, w ３ ３ (w ３ ３ <w ３ ２ ) the sum of the third integral values, ..., and w ３ ｂ (w ３ ｂ <w ３ ｂ－１ 23. The program for causing a computer to execute the program according to claim 22, wherein the program is any one of sums of a number of third integral values ​​(a, b is an integer equal to or greater than 1).

27. The method further causes the computer to execute a third step in which a second arithmetic unit calculates the first integral values ​​in the predetermined potential intervals based on the current-potential characteristics of the first cyclic voltammogram, calculates the second integral values ​​in the predetermined potential intervals based on the current-potential characteristics of the second cyclic voltammogram, and calculates the third integral values ​​in the predetermined potential intervals based on the current-potential characteristics of the third cyclic voltammogram; 17. The program for causing a computer to execute the program of claim 16, wherein in the first step, the first arithmetic unit calculates the first sum (L(+)_sum) and the fifth sum (L(all))_sum) based on the plurality of first integral values ​​calculated by the second arithmetic unit, calculates the second sum (M(+)_sum) and the sixth sum (M(all))_sum) based on the plurality of second integral values ​​calculated by the second arithmetic unit, and calculates the third sum (H(+)_sum), the fourth sum (H(-)_sum), and the seventh sum (H(all))_sum) based on the plurality of third integral values ​​calculated by the second arithmetic unit.

28. A program for causing a computer to execute the program described in claim 27, further comprising a fourth step in which a creation unit causes a computer to execute a fourth step of creating a curve indicating the dependence of the multiple sum integral values ​​on the multiple predetermined potential intervals as a feature of the first analysis object or the second analysis object based on the multiple predetermined potential intervals and the multiple sum integral values ​​respectively associated with the multiple predetermined potential intervals, and the second calculation unit, in the third step, further calculates the multiple sum integral values, which are the sum of the first integral value, the second integral value, and the third integral value in one of the predetermined potential intervals, for all of the multiple predetermined potential intervals to calculate the multiple sum integral values, and outputs the multiple predetermined potential intervals and the multiple sum integral values ​​respectively associated with the multiple predetermined potential intervals to the creation unit.

29. A program for causing a computer to execute the program described in claim 28, wherein the calculation data includes the plurality of predetermined potential sections and the plurality of sum integral values ​​respectively corresponding to the plurality of predetermined potential sections, and a judgment unit further causes the computer to execute a fifth step of judging whether or not P [several sum integral values] included in P (P is an integer of 2 or more) pieces of the calculation data differ from one another, and the creation unit creates P of the curves in the fourth step when the judgment unit judges in the fifth step that the P [several sum integral values] differ from one another.

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