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

Abstract

The diagnostic device calculates the scanning speed V of the potential r_Low , V r_Middle , V r_High Using the current-potential characteristics of three cyclic voltammograms measured while changing the potential, a plurality of integral values ITG in a plurality of predetermined potential intervals are calculated 1_Low ~ITG n_Low , ITG 1_Middle ~ITG n_Middle , ITG 1_High ~ITG n _ High Based on these, the sums of the integral values in the positive predetermined potential intervals, L(+)_sum, M(+)_sum, H(+)_sum, the sum of the integral values in the negative predetermined potential interval, H(-)_sum, and the sums of the integral values in all the predetermined potential intervals, L(all)_sum, M(all)_sum, H(all)_sum are calculated to diagnose the taste of the object to be analyzed

Description

Technical Field

[0001] This invention relates to a diagnostic apparatus, a diagnostic system using the same, and a program for causing a computer to execute.

Background Art

[0002] In solution analysis techniques for soft drinks, alcoholic beverages, tap water, urine, blood, etc., analysis methods such as FTIR (Fourier Transform Infrared Spectroscopy), gas chromatography, taste sensors, and Raman spectroscopy are used (Non-Patent Documents 1 to 3).

[0003] However, these devices are large and expensive, have problems with portability, and because the devices are complex, specialized knowledge is required for handling the devices.

[0004] Also, individual sensors for measuring temperature, humidity, total acidity (acidity of all types of acids in a solution), alcohol content, etc. can perform on-site measurement (measurement performed at the site), but many of them cannot be used alone to determine the state of a solution, and it is necessary to use a combination of multiple sensors.

[0005] Electrochemical sensors can simplify the measurement system, are small, inexpensive, and excellent in portability, and have high potential as an in-situ analysis technique for solutions in general.

Prior Art Documents

Non-Patent Documents

[0006]

Non-Patent Document 1

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

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

[0009] Further, 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 or the like based on a cyclic voltammogram of alcoholic beverages or the like.

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

Means for Solving the Problems

[0011] (Configuration 1) According to an embodiment of the present invention, the diagnostic apparatus includes a first arithmetic unit and a taste diagnosis unit. The first arithmetic unit calculates a first sum (L(+)_sum), which is the sum of 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 the current-potential characteristics of a first cyclic voltammogram measured while changing the potential at a scanning speed of a first potential. The first arithmetic unit also calculates a second sum (M(+)_sum), which is the sum of second integral values in a positive predetermined potential interval, based on a plurality of second integral values in a plurality of predetermined potential intervals calculated using the current-potential characteristics of a second cyclic voltammogram measured while changing the potential at a scanning speed of a second potential faster than the scanning speed of the first potential. The first arithmetic unit further calculates a third sum (H(+)_sum), which is the sum of third integral values in a positive predetermined potential interval, based on a plurality of third integral values in a plurality of predetermined potential intervals calculated using the current-potential characteristics of a third cyclic voltammogram measured while changing the potential at a scanning speed of a third potential faster than the scanning speed of the second potential. The first arithmetic unit calculates a fourth sum (H(-)_sum), which is the sum of third integral values in a negative predetermined potential interval, based on a plurality of third integral values in a plurality of predetermined potential intervals. The first arithmetic unit calculates a fifth sum (L(all)_sum), which is the sum of first integral values in all predetermined potential intervals, based on a plurality of first integral values in a plurality of predetermined potential intervals. The first arithmetic unit calculates a sixth sum (M(all)_sum), which is the sum of second integral values in all predetermined potential intervals, based on a plurality of second integral values in a plurality of predetermined potential intervals. The first arithmetic unit calculates a seventh sum (H(all)_sum), which is the sum of third integral values in all predetermined potential intervals, 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 analysis object 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 arithmetic unit further calculates a first factor (Body index (+)), which is a factor resulting from the diffusion coefficient 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 resulting from the diffusion coefficient 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 "fragrance" 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 diagnosis unit diagnoses the multiplication result obtained by multiplying the first factor (Body index (+)) by the coefficient k 1 as the "astringency" of the first analyte, diagnoses the multiplication result obtained by multiplying the second factor (Body index (all)) by the coefficient k 2 as the "aftertaste" of the first analyte, diagnoses the multiplication result obtained by multiplying the division result obtained by dividing the third sum (H(+)_sum) by the coefficient k 3 by the coefficient k 4 as the "sweetness" of the first analyte, diagnoses the multiplication result obtained by multiplying the division result obtained by dividing the fourth sum (H(-)_sum) by the coefficient k 5 by the coefficient k 6 as the "fragrance" of the first analyte, and diagnoses the "bitterness" of the first analyte from the multiplication result obtained by multiplying the "astringency" of the first analyte by the coefficient k 7 subtracting the multiplication result obtained by multiplying the "sweetness" of the first analyte by the coefficient k 8Diagnose that the subtraction result obtained by subtracting the multiplication result multiplied by is the "bitterness" of the first analysis object.

[0014] (Configuration 4) In Configuration 3, the taste diagnosis unit Performs a regression analysis with the first factor (Body index (+)) as the explanatory variable and "astringency" as the objective variable to obtain a regression equation, and determines the value multiplied by the first factor (Body index (+)) in the obtained regression equation as the coefficient k 1 of the value, Performs a regression analysis with the second factor (Body index (all)) as the explanatory variable and "aftertaste" as the objective variable to obtain a regression equation, and determines the value multiplied by the second factor (Body index (all)), which is the explanatory variable, in the obtained regression equation as the coefficient k 2 of the value, Performs a regression analysis with the third sum (H(+)_sum) as the explanatory variable and "sweetness" as the objective variable to obtain a regression equation, and determines the value obtained by dividing the explanatory variable (= the third sum (H(+)_sum)) in the obtained regression equation as the coefficient k 3 of the value, and determines the value multiplied by the explanatory variable (= the third sum (H(+)_sum)) as the coefficient k 4 of the value, Performs a regression analysis with the fourth sum (H(-)_sum) as the explanatory variable and "aroma" as the objective variable to obtain a regression equation, and determines the value obtained by dividing the explanatory variable (the fourth sum (H(-)_sum)) in the obtained regression equation as the coefficient k 5 of the value, and determines the value multiplied by the explanatory variable (the fourth sum (H(-)_sum)) as the coefficient k 6 of the value, Performs a regression analysis with "astringency" and "sweetness" as the explanatory variables and "bitterness" as the objective variable to obtain a regression equation, and determines the value multiplied by "astringency" in the obtained regression equation as the coefficient k 7 of the value, and determines the value multiplied by "sweetness" as the coefficient k 8 of the value.

[0015] (Configuration 5) In Configuration 3, when diagnosing the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of v (where v is an integer of 1 or more) first analysis objects, the coefficient k 1 value ~ coefficient k 8 value is updated, and the updated coefficient k 1 value ~ coefficient k 8 value is used to diagnose the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of the first analysis object.

[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 minimum predetermined potential interval, and when the decimal part of the division result obtained by dividing the positive potential interval by the minimum predetermined potential interval is not zero, it is the sum result obtained by adding "1" to the integer obtained by truncating the decimal part of the division result.) first integral values, n 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 of 2 or more) is any one of the first integral values, 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 minimum predetermined potential interval, and when the decimal part of the division result obtained by dividing the positive potential interval by the minimum predetermined potential interval is not zero, it is the sum result obtained by adding "1" to the integer obtained by truncating the decimal part of the division result.) second integral values, n 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 any one of (b is an integer of 2 or more) second integral values, The plurality of third integral values is n 3 1 (n 3 1 is the number of integral values when the integral value is calculated using the minimum predetermined potential interval, and when the decimal part of the division result obtained by dividing the positive potential interval by the minimum predetermined potential interval is not zero, it is the result of adding "1" to the integer obtained by rounding down the decimal part of the division result. ) third integral values, n 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 any one of (b is an integer of 2 or more) third integral values.

[0017] (Configuration 7) In Configuration 1, the first arithmetic unit further calculates an eighth sum (L(-)_sum_th), which is the sum of first integral values in a plurality of negative predetermined potential intervals consisting of negative potentials below a threshold value, based on the plurality of first integral values in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the first cyclic voltammogram; calculates a ninth sum (M(-)_sum_th), which is the sum of second integral values in a plurality of negative predetermined potential intervals consisting of negative potentials below a threshold value, based on the plurality of second integral values in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the second cyclic voltammogram; calculates a tenth sum (H(-)_sum_th), which is the sum of third integral values in a plurality of negative predetermined potential intervals consisting of negative potentials below a threshold value, based on the plurality of third integral values in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the third cyclic voltammogram; and calculates a third factor (Body index (-)_th), which is a factor due to the diffusion coefficient of the components of the second analysis object when a negative potential below the threshold value is applied to the second analysis object, 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 analysis object based on the third factor (Body index (-)_th).

[0018] (Configuration 8) In Configuration 7, the taste diagnosis unit diagnoses that the multiplication result of multiplying the third factor (Body index (-)_th) by the coefficient k 9 is the "astringency" of the second analysis object.

[0019] (Configuration 9) In Configuration 8, the taste diagnosis unit performs a regression analysis with the third factor (Body index (-)_th) as an explanatory variable and "astringency" as an objective variable to obtain a regression equation, and determines the value multiplied by the third factor (Body index (-)_th) in the obtained regression equation as the value of the coefficient k 9 .

[0020] (Configuration 10) In Configuration 8, when the taste diagnosis unit diagnoses the "astringency" of v (where v is an integer of 1 or more) second analysis objects, the coefficient k 9 updates the value of, and diagnoses the "astringency" of the second analysis object using the updated coefficient k 9 value of.

[0021] (Configuration 11) In Configuration 7, the sum of the first integral values in a plurality of negative predetermined potential intervals composed of negative potentials below the threshold is w 1 1 (w 1 1 is the result of adding "1" to the integer obtained by truncating the decimal part of the division result when the decimal part of the division result of dividing the negative potential interval below the threshold by the minimum predetermined potential interval is not zero.) the sum of w 1 2 (w 1 2 <w 1 1 ) the sum of the first integral values of w 1 3 (w 1 3 <w 1 2 ) the sum of the first integral values of, ···, and w 1 b (w 1 b <w 1 b-1 , b is an integer of 2 or more) is any of the sums of the first integral values, the sum of the second integral values in a plurality of negative predetermined potential intervals composed of negative potentials below the threshold is w 2 1 (w 2 1 is the result of adding "1" to the integer obtained by truncating the decimal part of the division result when the decimal part of the division result of dividing the negative potential interval below the threshold by the minimum predetermined potential interval is not zero.) the sum of w 2 2 (w 22 <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 any one of the sums of (b is an integer of 2 or more) second integral values, The sum of the third integral values in a plurality of negative predetermined potential intervals composed of negative potentials below the threshold is w 3 1 (w 3 1 is composed of the result of adding "1" to the integer obtained by truncating the decimal part of the division result when the decimal part of the division result obtained by dividing the negative potential interval below the threshold by the minimum predetermined potential interval is not zero. ) sum of the third integral values, w 3 2 (w 3 2 <w 3 1 ) 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 any one of the sums of (b is an integer of 2 or more) third integral values.

[0022] (Configuration 12) In Configuration 1, the diagnostic device further includes a second arithmetic unit. The second arithmetic unit 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 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.

[0023] (Configuration 13) In Configuration 12, the diagnostic device further includes a creation unit. The creation unit creates a curve showing the plurality of predetermined potential interval dependencies of the plurality of total integral values based on the plurality of predetermined potential intervals and the plurality of total integral values respectively associated with the plurality of predetermined potential intervals as a feature amount of the first analysis object or the second analysis object. The second arithmetic unit further calculates, for all of the plurality of predetermined potential intervals, a total 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 to calculate a plurality of total integral values, and outputs the plurality of predetermined potential intervals and the plurality of total integral values respectively associated with the plurality of predetermined potential intervals to the creation unit.

[0024] (Configuration 14) In Configuration 13, the calculation data includes a plurality of predetermined potential intervals and a plurality of total sum integral values respectively associated with the plurality of predetermined potential intervals. The determination unit determines whether P (P is an integer of 2 or more) total sum integral values included in P pieces of calculation data are different from each other. When it is determined that the P total sum integral values are different from each other, the creation unit creates P curves.

[0025] (Configuration 15) Further, according to an embodiment of the present invention, the 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 The first operation unit calculates a first sum (L(+)_sum) which is the sum of the first integral values in the positive predetermined potential intervals based on a plurality of first integral values calculated using the current-potential characteristics of the first cyclic voltammogram measured while changing the potential at the scanning speed of the first potential, calculates a second sum (M(+)_sum) which is the sum of the second integral values in the positive predetermined potential intervals based on a plurality of second integral values calculated using the current-potential characteristics of the second cyclic voltammogram measured while changing the potential at a second scanning speed faster than the scanning speed of the first potential, calculates a third sum (H(+)_sum) which is the sum of the third integral values in the positive predetermined potential intervals based on a plurality of third integral values calculated using the current-potential characteristics of the third cyclic voltammogram measured while changing the potential at a third scanning speed faster than the second scanning speed, calculates a fourth sum (H(-)_sum) which is the sum of the third integral values in the negative predetermined potential intervals based on a plurality of third integral values in a plurality of predetermined potential intervals, calculates a fifth sum (L(all)_sum) which is the sum of the first integral values in all the predetermined potential intervals based on a plurality of first integral values in a plurality of predetermined potential intervals, calculates a sixth sum (M(all)_sum) which is the sum of the second integral values in all the predetermined potential intervals based on a plurality of second integral values in a plurality of predetermined potential intervals, and calculates a seventh sum (H(all)_sum) which is the sum of the third integral values in all the predetermined potential intervals based on a plurality of third integral values in a plurality of predetermined potential intervals, and a first step of A program for causing a computer to execute a second step of diagnosing the taste of the first analysis object 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 arithmetic unit further calculates a first factor (Body index (+)), which is a factor resulting from the diffusion coefficient 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 resulting from the diffusion coefficient 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 "fragrance" 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, in the second step, the taste diagnosis unit diagnoses the multiplication result obtained by multiplying the first factor (Body index (+)) by the coefficient k 1 as the "astringency" of the first analyte, diagnoses the multiplication result obtained by multiplying the second factor (Body index (all)) by the coefficient k 2 as the "aftertaste" of the first analyte, diagnoses the multiplication result obtained by multiplying the division result obtained by dividing the third sum (H(+)_sum) by the coefficient k 3 by the coefficient k 4 as the "sweetness" of the first analyte, diagnoses the multiplication result obtained by multiplying the division result obtained by dividing the fourth sum (H(-)_sum) by the coefficient k 5 by the coefficient k 6 as the "fragrance" of the first analyte, and multiplies the "astringency" of the first analyte by the coefficient k 7From the multiplication result obtained by multiplying by, subtract the multiplication result obtained by multiplying by the coefficient k for the "sweetness" of the first analysis object 8 The subtraction result obtained by subtracting is diagnosed as the "bitterness" of the first analysis object.

[0029] (Configuration 19) In Configuration 18, in the second step, the taste diagnosis unit Performs a regression analysis with the first factor (Body index (+)) as the explanatory variable and "astringency" as the objective variable to obtain a regression equation, and the value multiplied by the first factor (Body index (+)) in the obtained regression equation is determined as the value of coefficient k 1 And Performs a regression analysis with the second factor (Body index (all)) as the explanatory variable and "aftertaste" as the objective variable to obtain a regression equation, and the value multiplied by the second factor (Body index (all)), which is the explanatory variable, in the obtained regression equation is determined as the value of coefficient k 2 And Performs a regression analysis with the third sum (H(+)_sum) as the explanatory variable and "sweetness" as the objective variable to obtain a regression equation, and the value obtained by dividing by the explanatory variable (= the third sum (H(+)_sum)) in the obtained regression equation is determined as the value of coefficient k, and the value multiplied by the explanatory variable (= the third sum (H(+)_sum)) is determined as the value of coefficient k 3 And 4 And Performs a regression analysis with the fourth sum (H(-)_sum) as the explanatory variable and "aroma" as the objective variable to obtain a regression equation, and the value obtained by dividing by the explanatory variable (the fourth sum (H(-)_sum)) in the obtained regression equation is determined as the value of coefficient k, and the value multiplied by the explanatory variable (the fourth sum (H(-)_sum)) is determined as the value of coefficient k 5 And 6 And Performs a regression analysis with "astringency" and "sweetness" as the explanatory variables and "bitterness" as the objective variable to obtain a regression equation, and the value multiplied by "astringency" in the obtained regression equation is determined as the value of coefficient k 7 And the value multiplied by "sweetness" is determined as the value of coefficient k 8 And determine.

[0030] (Configuration 20) In Configuration 18, when the taste diagnosis unit diagnoses the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of v (where v is an integer of 1 or more) first analysis objects in the second step, the coefficient k 1 value ~ coefficient k 8 value is updated, and the updated coefficient k 1 value ~ coefficient k 8 value is used to diagnose the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of the first analysis object.

[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 minimum predetermined potential interval, and when the decimal part of the division result obtained by dividing the positive potential interval by the minimum predetermined potential interval is not zero, it is the addition result obtained by adding "1" to the integer obtained by truncating the decimal part of the division result.) first integral values, n 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 any of the first integral values where b is an integer of 2 or more), the plurality of second integral values are n 2 1 (n 2 1is the number of integral values when the integral value is calculated using the minimum predetermined potential interval, and when the number of decimal places of the division result obtained by dividing the positive potential interval by the minimum predetermined potential interval is not zero, it consists of the addition result obtained by adding "1" to the integer obtained by rounding down the number of decimal places of the division result.) number of second integral values, n 2 2 (n 2 2 <n 2 1 ) number of second integral values, n 2 3 (n 2 3 <n 2 2 ) number of second integral values, ···, and n 2 b (n 2 b <n 2 b-1 , b is any one of the number of second integral values where b is an integer greater than or equal to 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 minimum predetermined potential interval, and when the number of decimal places of the division result obtained by dividing the positive potential interval by the minimum predetermined potential interval is not zero, it consists of the addition result obtained by adding "1" to the integer obtained by rounding down the number of decimal places of the division result.) number of third integral values, n 3 2 (n 3 2 <n 3 1 ) number of third integral values, n 3 3 (n 3 3 <n 3 2 ) number of third integral values, ···, and n 3 b (n 3 b <n 3 b-1 , b is any one of the number of third integral values where b is an integer greater than or equal to 2).

[0032] (Configuration 22) In Configuration 16, in the first step, the first arithmetic unit further calculates, 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, a sum of the first integral values in a plurality of negative predetermined potential intervals consisting of negative potentials below a threshold value, which is the eighth sum (L(-)_sum_th), calculates, 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 sum of the second integral values in a plurality of negative predetermined potential intervals consisting of negative potentials below a threshold value, which is the ninth sum (M(-)_sum_th), calculates, based on a plurality of third integral values in a plurality of predetermined potential intervals calculated using the current-potential characteristics of the third cyclic voltammogram, a sum of the third integral values in a plurality of negative predetermined potential intervals consisting of negative potentials below a threshold value, which is the tenth sum (H(-)_sum_th), and calculates a third factor (Body index (-)_th), which is a factor due to the diffusion coefficient of the components of the second analysis object when a negative potential below the threshold value is applied to the second analysis object, 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 analysis object based on the third factor (Body index (-)_th).

[0033] (Configuration 23) In Configuration 22, in the second step, the taste diagnosis unit diagnoses that the multiplication result of multiplying the third factor (Body index (-)_th) by a coefficient k 9 is 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 with the third factor (Body index (-)_th) as the explanatory variable and "astringency" as the objective variable to obtain a regression equation, and determines the value multiplied by the third factor (Body index (-)_th) in the obtained regression equation as the coefficient k 9 of the value.

[0035] (Configuration 25) In Configuration 23, in the second step, 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 of the value is updated, and the "astringency" of the second analysis object is diagnosed using the updated coefficient k 9 of the value.

[0036] (Configuration 26) In Configuration 22, the sum of the first integral values in a plurality of negative predetermined potential intervals composed of negative potentials below the threshold is w 1 1 (w 1 1 is the result of adding "1" to the integer obtained by truncating the decimal part of the division result when the decimal part of the division result obtained by dividing the negative potential interval below the threshold by the minimum predetermined potential interval is not zero.) the sum of w 1 2 (w 1 2 <w 1 1 ) the sum of the first integral values of w 1 3 (w 1 3 <w 1 2 ) the sum of the first integral values of w, ···, and w 1 b (w 1 b <w 1 b-1 , b is an integer of 2 or more) is any one of the sums of the first integral values, the sum of the second integral values in a plurality of negative predetermined potential intervals composed of negative potentials below the threshold is w2 1 (w 2 1 is the result of adding "1" to the integer obtained by truncating the decimal part of the division result when the decimal part of the division result of dividing the negative potential range below the threshold by the minimum predetermined potential range is not zero. The sum of (w 2 2 (w 2 2 <w 2 1 ) of the second integral values, w 2 3 (w 2 3 <w 2 2 ) of the second integral values, ···, and w 2 b (w 2 b <w 2 b-1 , b is any one of the sums of (where b is an integer of 2 or more) of the second integral values, The sum of the third integral values in a plurality of negative predetermined potential ranges consisting of negative potentials below the threshold is w 3 1 (w 3 1 is the result of adding "1" to the integer obtained by truncating the decimal part of the division result when the decimal part of the division result of dividing the negative potential range below the threshold by the minimum predetermined potential range is not zero. The sum of (w 3 2 (w 3 2 <w 3 1 ) of the third integral values, w 3 3 (w 3 3 <w 3 2 ) of the third integral values, ···, and w 3 b (w 3 b <w 3 b-1, b is any of the sums of (a being an integer of 2 or more) third integral values.

[0037] (Configuration 27) In Configuration 16, the second arithmetic unit 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 further causes the computer to execute a third step of 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 amount of the first analysis object or the second analysis object, a curve showing the plurality of predetermined potential dependencies of the plurality of sum integral values based on the plurality of predetermined potential intervals and the plurality of sum integral values respectively associated with the plurality of predetermined potential intervals. In the third step, the second arithmetic unit further calculates, for all of the plurality of predetermined potential intervals, 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, thereby calculating a plurality of sum integral values, and outputs the plurality of predetermined potential intervals and the plurality of sum integral values respectively associated with the plurality of 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 total sum integral values respectively associated with the plurality of predetermined potential intervals. The computer is further caused to execute a fifth step in which the determination unit determines whether P (where P is an integer of 2 or more) total sum integral values included in P pieces of calculation data are different from each other. When the determination unit determines in the fifth step that the P total sum integral values are different from each other, the creation unit creates P curves in the fourth step.

Advantages of the Invention

[0040] According to the embodiment of this invention, the diagnostic apparatus can diagnose the taste of alcoholic beverages or the like based on the cyclic voltammogram of alcoholic beverages or the like.

Brief Description of the Drawings

[0041]

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Embodiments for Carrying Out the Invention

[0042] Embodiments of the present invention will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are denoted by the same reference numerals and their description will not be repeated.

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

[0044] The diagnostic system 10 is arranged, for example, in 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, for example, measurement data of a cyclic voltammogram of an analyte composed of shochu or grapes (grape juice) by the cyclic voltammetry method, and transmits the measured measurement data of the cyclic voltammogram CVG to the diagnostic device 2 by wireless communication or wired communication.

[0046] The cyclic voltammetry method (CV (cyclic voltammetry) method) is a measurement method that analyzes a current-potential curve (cyclic voltammogram CVG) obtained by measuring the current flowing when the potential is repeatedly swept with an electrode disposed in a stationary solution, and examines redox characteristics and the like.

[0047] The cyclic voltammogram CVG is a current-potential curve measured by the cyclic voltammetry method, and the measurement data of the cyclic voltammogram CVG includes current-potential characteristics (I-V) in which current I and potential V are associated with each other.

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

[0049] Also, when the sensor device 1 transmits the measurement data of the cyclic voltammogram CVG to the diagnostic device 2 by wired communication, it 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 measurement data of the cyclic voltammogram CVG from the sensor device 1 by wireless communication or wired communication. Then, the diagnostic device 2 calculates, for all predetermined potential intervals, an integrated value in a predetermined potential interval of the current-potential characteristic (I-V) included in the measurement data based on the measurement data of the cyclic voltammogram CVG by the method described later, calculates a plurality of integrated values in a plurality of predetermined potential intervals, creates a curve CUR indicating the dependence of the integrated value on the predetermined potential interval as an [index curve which is a curve serving as an index when identifying the object to be analyzed], diagnoses the taste of shochu or grapes (grape juice) based on the plurality of integrated 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] Referring to FIG. 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 wirings 115 to 117.

[0053] In FIG. 2, the x-y plane is defined. The substrate 111 has, for example, a flat plate shape and is arranged along the x-y plane.

[0054] The wirings 115 to 117 are arranged along the x-axis direction (first direction) on the upper surface of the substrate 111. And the wiring 116 is arranged along the x-axis direction (first direction) with a predetermined interval (for example, 2 to 3 mm) from the wiring 115 in the y-axis direction (second direction orthogonal to the first direction). Also, the wiring 117 is arranged along the x-axis direction (first direction) with a predetermined interval (for example, 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 opposite to the measuring instrument 12 side of the wiring 115 and is electrically connected to the wiring 115. The counter electrode 113 is disposed on one end opposite to the measuring instrument 12 side of the wiring 116 and is electrically connected to the wiring 116. The reference electrode 114 is disposed on one end opposite to the measuring instrument 12 side of the wiring 117 and is electrically connected to the wiring 117.

[0056] The substrate 111 is made of, for example, any one of a printed circuit board (PCB), a plastic board, and 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, any one of boron (B)-doped diamond (BDD), a carbon electrode, glassy carbon (glass-like diamond), gold (Au), and 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 a 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 by hydrogen.

[0058] The working electrode 112 has, for example, a square planar shape with an area of 3×3 mm 2 and the counter electrode 113 has, for example, a square planar shape with an area of 3×3 mm 2 and the reference electrode 114 has, for example, a square planar shape with an area of 1×2 mm 2

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

[0060] When the working electrode 112 is made of glassy carbon, a cyclic voltammogram CVG can be measured in a wide range.​

[0061] The working electrode 112 is an electrode that exchanges electrons with the object to be analyzed. The counter electrode 113 is an electrode for returning to the system a current value equal to 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] The object to be analyzed, which consists of shochu or grapes (grape juice), is supplied to the region where the working electrode 112, the counter electrode 113, and the reference electrode 114 are arranged.

[0063] Referring to FIG. 3, the measuring instrument 12 has a recess 121A for inserting a part of the other end side of the sensor 11.

[0064] When the sensor 11 is electrically connected to the measuring instrument 12, a part of the other end side of the sensor 11 in the x-axis direction (the first direction) is inserted into the recess 121A of the measuring instrument 12. As a result, the wirings 115 to 117 of the sensor 11 are electrically connected to the measuring instrument 12. Also, when the sensor 11 is not electrically connected to the measuring instrument 12, a part of the other end side of the sensor 11 in the x-axis direction (the first direction) is pulled out from the recess 121A of the measuring instrument 12.

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

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

[0067] Thus, since the sensor 11 is discarded every time the measurement of the cyclic voltammogram is performed, regeneration of the electrodes (working electrode 112, counter electrode 113, and reference electrode 114) by physical polishing or the like and pretreatment of the sample (object to be analyzed) are unnecessary.

[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 supply unit 121, a measuring unit 122, and a transmission unit 123.

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

[0071] Here, the user of the sensor device 1 is, for example, a staff member of a restaurant such as a Japanese restaurant, a Chinese restaurant, and a Western restaurant, a sake brewer, and a staff member of a liquor store.

[0072] The measuring unit 122 is electrically connected to the working electrode 112, the counter electrode 113, and the reference electrode 114 by the wirings 115, 116, and 117, respectively. The measuring unit 122 measures the potential V of the working electrode 112 with 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 including the current-potential characteristics (I-V) in which the measured potential V and the current value I are associated with each other.

[0073] The scanning speed of the potential consists of, for example, any one of 0.3 V / sec, 0.5 V / sec, and 0.6 V / sec. The potential scan range is, for example, -2.5 V to +2.5 V.

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

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

[0079] In the embodiment of the present invention, the supply unit 121 supplies the potential V in the potential scan range of -2.5V to +2.5V to the working electrode 112 while changing the potential V at a scanning speed of 0.3V / sec. The measurement unit 122 measures the potential V of the working electrode 112 with 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 the current-potential characteristic (I-V)_Low in which the measured potential V and the current value I are associated with each other.

[0080] Further, the supply unit 121 supplies the potential V within the potential scan range of -2.5V to +2.5V to the working electrode 112 while changing the potential V at a scanning speed of 0.5V / sec. The measurement unit 122 measures the potential V of the working electrode 112 with reference to the potential of the reference electrode 114, measures the current value I from the counter electrode 113, and creates measurement data MRS_Middle including the current-potential characteristic (I-V)_Middle in which the measured potential V and the current value I are associated with each other.

[0081] Furthermore, the supply unit 121 supplies the potential V within the potential scan range of -2.5V to +2.5V to the working electrode 112 while changing the potential V at a scanning speed of 0.6V / sec. The measurement unit 122 measures the potential V of the working electrode 112 with reference to the potential of the reference electrode 114, measures the current value I from the counter electrode 113, and creates measurement data MRS_High including the current-potential characteristic (I-V)_High in which the measured potential V and the current value I are associated with each other.

[0082] Then, the measurement unit 122 creates measurement data MRS including the measurement data MRS_Low, the measurement data MRS_Middle, and the measurement data MRS_High, and outputs the created 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] FIG. 6 is a schematic diagram of the measurement data MRS. Referring to FIG. 6, the measurement data MRS includes the name of the object to be analyzed, the type of the object to be analyzed, and the measurement data MRS_Low, MRS_Middle, and MRS_High.

[0085] The name of the object to be analyzed consists of, for example, shochu or grapes. When the name of the object to be analyzed is shochu, the types of the object to be analyzed consist of, for example, potato shochu, barley shochu, rice shochu, and the like. Also, when the name of the object to be analyzed is "grapes", the types of the object to be analyzed consist of, for example, Green Seedless, Crimson Seedless, and Shine Muscat, and the like.

[0086] The measurement data MRS_Low includes the scanning speed V of the potential r_Low and the current-potential characteristic (I Low -V Low ). The current-potential characteristic (I Low -V Low ) consists of a configuration in which the potential V Low is associated with the current value I Low .

[0087] The potential V Low consists of V 1_Low ~V d_Low , and the current value I Low consists of I 1_Low ~I d_Low . The current values I 1_Low ~I d_Low are respectively associated with the potentials V 1_Low ~V d_Low .

[0088] And the potentials V 1_Low ~V d_Low are the potentials of the working electrode 112 measured with reference to the potential of the reference electrode 114 when the scanning speed of the potential is "V r_Low ", and the current values I 1_Low ~I d_Low are the current values I from the counter electrode 113 when the scanning speed of the potential is "V r_Low ".

[0089] The measurement data MRS_Middle includes the scanning speed V of the potential r_Middle and the current-potential characteristic (I Middle -V Middle ). The current-potential characteristic (I Middle -V Middle ) consists of a configuration in which the potential V Middle is associated with the current value I MiddleIt consists of a configuration in which they are associated with each other.

[0090] Potential V Middle is V 1_Middle ~V d_Middle and consists of a current value I Middle is I 1_Middle ~I d_Middle The current value I 1_Middle ~I d_Middle is respectively associated with the potential V 1_Middle ~V d_Middle

[0091] And the potential V 1_Middle ~V d_Middle is the potential of the working electrode 112 measured with reference to the potential of the reference electrode 114 when the scanning speed of the potential is "V r_Middle ", and the current value I 1_Middle ~I d_Middle is the current value I from the counter electrode 113 when the scanning speed of the potential is "V r_Middle ".

[0092] The measurement data MRS_High includes the scanning speed V of the potential r_High and the current-potential characteristics (I High -V High ). The current-potential characteristics (I High -V High ) consists of a configuration in which the potential V High and the current value I High are associated with each other.

[0093] The potential V High is V 1_High ~V d_High and consists of a current value I High is I 1_High ~I d_High The current value I 1_High ~I d_High is respectively associated with the potential V 1_High ~V d_High

[0094] And the potential V 1_High ~V d_High is the potential of the working electrode 112 measured with reference to the potential of the reference electrode 114 when the scanning speed of the potential is "V r_High ​​is the potential of the working electrode 112 measured with reference to the potential of the reference electrode 114, and the current value I 1_High ~I d_High is the current value I from the counter electrode 113 when the potential scanning rate is “V r_High ”.

[0095] Potential V Low When the scanning range is -2.5V to +2.5V, 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 are, respectively, 0V, 1mV, 2mV, 3mV, ···, 2499mV, 2500mV, 2499mV, ···, 2mV, 1mV, 0V, -1mV, -2mV, ···, -2499mV, -2500mV, -2499mV, ···, -2mV, -1mV, 0V. 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 consists of potentials that change every unit potential (=1mV). As a result, d represents twice the total number of unit potentials in the scanning range of the potential V.

[0096] Potential V 1_Middle ~V d_Middle and potential V 1_High ~V d_High are the same.

[0097] The measurement unit 122 receives the name of the analysis object, the type of the analysis object, and the potential scanning rates V r_Low , V r_Middle , V r_High from the user of the sensor device 1, measures the current-potential characteristics (I r_Low -V Low ) at the potential scanning rate V Low , and measures the current-potential characteristics (I r_Middle -V Middle -V Middle) is measured, and the scanning speed V of the potential r_High is used to measure the current-potential characteristics (I High -V High ). Then, the measurement unit 122 records the name of the object to be analyzed, the type of the object to be analyzed, the scanning speed V of the potential 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 ) to create measurement data MRS, 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 by wired communication or wireless communication.

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

[0100] Also, 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 the P cyclic voltammograms of P (P is an integer of 2 or more) objects to be analyzed by the cyclic voltammetry method, the measurement unit 122 of the sensor device 1 creates each of the P measurement data MRS_1 to MRS_P by the method described above, and the transmission unit 123 of the sensor device 1 transmits the P 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 measurement data MRS_1 to MRS_P has the same configuration as the measurement data MRS shown in FIG. 6.

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

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

[0104] Then, based on the measurement data MRS, the analysis / diagnosis unit 21 diagnoses the taste of the object to be analyzed (shochu or grape) by the method described later, associates the diagnosis result of the diagnosed taste with the object to be analyzed (shochu or grape), stores it in the database 22, and displays the diagnosis result of the taste of the object to be analyzed (shochu or grape).

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

[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 reception unit 211, a control unit 212, arithmetic 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 reception unit 211 receives the measurement data MRS from the measuring instrument 12 (transmission 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 measurement data.

[0109] The control unit 212 incorporates a timer. When the control unit 212 receives one piece of measurement data MRS_uni from the reception unit 211, it refers to the timer and determines the time t when the measurement data MRS_uni is received. uniDetect and issue identification information ID for identifying measurement data MRS_uni uni The measurement data MRS_uni has the same configuration as the measurement data MRS shown in FIG. 6.

[0110] Then, the control unit 212 determines the name ALY_Na of the analysis target uni the type ALY_Kd of the analysis target uni the scanning speed V of the potential r_Low_uni the current-potential characteristic (I Low -V Low ) uni the scanning speed V of the potential r_Middle_uni the current-potential characteristic (I Middle -V Middle ) uni the scanning speed V of the potential r_High_uni the current-potential characteristic (I High -V High ) uni from the measurement data MRS_uni.

[0111] After that, the control unit 212 creates measurement data MRS_Low_uni = {V r_Low_uni : (I Low -V Low ) uni} by associating the scanning speed V of the potential with the current-potential characteristic (I r_Low_uni -V Low -V Low ) uni}, measurement data MRS_Middle_uni = {V r_Middle_uni : (V Middle -V Middle ) uni} by associating the scanning speed V of the potential with the current-potential characteristic (I r_Middle_uni -V Middle -V Middle ) uni}, and measurement data MRS_High_uni = {V r_High_uni : (I High -V High ) uni} by associating the scanning speed V of the potential with the current-potential characteristic (I r_High_uni -V High -V High ) uni .

[0112] Then, the control unit 212, at time t uni , identification information ID uni , name of the object to be analyzed ALY_Na uni , type of the 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} are associated with each other to create analysis data ALY_D uni = [t uni / ID uni / ALY_Na uni / ALY_Kd uni / MRS_Low_uni / MRS_Middle_uni / MRS_HigH_uni].

[0113] And the control unit 212 stores the analysis data ALY_D uni in the database 22 and outputs the analysis data ALY_D uni to the arithmetic unit 213.

[0114] Also, when the control unit 212 receives P pieces of measurement data MRS_1 to MRS_P (i.e., a plurality of measurement data) from the reception unit 211, it refers to the timer and detects the times t 1 ~ t P when each of the P pieces of measurement data MRS_1 to MRS_P is received, and issues P pieces of identification information ID 1 ~ ID P for identifying each of the P pieces of measurement data MRS_1 to MRS_P. Here, 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. 6.

[0115] Then, the control unit 212 determines the name ALY_Na of the object to be analyzed p and the type ALY_Kd of the object to be analyzed p and the scanning speed V of the potential r_Low_p and the current-potential characteristic (I Low -V Low ) p and the scanning speed V of the potential r_Middle_p and the current-potential characteristic (I Middle -V Middle ) p and the scanning speed V of the potential r_High_p and the current-potential characteristic (I High -V High ) p from the measurement data MRS_p (where p is any one of 1 to P), and this is executed for all of the P measurement data MRS_1 to MRS_P (i.e., a plurality of measurement data).

[0116] After that, the control unit 212 creates measurement data MRS_Low_p that associates the scanning speed V of the potential r_Low_p and the current-potential characteristic (I Low -V Low ) p such that MRS_Low_p = {V r_Low_p : (I Low -V Low ) p}, measurement data MRS_Middle_p that associates the scanning speed V of the potential r_Middle_p and the current-potential characteristic (I Middle -V Middle ) p such that MRS_Middle_p = {V r_Middle_p : (I Middle -V Middle ) p}, and measurement data MRS_High_p that associates the scanning speed V of the potential r_High_p and the current-potential characteristic (I High -V High ) p such that MRS_High_p = {V r_High_p : (I High -V High ) p} for all p from 1 to P, and creates P measurement data MRS_Low_1 = {V r_Low_1 : (ILow -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} are created.

[0117] Then, the control unit 212, at time t p , identification information ID p , name of the object to be analyzed ALY_Na p , type of the 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 as analysis data ALY_D p = [t p / ID p / ALY_Na p / ALY_Kd pCreate [[Measurement Data MRS_Low_p] / [Measurement Data MRS_Middle_p] / [Measurement Data MRS_High_p]] for all p = 1 to P, and generate 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 Create [[Measurement Data MRS_Low_P] / [Measurement Data MRS_Middle_P] / [Measurement Data MRS_High_P]].

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

[0119] Furthermore, after the control unit 212 outputs the analysis data ALY_D uni to the arithmetic unit 213, when it receives the analysis result ALY_RLS uni associated with the identification information ID uni and the calculation data CAL uni and the curve CUR uni =[ID uni / CAL uni / CUR uni from the creation unit 215, it detects the identification information ID uni , the calculation data CAL uni and the curve CUR uni from the analysis result ALY_RLS uni , and reads out the analysis data ALY_D uni having the same identification information as the detected identification information ID uni from the database 22.

[0120] Then, the control unit 212 stores the analysis data ALY_D read from the database 22 uni with the calculation data CAL uni and the curve CUR uni to update the analysis data ALY_D uni to the index data IDX uni and outputs the updated index data IDX uni to the calculation unit 216.

[0121] After that, the control unit 212 stores the index data IDX uni in the database 22 instead of the analysis data ALY_D uni .

[0122] Furthermore, after the control unit 212 outputs P pieces of analysis data ALY_D 1 ~ALY_D P to the calculation unit 213, P pieces of identification information ID 1 ~ID P , P pieces of calculation data CAL 1 ~CAL P , and P pieces of curves CUR 1 ~CUR P are respectively associated with P pieces of analysis results ALY_RLS 1 = [ID 1 / CAL 1 / CUR 1 ~ALY_RLS P = [ID P / CAL P / CUR P , and when receiving from the creation unit 215 a determination result JDGR indicating whether P pieces of curves CUR 1 ~CUR P are mutually different, the analysis result ALY_RLS p = [ID p / CAL p / CUR p (p is any one of 1 to P) is used to detect the identification information ID p , the calculation data CAL p , and the curve CUR p , and the detected identification information ID pAnalysis data ALY_D having the same identification information p Read from the database 22 for all p = 1 to P.

[0123] Then, the control unit 212 stores the operation data CAL p and the curve CUR p in the analysis data ALY_D read from the database 22 p and updates the analysis data ALY_D p to the index data IDX p for all p = 1 to P.

[0124] After that, the control unit 212 outputs the P index data IDX 1 ~IDX P to the operation unit 216.

[0125] Then, the control unit 212 stores the P index data IDX 1 ~IDX P in the database 22 instead of the P analysis data ALY_D 1 ~ALY_D P respectively.

[0126] And the control unit 212 stores the determination result JDGR in the database 22 in association with the P index data IDX 1 ~index data IDX P respectively.

[0127] Furthermore, after the control unit 212 outputs the index data IDX uni to the operation unit 216, when receiving the diagnosis result JDR uni which is the result of diagnosing the taste (astringency, aftertaste, sweetness, aroma, and bitterness) of the object to be analyzed (shochu or grape), from the taste diagnosis unit 217, the received diagnosis result JDR uni is stored in the database 22 in association with the index data IDX uni respectively.

[0128] Furthermore, the control unit 2121 ~IDX P After outputting to the arithmetic unit 216, it is the result of diagnosing the taste (astringency, aftertaste, sweetness, aroma, and bitterness) of the object to be analyzed (shochu or grapes), which is P diagnosis results JDR 1 ~JDR P When receiving the P diagnosis results JDR 1 ~JDR P from the taste diagnosis unit 217, each of the received P diagnosis results JDR 1 ~IDX P is stored in the database 22 in association with the P index data IDX

[0129] Furthermore, the control unit 212 has the name ALY_Na of the object to be analyzed uni and the diagnosis result JDR uni associated with the name ALY_Na uni When receiving the request RQT uni from the reception unit 219 to display the name ALY_Na of the object to be analyzed uni Based on the name ALY_Na of the object to be analyzed uni the diagnosis result JDR uni associated with the name ALY_Na of the object to be analyzed is detected from the database 22, and the name ALY_Na of the object to be analyzed uni and the diagnosis result JDR uni are output to the display unit 218

[0130] Also, the control unit 212 has the P names ALY_Na of the P objects to be analyzed 1 ~ALY_Na P Among them, q (q is an integer satisfying 1 ≦ q ≦ P) names ALY_Na 1 ~ALY_Na q and the q diagnosis results JDR 1 ~ALY_Na q respectively associated with the q names ALY_Na 1 ~JDR q When receiving the request RQT q from the reception unit 219 to display the q names ALY_Na 1 ~ALY_Na q Based on the q names ALY_Na of the q objects to be analyzed 1 ~ALY_Naq and q names ALY_Na of q objects to be analyzed 1 ~ALY_Na q and q diagnostic results JDR respectively associated with them 1 ~JDR q are detected from the database 22, and q names ALY_Na of q objects to be analyzed 1 ~ALY_Na q and q diagnostic results JDR 1 ~JDR q are output to the display unit 218.

[0131] In this case, in the request RQT q q types ALY_Kd of q objects to be analyzed may be used instead of q names ALY_Na of q objects to be analyzed 1 ~ALY_Na q ~ALY_Kd 1 ~ALY_Kd q

[0132] The arithmetic unit 213 receives from the control unit 212 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}

[0133] Then, the arithmetic unit 213 receives 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}] of the current - potential characteristics (I Low -V Low ) uni ,(I Middle -V Middle ) uni ,(I High -V High ) uni} Based on this, for each predetermined potential interval V_ITV, the integration value ITG of the cyclic voltammogram CVG is calculated by the method described below, and this is executed for all the predetermined potential intervals V_ITV.

[0134] And when the number of the predetermined potential intervals V_ITV is n, the arithmetic unit 213 designates the first predetermined potential interval V_ITV as "class Cls 1 ", the second predetermined potential interval V_ITV as "class Cls 2 ", and so on. Similarly, the (n - 1)-th predetermined potential interval V_ITV is designated as "class Cls n-1 ", and the n-th predetermined potential interval V_ITV is designated as "class Cls n ".

[0135] And the arithmetic unit 213, based on the current - potential characteristics (I Low -V Low ) uni} of the measurement data MRS_Low_uni = {Low_uni:(I Low -V Low ) uni sets the integration value in the first predetermined potential interval V_ITV as ITG 1_Low , the integration value in the second predetermined potential interval V_ITV as ITG 2_Low , and so on. Similarly, the integration value in the (n - 1)-th predetermined potential interval V_ITV is set as ITG (n-1)_Low , and the integration value in the n-th predetermined potential interval V_ITV as ITGn_Low be set as

[0136] Also, the arithmetic unit 213 calculates the integral value ITG Middle -V Middle ) uni} of the current-potential characteristics (I Middle -V Middle ) uni in the first predetermined potential range V_ITV and sets it as ITG 1_Middle , calculates the integral value in the second predetermined potential range V_ITV and sets it as ITG 2_Middle , and so on. Similarly, it calculates the integral value in the (n - 1)-th predetermined potential range V_ITV and sets it as ITG n-1_Middle , and calculates the integral value in the n-th predetermined potential range V_ITV and sets it as ITG n_Middle be set as

[0137] Furthermore, the arithmetic unit 213 calculates the integral value ITG High -V High ) uni} of the current-potential characteristics (I High -V High ) uni in the first predetermined potential range V_ITV and sets it as ITG 1_High , calculates the integral value in the second predetermined potential range V_ITV and sets it as ITG 2_High , and so on. Similarly, it calculates the integral value in the (n - 1)-th predetermined potential range V_ITV and sets it as ITG n-1_High , and calculates the integral value in the n-th predetermined potential range V_ITV and sets it as ITG n_High be set as

[0138] Then, the arithmetic unit 213 adds the integral values ITG 1 in the class Cls 1_Low , ITG 1_Middle , ITG 1_High to calculate the total sum ITG 1_Low + ITG 1_Middle + ITG 1_High of the integral values, and for the integral values ITG 2 in the class Cls 2_Low , ITG2_Middle , ITG 2_High Add them to obtain the total integral value ITG 2_Low +ITG 2_Middle +ITG 2_High Calculate it. Then, in the same way, for the class Cls n the integral value ITG n_Low , ITG n_Middle , ITG n_High Add them to obtain the total integral value ITG n_Low +ITG n_Middle +ITG n_High Calculate it.

[0139] And the arithmetic unit 213 has n classes Cls 1 ~Cls n and n integral values ITG 1 ~ITG n respectively associated with the n classes Cls 1_Low ~ITG n_Low , ITG 1_Middle ~ITG n_Middle , ITG 1_High ~ITG n_High and the total sum of n integral values (ITG 1 ~Cls n +ITG 1_Low +ITG 1_Middle +ITG 1_High )~(ITG n_Low +ITG n_Middle +ITG n_High ) to create the arithmetic data CAL uni Create it.

[0140] After that, the arithmetic unit 213 creates the arithmetic result CAL_RLS uni by associating the arithmetic data CAL uni with the identification information ID uni =ID uni / CAL uni . And the arithmetic unit 213 outputs the arithmetic result CAL_RLS uni =ID uni / CAL uni and the signal S_u indicating that there is one piece of arithmetic data to the creation unit 215.

[0141] Also, the arithmetic unit 213 receives 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 -V Low ) 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] And the arithmetic unit 213 calculates 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}] received from the control unit 212 in the same manner, 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}] of the 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), 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 ) are associated to create the arithmetic data CAL p for all of the P analysis data ALY_D 1 ~ALY_D P to create P arithmetic data CAL 1 ~CAL P .

[0143] And the arithmetic unit 213 uses the P identification information IDs 1 ~ID P and the P arithmetic data CAL 1~CAL P The P calculation results CAL_RLS associated with them respectively 1 =[ID 1 / CAL 1 ~CAL_RLS P =[ID P / CAL P are output to the determination unit 214 and the creation unit 215.

[0144] In this way, when the calculation unit 213 creates one calculation data CAL uni the calculation result CAL_RLS uni =[ID uni / CAL uni is output only to the creation unit 215, and when creating P calculation results CAL_RLS 1 =[ID 1 / CAL 1 ~CAL_RLS P =[ID P / CAL P (that is, a plurality of calculation results), the P calculation results CAL_RLS 1 ~CAL_RLS P (that is, a plurality of calculation results) are output to the determination unit 214 and the creation unit 215.

[0145] Note that as described above, the calculation data CAL uni is composed of n levels 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 ), but each of the n levels Cls 1 ~Cls n consists of a predetermined potential interval V_ITV, so the calculation data CAL uniis the n predetermined potential intervals V_ITV 1 ~V_ITV n and the n integral values ITG 1_Low ~ITG n_Low , ITG 1_Middle ~ITG n_Middle , ITG 1_High ~ITGn _High and the sum of the n integral values (ITG 1_Low +ITG 1_Middle +ITG 1_High )~(ITG n_Low +ITG n_Middle +ITG n_High ) are associated with each other. The same applies to each of the P calculation data CAL 1 ~CAL P .

[0146] The determination unit 214 receives P calculation results CAL_RLS 1 ~CAL_RLS P (that is, 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 and detects the P calculation data CAL 1 ~CAL P included therein.

[0147] Then, the determination unit 214 detects C 1 ~CAL P pairs of two calculation data CAL P C 2 (i≠j) based on the P calculation data CAL i , CAL j (i≠j). Here,[[]] P C 2 is the number of combinations of two different calculation data CAL 1 ~CAL P from the P calculation data CAL i , CAL j (i≠j) when extracting two different calculation data CAL i , CAL j (i≠j).

[0148] After that, the determination unit 214 uses two pieces of arithmetic data CAL i , CAL j (i≠j) to calculate the difference for each class Cls between the multiple integral values included in the arithmetic data CAL i and the multiple integral values included in the arithmetic data CAL j , and calculates the standard deviation of the calculated differences. This is P C 2 for all pairs of two pieces of arithmetic data CAL i , CAL j (i≠j).

[0149] The determination unit 214 calculates the difference for each class Cls between the multiple integral values included in the arithmetic data CAL i and the multiple integral values included in the arithmetic data CAL j by the following method.

[0150] The determination unit 214, based on the multiple integral values ITG i ~ ITG 1_i included in the arithmetic data CAL n_i and the multiple integral values ITG j ~ ITG 1_j included in the arithmetic data CAL n_j , calculates, by the following formula, the difference DF k between the integral value ITG k_i and the integral value ITG k_j in one class Cls k .

[0151]

Equation

[0152] Note that the unit of the difference DF k calculated by Equation (1) is “%”.

[0153] Then, the determination unit 214 calculates the difference DF k of the integral values in one class Cls k by Equation (1) for n classes Cls 1 ~ Clsn Execute for all of them to calculate n differential DFs 1 ~DF n to calculate.

[0154] Then, the determination unit 214 calculates the standard deviation σ of the n differential DFs 1 ~DF n to calculate. DF to calculate.

[0155] And when the standard deviation σ of the difference is greater than the threshold value σ DF (=6%), it is determined that the two calculation data CAL th (i≠j) are different, and when the standard deviation σ of the difference is less than or equal to the threshold value σ i ,CAL j (i≠j) are not different. Note that the determination unit 214 holds the threshold value σ DF (=6%) in advance. th (=6%) or less, it is determined that the two calculation data CAL i ,CAL j (i≠j) are not different. Note that the determination unit 214 holds the threshold value σ th (=6%) in advance.

[0156] The determination unit 214 determines whether the two calculation data CAL i ,CAL j (i≠j) are different by the method described above P C 2 for all pairs of two calculation data CAL i ,CAL j (i≠j) and executes to P C 2 for all pairs of two calculation data CAL i ,CAL j (i≠j) and determines whether each pair of the two calculation data CAL i ,CAL j (i≠j) are different.

[0157] Note that determining that the two calculation data CAL i ,CAL j (i≠j) are not different means that the two calculation data CAL i ,CAL jDetermining that it is not possible to discriminate (i≠j) is equivalent to determining that two pieces of arithmetic data CAL i , CAL j are different is equivalent to determining that two pieces of arithmetic data CAL i , CAL j (i≠j) can be discriminated.

[0158] The determination unit 214 determines, by the method described above, P C 2 for each pair of two pieces of arithmetic data CAL i , CAL j (i≠j) whether the two pieces of arithmetic data CAL i , CAL j (i≠j) are different, and creates a determination result shown in Table 1. Then, the determination unit 214 outputs the determination result shown in Table 1 to the creation unit 215.

[0159]

Table 1

[0160] Determining that P pieces of arithmetic data CAL 1 ~CAL P are not mutually different is equivalent to determining that P pieces of arithmetic data CAL 1 ~CAL P cannot be mutually discriminated, and determining that P pieces of arithmetic data CAL 1 ~CAL P are mutually different is equivalent to determining that P pieces of arithmetic data CAL 1 ~CAL P can be mutually discriminated.

[0161] When the creation unit 215 receives one arithmetic result CAL_RLS uni and a signal S_u indicating that there is one piece of arithmetic data from the arithmetic unit 213, it creates a curve CUR uni based on the arithmetic data CAL uni by the method described later.

[0162] Further, the creation unit 215 receives P calculation results CAL_RLS 1 ~CAL_RLS P (i.e., a plurality of calculation results) from the calculation unit 213, and also receives a determination result JDGR (the determination result shown in Table 1) indicating whether P calculation data CAL 1 ~CAL P are different from each other from the determination unit 214. Then, according to the method described later, based on the P calculation data CAL 1 ~CAL P , P curves CUR 1 ~CUR P are created respectively.

[0163] When the creation unit 215 creates one curve CUR uni , it adds the curve CUR uni to the calculation result CAL_RLS uni to create an analysis result ALY_RLS uni = [ID uni / CAL uni / CUR uni , and outputs the created analysis result ALY_RLS uni = [ID uni / CAL uni / CUR uni to the control unit 212.

[0164] On the other hand, when the creation unit 215 creates P curves CUR 1 ~CUR P , it adds the P curves CUR 1 ~CAL_RLS P to the P calculation results CAL_RLS 1 ~CUR P respectively to create P analysis results ALY_RLS 1 = [ID 1 / CAL 1 / CUR 1 ~ALY_RLS P = [ID P / CAL P / CUR P , and outputs the P analysis results ALY_RLS 1 ~ALY_RLS PAnd the determination result JDGR (the determination result shown in Table 1) is output to the control unit 212.

[0165] The arithmetic unit 216 receives the index data IDX uni from the control unit 212. Then, the arithmetic unit 216 detects the arithmetic data CAL uni from the index data IDX uni , and based on the detected arithmetic data CAL uni , by the method described later, the Body index (+) uni , Body index (all) uni , Body index (-)_th uni , the total sum H(+)_sum of the integral values uni and the total sum H(-)_sum of the integral values uni are calculated.

[0166] Then, the arithmetic unit 216 creates 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 by associating the name ALY_Na of the analysis object uni , Body index (+) uni , Body index (all) uni , Body index (-)_th uni , the total sum H(+)_sum of the integral values uni and the total sum H(-)_sum of the integral values uni with each other, and outputs 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 to the taste diagnosis unit 217.

[0167] Also, the arithmetic unit 216 receives P pieces of index data IDX 1 ~IDX P from the control unit 212. Then, the arithmetic unit 216 detects P pieces of arithmetic data CAL 1 ~CAL P from the P pieces of index data IDX 1 ~IDX P respectively.

[0168] Then, based on the arithmetic data CAL p , the arithmetic unit 216 calculates the Body index (+) p , Body index (all) p , Body index (-)_th p , the total sum of integral values H(+)_sum p and the total sum of integral values H(-)_sum p (p = 1 to P) by the method described later.

[0169] And the arithmetic unit 216 creates 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 by associating the name of the object to be analyzed ALY_Na p , Body index (+) p , Body index (all) p , Body index (-)_th p , the total sum of integral values H(+)_sum p and the total sum of integral values H(-)_sum p with each other for all p = 1 to P, and creates P pieces of 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_DP =[ALY_Na P / Body index (+) P / Body index (all) P / Body index (-)_th P / H(+)_sum P / H(-)_sum P is created.

[0170] Then, the arithmetic unit 216 outputs P pieces of 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 the taste diagnosis unit 217.

[0171] The taste diagnosis unit 217 receives the 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 from the arithmetic unit 216.

[0172] And then, the taste diagnosis unit 217 extracts from the index data INDX_D uni the name of the object to be analyzed ALY_Na uni , Body index (+) uni , Body index (all) uni , Body index (-)_th uni , the total sum of integral values H(+)_sum uniand the sum H(-)_sum of the integrated values uni is detected.

[0173] Then, the taste diagnosis unit 217 diagnoses that the "astringency" of the analysis object having the name ALY_Na of the analysis object is the index value ASTG uni based on the Body index(+) uni by the method described later. uni is diagnosed.

[0174] Also, the taste diagnosis unit 217 diagnoses that the "aftertaste" of the analysis object having the name ALY_Na of the analysis object is the index value LNGS uni based on the Body index(all) uni by the method described later. uni is diagnosed.

[0175] Furthermore, the taste diagnosis unit 217 diagnoses that the "sweetness" of the analysis object having the name ALY_Na of the analysis object is the index value SWT uni based on the sum H(+)_sum of the integrated values uni by the method described later. uni is diagnosed.

[0176] Furthermore, the taste diagnosis unit 217 diagnoses that the "aroma" of the analysis object having the name ALY_Na of the analysis object is the index value SCT uni based on the sum H(-)_sum of the integrated values uni by the method described later. uni is diagnosed.

[0177] Furthermore, the taste diagnosis unit 217 diagnoses that the "bitterness" of the analysis object having the name ALY_Na of the analysis object is the index value BIT uni based on the index value ASTG of "astringency" uni and the index value SWT of "sweetness" uni by the method described later. uni is diagnosed.

[0178] And the taste diagnosis unit 217 determines the name ALY_Na of the analysis object uni and the index value ASTG of "astringency"uni and the index value LNGS of "aftertaste" uni and the index value SWT of "sweetness" uni and the index value SCT of "fragrance" uni and the index value BIT of "bitterness" uni and the taste diagnosis result JDR in which they are mutually associated uni = ALY_Na uni / ASTG uni / LNGS uni / SWT uni / SCT uni / BIT uni is created, and the created taste diagnosis result JDR uni = ALY_Na uni / ASTG uni / LNGS uni / SWT uni / SCT uni / BIT uni is output to the control unit 212 and the display unit 218.

[0179] Note that when the name ALY_Na of the analysis target is "grape", the Body index (-)_th uni is used to diagnose that the "astringency" of the analysis target having the name ALY_Na uni (= grape) of the analysis target is the index value ASTG uni by the method described later. th_uni is diagnosed.

[0180] Then, the taste diagnosis unit 217 creates a diagnosis result JDR uni in which the name ALY_Na of the analysis target (= grape) is associated with the index value ASTG of "astringency" th_uni and outputs the created diagnosis result JDR th_uni = ALY_Na uni (= grape) / ASTG th_uni to the control unit 212 and the display unit 218. th_uni = ALY_Na uni (= grape) / ASTG th_uni to the control unit 212 and the display unit 218.

[0181] On the one hand, the taste diagnosis unit 217 calculates 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, and then detects the name ALY_Na of the object to be analyzed p = [ALY_Na p / Body index (+) p / Body index (all) p / Body index (-)_th p / H(+)_sum p / H(-)_sum p ] from the index data INDX_D p , Body index (+) p , Body index (all) p , Body index (-)_th p , the total sum H(+)_sum of integral values p and the total sum H(-)_sum of integral values p .

[0182] Then, based on the Body index (+) p , the taste diagnosis unit 217 diagnoses that the "astringency" of the object to be analyzed having the name ALY_Na of the object to be analyzed p is the index value ASTG p .

[0183] Also, based on the Body index (all) p , the taste diagnosis unit 217, by the method described later, determines the name ALY_Na of the object to be analyzed pThe "aftertaste" of the object to be analyzed having [object name] is the index value LNGS p is diagnosed as such.

[0184] Furthermore, the taste diagnosis unit 217, based on the total sum H(+)_sum of the integrated values p by the method described later, diagnoses that the "sweetness" of the object to be analyzed having the name ALY_Na p is the index value SWT p is diagnosed as such.

[0185] Furthermore, the taste diagnosis unit 217, based on the total sum H(-)_sum of the integrated values p by the method described later, diagnoses that the "fragrance" of the object to be analyzed having the name ALY_Na p is the index value SCT p is diagnosed as such.

[0186] Furthermore, the taste diagnosis unit 217, based on the index value ASTR of "astringency" p and the index value SWT of "sweetness" p by the method described later, diagnoses that the "bitterness" of the object to be analyzed having the name ALY_Na p is the index value BIT p is diagnosed as such.

[0187] Then, the taste diagnosis unit 217 creates a taste diagnosis result JDR p corresponding to each other with the name ALY_Na of the object to be analyzed p and the index value LNGS of "aftertaste" p and the index value SWT of "sweetness" p and the index value SCT of "fragrance" p and the index value BIT of "bitterness" p as JDR p =[ALY_Na p / ASTG p / LNGS p / SWT p / SCT p / BIT p .

[0188] The taste diagnosis unit 217 executes the above-described operations for all p = 1 to P and obtains 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 outputs the created 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] Note that when the name ALY_Na of the analysis object p is "grape", based on the Body index (-)_th p the taste diagnosis unit 217 diagnoses that the "astringency" of the analysis object having the name ALY_Na p (= grape) is the index value ASTG th_p by the method described later.

[0190] Then, the taste diagnosis unit 217 associates the name ALY_Na p (= grape) of the analysis object with the index value ASTG th_p of "astringency" to obtain a diagnosis result JDR th_p =[ALY_Na p (= grape) / ASTG th_pExecute for all p = 1 to P to create 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 create 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 and output it to the control unit 212 and the display unit 218.

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

[0192] Also, when the display unit 218 receives the diagnosis result JDR th_uni =[ALY_Na uni (= grape) / ASTG th_uni from the taste diagnosis unit 217, it displays the received diagnosis result JDR th_uni =[ALY_Na uni (= grape) / ASTG th_uni .

[0193] Furthermore, the display unit 218 displays the 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 receives from the taste diagnosis unit 217 the P received 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 displays them.

[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 receives from the taste diagnosis unit 217 the P received taste diagnosis results JDR th_1 =[ALY_Na 1 (= grape) / ASTGt h_1 ~JDR th_P =[ALY_Na P (= grape) / ASTG th_P and displays them.

[0195] The reception unit 219 receives a request RQT from clerks in restaurants such as Japanese restaurants, Chinese restaurants, and Western restaurants, sake brewers, and clerks in liquor stores, etc uni or the request RQT q and receives the received request RQT uniOr request RQT q Output it to the control unit 212.

[0196] [Calculation of integral value] FIG. 9 and FIG. 10 are respectively a first and a second conceptual diagram for explaining a method of calculating an integral value.

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

[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, any 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, [0~100 mV], [101~200 mV], [201~300 mV], ···, [2301~2400 mV], [2401~2500 mV], [0~-100 mV], [-101~-200 mV], ···, [-2301~-2400 mV], [-2401~-2500 mV].[[]END]]

[0202] FIG. 10 shows a predetermined potential interval [V in the cyclic voltammogram CVG shown in FIG. 9 1 ~V 2Shows an enlarged view of the portion in

[0203] Referring to FIG. 10, the arithmetic unit 213, when calculating the integral value ITG of a predetermined potential range [V 1 ~V 2 , detects the current value Iox_1 of the oxidation wave and the current value Ird_1 of the reduction wave at the potential V 1 and calculates the difference (Iox_1 - Ird_1) between the current value Iox_1 and the current value Ird_1, thereby calculating the intensity (Iox_1 - Ird_1) of the cyclic voltammogram CVG at the potential V 1 .

[0204] Then, the arithmetic unit 213 detects the current value Iox_2 of the oxidation wave and the current value Ird_2 of the reduction wave at the potential V 1 plus the unit potential (= 1 mV), i.e., at the potential V 1 + 1, and calculates the difference (= Iox_2 - Ird_2) between the current value Iox_2 and the current value Ird_2, thereby calculating the intensity (Iox_2 - Ird_2) of the cyclic voltammogram CVG at the potential V 1 + 1.

[0205] Furthermore, the arithmetic unit 213 detects the current value Iox_3 of the oxidation wave and the current value Ird_3 of the reduction wave at the potential V 1 plus the unit potential (= 1 mV) added to V 1 + 1, i.e., at the potential V 1 + 2, and calculates the difference (Iox_3 - Ird_3) between the current value Iox_3 and the current value Ird_3, thereby calculating the intensity (Iox_3 - Ird_3) of the cyclic voltammogram CVG at the potential V

[0206] Hereinafter, in the same manner, the arithmetic unit 213 detects the current value Iox_N of the oxidation wave and the current value Ird_N of the reduction wave at the potential V 2 minus the unit potential (= 1 mV), i.e., at the potential V 2 , and calculates the difference (Iox_N - Ird_N) between the current value Iox_N and the current value Ird_N, thereby calculating the potential V 2Calculate the intensity (Iox_N - Ird_N) of the cyclic voltammogram CVG in

[0207] Then, the arithmetic unit 213 calculates the integral value ITG in a predetermined potential range [V 1 ~V 2 according to the following formula.

[0208]

Equation

[0209] That is, the arithmetic unit 213 calculates a plurality of intensities ((Iox_1 - Ird_1), (Iox_2 - Ird_2), ···, (Iox_N - Ird_N)) of the cyclic voltammogram CVG for each unit potential (= 1 mV) in the predetermined potential range [V 1 ~V 2 , and adds the calculated plurality of intensities ((Iox_1 - Ird_1), (Iox_2 - Ird_2), ···, (Iox_N - Ird_N)) to calculate the integral value ITG of the cyclic voltammogram CVG in the predetermined potential range [V 1 ~V 2 .

[0210] Here, in the predetermined potential range [V 1 ~V 2 , calculating the difference (Iox_1 - Ird_1) at the potential V 1 , calculating the difference (Iox_2 - Ird_2) at the potential V 1 + 1, calculating the difference (Iox_3 - Ird_3) at the potential V 1 + 2, ···, calculating the difference (Iox_N - Ird_N) at the potential V 2 corresponds to performing a subtraction process of 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 range to calculate the intensity of the cyclic voltammogram at one unit potential, and performing this for all unit potentials in one predetermined potential range to calculate a plurality of intensities in one predetermined potential range.

[0211] Then, according to Equation (2), calculating the integral value ITG in a predetermined potential range [V 1 ~V 2 is equivalent to calculating the sum of the calculated multiple intensities as the area of the cyclic voltammogram in one predetermined potential range.

[0212] Also, calculating the difference (Iox_1 - Ird_1) at the potential V 1 , calculating the difference (Iox_2 - Ird_2) at the potential V 1 +1, calculating the difference (Iox_3 - Ird_3) at the potential V 1 +2, ···, calculating the difference (Iox_N - Ird_N) at the potential V 2 each corresponds to calculating the intensity of the cyclic voltammogram at one unit potential by 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 range.

[0213] Note that the arithmetic unit 213 calculates the sum SUM_Iox of the current values Iox_1 to Iox_N of N oxidation waves and the sum SUM_Ird of the current values Ird_1 to Ird_N of N reduction waves in a predetermined potential range [V 1 ~V 2 , subtracts the sum SUM_Ird from the sum SUM_Iox, and may calculate the integral value ITG of the cyclic voltammogram CVG in the predetermined potential range [V 1 ~V 2 .

[0214] The arithmetic unit 213 is the above-mentioned predetermined potential range [V 1 ~V 2A method for calculating the integrated value ITG of the cyclic voltammogram CVG in [ ] calculates a plurality of integrated values ITG 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 , ITG 2 , ITG 3 , ···, ITG 24 , ITG 25 , ITG 26 , ITG 27 , ···, ITG 49 , ITG 50 are calculated.

[0215] By calculating a plurality of integrated values ITG by the method shown in FIGS. 9 and 10 described above 1 ~ ITG 50 , it is possible to correct the characteristic variations between sensors used for measuring the cyclic voltammogram CVG and clearly show the signal intensity difference between the solutions of the analysis object.

[0216] Each of (Iox_1 - Ird_1), (Iox_2 - Ird_2), ···, (Iox_N - Ird_N) in the above-described formula (2) represents the current values in the oxidation reaction and the reduction reaction between the electrode (working electrode 112) and the analysis object at each unit potential.

[0217] Also, the sum (= integrated value) of the subtraction results (Iox - Ird) in one predetermined potential interval represents the total number of electrons in the oxidation reaction and the reduction reaction between the electrode (working electrode) and the analysis object in one predetermined potential interval.

[0218] Furthermore, a plurality of integrated 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 represents the dependency of the total number of electrons (= integrated value) in the oxidation reaction and the reduction reaction between the electrode (working electrode) and the analyte on a predetermined potential range.

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

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

[0221] Referring to FIG. 11(a), when calculating the integrated value of a predetermined potential range in the region REG, the current values I ox_1_REG , I ox_2_REG , ···, I ox_N’_REG of the oxidation wave are each smaller than the current values I rd_1_REG , I rd_2_REG , ···, I rd_N’_REG of the reduction wave. Therefore, the integrated value ITG of the predetermined potential range in the region REG becomes a negative value.

[0222] Referring to FIG. 11(b), when calculating the integrated value of a predetermined potential range in the region REG, each of the current values I ox_1_REG , I ox_2_REG , ···, I ox_N’_REG of the oxidation wave is a negative current value, and the current values I rd_1_REG , I rd_2_REG , ···, I rd_N’_REGSince each of them is a positive current value, the integrated value ITG of the predetermined potential range in the region REG becomes a negative value.

[0223] Referring to Fig. 11(c), when calculating the integrated value of the predetermined potential range in the region REG, the current value I of the oxidation wave ox_1_REG , I ox_2_REG , ···, I ox_N’_REG each of which is a negative current value, and the current value I of the reduction wave rd_1_REG , I rd_2_REG , ···, I rd_N’_R each of which is a negative current value, and the absolute value |I ox_1_REG , I ox_2_REG , ···, I ox_N’_REG | of the oxidation wave current value I ox_1_REG , |I ox_2_REG |, ···, |I ox_N’_REG | is respectively larger than the absolute value |I rd_1_REG , I rd_2_REG , ···, I rd_N’_R | of the reduction wave current value I rd_1_REG , |I rd_2_REG , ···, |I rd_N’_R |, so the integrated value ITG of the predetermined potential range in the region REG becomes a negative value.

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

[0225] Note that the arithmetic unit 213 may calculate a plurality of integrated values in a plurality of predetermined potential ranges by a method different from the method described above.

[0226] For example, the arithmetic unit 213 uses the potential V as the explanatory variable and the current I as the objective variable to calculate the regression curve RC1 (the curve of the solid line part) showing the oxidation wave of the cyclic voltammogram CVG and the regression curve RC2 (the curve of the broken line part) showing the reduction wave in Fig. 9, and the predetermined potential range [V 1 -V 2Calculate the integrated value ITG_RC1 of the regression curve RC1 and the integrated value ITG_RC2 of the regression curve RC2 in 1 -V 2 , and calculate the integrated value of the cyclic voltammogram CVG shown in FIG. 9 in the potential range [V

[0227] This may be executed for all predetermined potential ranges to calculate a plurality of integrated values in a plurality of predetermined potential ranges. The arithmetic unit 213 may calculate the plurality of integrated values in the plurality of predetermined potential ranges by any method as long as it can calculate the plurality of integrated values in the plurality of predetermined potential ranges.

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

[0229] (a) of FIG. 12 shows one piece of arithmetic data CAL uni , and (b) of FIG. 12 shows a curve CUR uni representing the relationship between the integrated value and the class.

[0230] Referring to (a) of FIG. 12, the arithmetic data CAL uni includes the name ALY-Na of the analysis object uni , the type ALY-Kd of the analysis object uni , the class Cls, the integrated values ITG _Low , ITG _Middle , ITG _High , and the total integrated value ITG ST .

[0231] The integrated value ITG _Low is the integrated value calculated by the method described above based on the cyclic voltammogram CVG measured by changing the potential V at a potential scanning speed V r_Low (for example, 0.3 V / sec), and the integrated value ITG _Middle is the potential scanning speed V r_MiddleThe integral value ITG calculated by the above-described method based on the cyclic voltammogram CVG measured by changing the potential V at, for example, 0.5 V / sec _High is the scanning speed V of the potential r_High The integral value is calculated by the above-described method based on the cyclic voltammogram CVG measured by changing the potential V at, for example, 0.6 V / sec

[0232] The class Cls consists of n classes Cls 1 ~Cls n and the integral value ITG _Low consists of n integral values ITG 1_Low ~ITG n_Low The integral value ITG _Middle consists of n integral values ITG 1_Middle ~ITG n_Middle The integral value ITG _High consists of n integral values ITG 1_High ~ITG n_High Here, n represents the total number of predetermined potential intervals. When the potential scan range is [-V S1 ~+V S2 and one predetermined potential interval is V PTS n = (|-V S1 |+|+V S2 |) / V PTS

[0233] The n integral values ITG 1_Low ~ITG n_Low are integral values calculated by the above-described method based on the potential V 1_Low ~V d_Low and the current value I 1_Low ~I d_Low shown in FIG. 6. The n integral values ITG 1_Middle ~ITG n_Middle are integral values calculated by the above-described method based on the potential V 1_Middle ~V d_Middle and the current value I 1_Middle ~I d_Middle shown in FIG. 6. The n integral values ITG 1_High ~ITG n_High are the potential V shown in FIG. 6​1_High ~V d_High and current value I 1_High ~I d_High is the integral value calculated by the above-described method based on ~V and ~I.

[0234] Total integral value 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 .

[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 denoted as "sum of n integral values ITG_ SMT1 ~ITG_ SMTn ".

[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 respectively associated with n classes Cls 1 ~Cls n .

[0237] When the creation unit 215 receives one operation data CAL uni and a signal S_u indicating that there is one operation data from the operation unit 213, it determines that there is one operation data CAL calculated by the operation unit 213 based on the signal S_u.

[0238] Then, the creation unit 215 generates a set of (classes Cls 1, the total integral value ITG_ SMT1 ) is detected from the calculation data CAL uni , and then, a set of (class Cls 2 , the total integral value ITG_ SMT2 ) is detected from the calculation data CAL uni , and hereinafter, in the same manner, a set of (class Cls n-1 , the total integral value ITG_ SMTn―1 ) is detected from the calculation data CAL uni , and a set of (class Cls n , the total integral value ITG_ SMTn ) is detected from the calculation data CAL uni .

[0239] Then, the creation unit 215 plots a set of (class Cls 1 , the total integral value ITG_ SMT1 ), a set of (class Cls 2 , the total integral value ITG_ SMT2 ), a set of (class Cls 3 , the total integral value ITG_ SMT3 ), ···, a set of (Cls n-2 , the total integral value ITG_ SMTn―2 ), a set of (class Cls n-1 , the total integral value ITG_ SMTn―1 ) and a set of (class Cls n , the total integral value ITG _SMTn ) on a graph with the class on the horizontal axis and the integral value on the vertical axis.

[0240] Thereafter, the creation unit 215 creates a curve CURuni by connecting the plotted n points. In this case, since the plotted n points are plotted for each class Cls, the creation unit 215 can create a curve CUR uni consisting of a smooth curve by connecting the plotted n points.

[0241] Note that after plotting the n points, the creation unit 215 obtains a regression curve with the class Cls as the explanatory variable and the total integral value ITG_ SMT as the objective variable to obtain the curve CUR unimay be created.

[0242] And the creation unit 215 sets the curve CUR uni as an index curve that is an index for identifying the object to be analyzed.

[0243] Also, the creation unit 215 receives P calculation results CAL_RLS 1 ~CAL_RLS P (that is, a plurality of calculation results) from the calculation unit 213, and also receives a determination result JDGR (the determination result shown in Table 1) indicating whether P calculation data CAL 1 ~CAL P are different from each other from the determination unit 214.

[0244] Then, the creation unit 215 creates, by the method described with reference to FIG. 12, a curve CUR p (p is any one of 1 to P) indicating the class dependence of the total integral value ITG_ SMT for all of the P calculation data CAL p ~CAL 1 ~CAL P and creates P curves CUR 1 ~CUR P (that is, a plurality of curves CUR).

[0245] In this case, each of the P calculation data CAL 1 ~CAL P (that is, a plurality of calculation data) has the same configuration as the calculation data CAL uni shown in FIG. 12(a).

[0246] Note that the curve CUR uni represents the dependence on [n total integral values ITG_ SMT1 ~ITG_ SMTn with respect to [n classes Cls 1 ~Cls n . And since each of the n classes Cls 1 ~Cls n consists of a predetermined potential interval, the curve CUR uniis a curve representing the dependence of the integrated value on a predetermined potential range. Similarly, each of the P curves CUR 1 ~CUR P is also a curve representing the dependence of the integrated value on a predetermined potential range.

[0247] When the creation unit 215 creates one curve CUR uni it adds the curve CUR uni to the calculation result CAL_RLS uni to create the analysis result ALY_RLS uni =[ID uni / CAL uni / CUR uni and outputs the created analysis result ALY_RLS uni =[ID uni / CAL uni / CUR uni to the control unit 212.

[0248] On the other hand, when the creation unit 215 creates P curves CUR 1 ~CUR P it adds the P curves CUR 1 ~CAL_RLS P respectively to the P calculation results CAL_RLS 1 ~CUR P to create P analysis results ALY_RLS 1 =[ID 1 / CAL 1 / CUR 1 ~ALY_RLS P =[ID P / CAL P / CUR P and outputs the P analysis results ALY_RLS 1 ~ALY_RLS P and the determination result (the determination result shown in Table 1) to the control unit 212.

[0249] [Taste Diagnosis] (I) Taste Diagnosis of Shochu When the arithmetic unit 216 receives the index data IDX uni from the control unit 212, it receives the arithmetic data CAL uni (see Fig. 12) as the index data IDXuni Detect from. Then, the arithmetic unit 216 uses the arithmetic data CAL uni (see FIG. 12) to obtain the integrated value ITG _Low (ITG 1_Low ~ITG n_Low ), the integrated value ITG _Middle (ITG 1_Middle ~ITG n_Middle ) and the integrated value ITG _High (ITG 1_High ~ITG n_High ).

[0250] FIG. 13 is a conceptual diagram showing the class dependence of the integrated value created based on the cyclic voltammogram measured by changing the scanning speed of the potential to the scanning speed of the potential V r_Low , V r_Middle , V r_High .

[0251] (a) of FIG. 13 is a conceptual diagram showing the class dependence of the integrated value created based on the cyclic voltammogram measured by setting the scanning speed of the potential to "V r_Low ", (b) of FIG. 13 is a conceptual diagram showing the class dependence of the integrated value created based on the cyclic voltammogram measured by setting the scanning speed of the potential to "V r_Middle ", and (c) of FIG. 13 is a conceptual diagram showing the class dependence of the integrated value created based on the cyclic voltammogram measured by setting the scanning speed of the potential to "V r_High ".

[0252] The arithmetic unit 216, based on the integrated value ITG _Low (ITG 1_Low ~ITG n_Low ), detects x (x is an integer satisfying x = INT(n / 2)) integrated values ITG 1_Low (+)~ITG x_Low(+) in x classes corresponding to the positive predetermined potential interval (see (a) of FIG. 13), and the sum SMT 1_Low(+) ~ITG x_Low(+) of the detected x integrated values ITG _Low(+)Calculate it. Here, "INT(n / 2)" is the result of dividing n by 2 and then rounding down to the nearest integer.

[0253] Also, the arithmetic unit 216 detects x integral values ITG _Middle (ITG 1_Middle ~ITG n_Middle ) corresponding to x levels in the positive predetermined potential range (see (b) of FIG. 13), and calculates the sum SMT 1_Middle(+) ~ITG x_Middle(+) of the detected x integral values ITG 1_Middle(+) ~ITG x_Middle(+) . _Middle(+)

[0254] Furthermore, the arithmetic unit 216 detects x integral values ITG _High (ITG 1_High ~ITG n_High ) corresponding to x levels in the positive predetermined potential range (see (c) of FIG. 13), and calculates the sum SMT 1_High(+) ~ITG x_High(+) of the detected x integral values ITG 1_High(+) ~ITG x_High(+) . _High(+)

[0255] Then, the arithmetic unit 216 calculates the Body index (+) by the following formula based on the sum SMT _Low(+) , the sum SMT _Middle(+) and the sum SMT _High(+) .

[0256]

Equation

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

[0258] Also, the arithmetic unit 216 detects x integral values ITG _Low (ITG 1_Low ~ITGn_Low Based on [1], n integrated values ITG in n class intervals corresponding to n predetermined potential intervals (see (a) of FIG. 13) 1_Low(all) ~ITG n_Low(all) are detected, and the sum SMT of the n detected integrated values ITG 1_Low(all) ~ITG n_Low(all) is calculated. _Low(all)

[0259] Next, the arithmetic unit 216 detects n integrated values ITG in n class intervals corresponding to n predetermined potential intervals (see (b) of FIG. 13) based on the integrated value ITG _Middle (ITG 1_Middle ~ITG n_Middle ), and the sum SMT of the n detected integrated values ITG 1_Middle(all) ~ITG n_Middle(all) is calculated. 1_Middle(all) ~ITG n_Middle(all) _Middle(all)

[0260] Furthermore, the arithmetic unit 216 detects n integrated values ITG in n class intervals corresponding to n predetermined potential intervals (see (c) of FIG. 13) based on the integrated value ITG _High (ITG 1_High ~ITG n_High ), and the sum SMT of the n detected integrated values ITG 1_High(all) ~ITG n_High(all) is calculated. 1_High(all) ~ITG n_High(all) _High(all)

[0261] Then, the arithmetic unit 216 calculates the Body index (all) by the following formula based on the sum SMT _Low(all) , the sum SMT _Middle(all) and the sum SMT _High(all) .

[0262]

Equation

[0263] The Body index (all) is a factor based on the diffusion coefficient of the components of the object to be analyzed when a positive potential and a negative potential are applied to the object to be analyzed.

[0264] Furthermore, the arithmetic unit 216 uses the total sum SMT used for calculating the Body index (+) _High(+) as H(+)_sum (= SMT _High(+) ).

[0265] Furthermore, the arithmetic unit 216 is based on the integral value ITG _High (ITG 1_High ~ITG n_High ) and detects x integral values ITG in x classes corresponding to a negative predetermined potential range (see (c) of FIG. 13) 1_High(-) ~ITG x_High(-) , and calculates the total sum SMT 1_High(-) ~ITG x_High(-) of the detected x integral values ITG as H(-)_sum. _High(-)

[0266] Then, the arithmetic unit 216 outputs the Body index (+), Body index (all), H(+)_sum (= SMT _High(+) ) and H(-)_sum (= SMT _High(-) ) to the taste diagnosis unit 217.

[0267] The taste diagnosis unit 217 receives the 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 arithmetic unit 216, it substitutes the Body index (+) into the following formula and diagnoses the calculation result (= ASTG) as the astringency ASTG of the shochu, which is the object to be analyzed.

[0269] [Equation]

[0270] In formula (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 arithmetic unit 216, it substitutes the Body index (all) into the following formula and diagnoses the calculated result (=LNGS) as the aftertaste LNGS of the shochu, which is the object to be analyzed.

[0272]

Number

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

[0274] (3) Diagnosis of "sweetness" When the taste diagnosis unit 217 receives H(+)_sum (=SMT _High(+) ) from the arithmetic unit 216, it substitutes H(+)_sum (=SMT _High(+) ) into the following formula and diagnoses the calculated result (=SWT) as the "sweetness" of the shochu, which is the object to be analyzed.

[0275]

Number

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

[0277] (4) Diagnosis of "aroma" When the taste diagnosis unit 217 receives H(-)_sum (=SMT _High(-) ) from the arithmetic unit 216, it substitutes H(-)_sum (=SMT _High(-) ) into the following formula and diagnoses the calculated result (=SCT) as the "aroma" of the shochu, which is the object to be analyzed.

[0278] [Number]

[0279] In formula (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 diagnoses the "bitterness" of the shochu, which is the analysis object, by substituting the calculated result (= BIT) calculated by substituting the "astringency" (= ASTG in formula (5)) and the "sweetness" (= SWT in formula (7)) diagnosed by the above-described method into the following formula.

[0281] [Number]

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

[0283] The coefficient k of formula (5) 1 , the coefficient k of formula (6) 2 , the coefficient k of formula (7) 3 , k 4 , the coefficient k of formula (8) 5 , k 6 , and the coefficient k of formula (9) 7 , k 8 are determined using a plurality of shochus for which "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" are known as teacher data.

[0284] Figure 14 is a conceptual diagram of the teacher data. Referring to Figure 14, the teacher data consists of teacher data TH1 to TH5. The teacher data TH1 to TH5 are stored in the database 22 in advance.

[0285] The teacher data TH1 is the coefficient k of formula (5)1 is the training data for determining the value of, and the training data TH2 is the coefficient k in Equation (6) 2 is the training data for determining the value of, and the training data TH3 is the coefficient k in Equation (7) 3 , k 4 is the training data for determining the value of, and the training data TH4 is the coefficient k in Equation (8) 5 , k 6 is the training data for determining the value of, and the training data TH5 is the coefficient k in Equation (9) 7 , k 8 is the training data for determining the value of.

[0286] The taste diagnosis unit 217 reads the training data TH1 to TH5 from the database 22 when determining the values of the coefficients k 1 ~ k 8 .

[0287] (A) Determination of the coefficient k 1 The training data TH1 includes astringency and Body index (+). The astringency consists of, for example, 10 a 1 ~ a 10 and the Body index (+) consists of, for example, 10 b(+) 1 ~ b(+) 10 . The 10 a 1 ~ a 10 are known.

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

[0289] (i) For one shochu y, the sensor device 1 changes the potential scanning speed to V r_Low , V r_Middle , V r_High and measures the cyclic voltammograms CVG _Low_y , CVG _Middle_y , CVG _High_y respectively, and the measured cyclic voltammograms CVG _Low_y , CVG _Middle_y , CVG _High_y ​It is transmitted to a personal computer PC by wireless communication or wired communication.

[0290] (ii) Based on the cyclic voltammogram CVG _Low_y by the method described above, the correspondence relationship between the class Cls and the integrated value ITG [(Cls 1 -ITG 1 )~(Cls n -ITG n )] _Low_y is obtained.

[0291] (iii) Based on the cyclic voltammogram CVG _Middle_y by the method described above, the correspondence relationship between the class Cls and the integrated value ITG [(Cls 1 -ITG 1 )~(Cls n -ITG n )] _Middle_y is obtained .

[0292] (iv) Based on the cyclic voltammogram CVG _High_y by the method described above, the correspondence relationship between the class Cls and the integrated value ITG [(Cls 1 -ITG 1 )~(Cls n -ITG n )] _High_y is obtained.

[0293] (v) Based on the correspondence relationship [(Cls 1 -ITG 1 )~(Cls n -ITG n )] _Low_y the (n / 2) integrated values associated with the class corresponding to the positive predetermined potential range are detected, and the sum of the detected (n / 2) integrated values is calculated to obtain [the sum of Low(+)] _y .

[0294] (vi) The personal computer PC detects (n / 2) integral values associated with the class corresponding to the positive predetermined potential range based on the correspondence relationship [(Cls 1 -ITG 1 )~(Cls n -ITG n )] _Middle_y and calculates the sum of the detected (n / 2) integral values to obtain [Sum of Middle(+)]. _y

[0295] (vii) The personal computer PC detects (n / 2) integral values associated with the class corresponding to the positive predetermined potential range based on the correspondence relationship [(Cls 1 -ITG 1 )~(Cls n -ITG n )] _High_y and calculates the sum of the detected (n / 2) integral values to obtain [Sum of High(+)]. _y

[0296] (viii) The personal computer PC substitutes [Sum of Low(+)], [Sum of Middle(+)], and [Sum of High(+)] into SMT _y , SMT _y , and SMT _y of Equation (3) respectively to calculate b(+) _Low(+) , SMT _Middle(+) , and SMT _High(+) to calculate b(+) y .

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

[0298] (x) When the personal computer PC obtains b(+) 1 to b(+) 10 , it obtains the obtained b(+) 1 to b(+) 10 and assigns them to a 1 to a 10 ​​Store it in the "Body Index (+)" column of the teacher data TH1 in association with it.

[0299] And the personal computer PC performs a regression analysis with "Body Index (+)" as the explanatory variable and "astringency" as the objective variable based on (a 1 , b(+) 1 ), (a 2 , b(+) 2 ), ···, (a 9 , b(+) 9 ), (a 10 , b(+) 10 ) of the teacher data TH1, obtains a regression equation, and determines the value multiplied by the explanatory variable "Body Index (+)" in the obtained regression equation as the value of the coefficient k 1 in Equation (5).

[0300] (B) Determination of the coefficient k 2 The teacher data TH2 includes aftertaste and Body Index (all). The aftertaste consists of, for example, 10 c 1 ~ c 10 , and the Body Index (all) consists of, for example, 10 b(all) 1 ~ b(all) 10 . The 10 c 1 ~ c 10 are known.

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

[0302] (xi) The personal computer PC calculates the sum SUM 1 - ITG 1 ) ~ (Cls n - ITG n ) ]_Low_y of all the integral values ITG 1 ~ ITG n based on the correspondence relationship [(Cls all_Low_y ) obtained in (ii) above.

[0303] (xii) The personal computer PC calculates the sum SUM all_Middle_y of all integral values ITG 1 to ITG n based on the correspondence relationship [(Cls 1 - ITG 1 ) to (Cls n - ITG n ) ] obtained in (iii) above. 1 -ITG 1 )~(Cls n -ITG n )] _Middle_y Based on the above, the personal computer PC calculates the sum SUM all_Middle_y of all integral values ITG 1 to ITG n . 1 ~ITG n The sum SUM all_Middle_y is calculated.

[0304] (xiii) The personal computer PC calculates the sum SUM all_High_y of all integral values ITG 1 to ITG n based on the correspondence relationship [(Cls 1 - ITG 1 ) to (Cls n - ITG n ) ] obtained in (iv) above. 1 -ITG 1 )~(Cls n -ITG n )] _High_y Based on the above, the personal computer PC calculates the sum SUM all_High_y of all integral values ITG 1 to ITG n . 1 ~ITG n The sum SUM all_High_y is calculated.

[0305] (xiv) The personal computer PC substitutes the sums SUM all_Low_y , SUM all_Middle_y , and SUM all_High_y into SMT _Low(all) , SMT _Middle(all) , and SMT _High(all) of Equation (4) respectively to calculate b(all) y . all_Low_y The sum SUM all_Middle_y and the sum SUM all_High_y are each substituted into SMT _Low(all) , SMT _Middle(all) , and SMT _High(all) of Equation (4) to calculate b(all) y . _Low(all) ,SMT _Middle(all) ,SMT _High(all) Substitute into the formula to calculate b(all) y is calculated.

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

[0307] (xvi) When the personal computer PC obtains b(all) 1 to b(all) 10 , it associates the obtained b(all) 1 to b(all) 10 with c 1 to c 10 of the teacher data TH2 respectively and stores them in the column of "Body index (all)" of the teacher data TH2. 1 ~b(all) 10 When the personal computer PC obtains b(all), it 1 ~b(all) 10 associates the obtained b(all) with c 1 ~c 10 of the teacher data TH2 respectively and stores them in the column of "Body index (all)" of the teacher data TH2.

[0308] Then, based on (c 1 ,b(all) 1 ),(c 2 ,b(all) 2 ),···,(c 9 ,b(all) 9 ),(c 10 ,b(all) 10 ) of the teacher data TH2, the personal computer PC performs a regression analysis with "Body index (all)" as the explanatory variable and "Aftertaste" as the objective variable to obtain a regression equation, and determines the value multiplied by the explanatory variable "Body index (all)" in the obtained regression equation as the value of the coefficient k 2 in Equation (6).

[0309] (C) Determination of the coefficient k 3 ,k 4 The teacher data TH3 includes sweetness and H(+)_sum. The sweetness consists of, for example, 10 d 1 ~d 10 , and H(+)_sum consists of, for example, 10 h(+) 1_sum ~h(+) 10_sum . The 10 d 1 ~d 10 are 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 y = 1 to 10 to obtain h(+) 1_sum ~h(+) 10_sum .

[0312] (xviii) When the personal computer PC obtains h(+) 1_sum ~h(+) 10_sum , it uses the obtained h(+) 1_sum ~h(+) 10_sum as d of the teacher data TH3 respectively 1 ~d 10 ​Store it in the column of "H(+)_sum" of the teacher data TH3 in association with it.

[0313] Then, the personal computer PC performs a regression analysis with "H(+)_sum" as the explanatory variable and "sweetness" as the objective variable based on (d 1 , h(+) 1_sum ), (d 2 , h(+) 2_sum ), ···, (d 9 , h(+) 9_sum ), (d 10 , h(+) 10_sum ) of the teacher data TH3, obtains a regression equation, and determines the value obtained by dividing by the explanatory variable "H(+)_sum" in the obtained regression equation as the coefficient k of Equation (7) 3 's value, and determines the value multiplied by the explanatory variable "H(+)_sum" as the coefficient k of Equation (7) 4 's value.

[0314] (D) Determination of coefficients k 5 , k 6 The teacher data TH4 includes aroma and H(-)_sum. The aroma consists of, for example, 10 e 1 ~e 10 , and H(-)_sum consists of, for example, 10 h(-) 1_sum ~h(-) 10_sum . The 10 e 1 ~e 10 are known.

[0315] h(-) y_sum (y = 1~10) is obtained as follows.

[0316] (xix) The personal computer PC detects (n / 2) integral values associated with the class corresponding to the negative predetermined potential range based on the correspondence relationship [(Cls 1 -ITG 1 )~(Cls n -ITG n )] obtained in (iv) above, calculates the sum of the detected (n / 2) integral values, and calculates h(-) _High_y y_sum ​To obtain.

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

[0318] (xxi) When the personal computer PC obtains h(-) 1_sum ~h(-) 10_sum and stores the obtained h(-) 1_sum ~h(-) 10_sum into e of the teacher data TH4 respectively 1 ~e 10 and associates it with "H(-)_sum" of the teacher data TH4.

[0319] Then, based on (e 1 , h(-) 1_sum ), (e 2 , h(-) 2_sum ), ···, (e 9 , h(-) 9_sum ), (e 10 , h(-) 10_sum ) of the teacher data TH4, the personal computer PC performs a regression analysis with "H(-)_sum" as the explanatory variable and "scent" as the objective variable to obtain a regression equation, and determines the value obtained by dividing the explanatory variable "H(-)_sum" in the obtained regression equation as the coefficient k of Equation (8) 5 and determines the value obtained by multiplying the explanatory variable "H(-)_sum" as the coefficient k of Equation (8) 6 .

[0320] (E) Determination of the coefficients k 7 , k 8 The teacher data TH5 includes "bitterness", "astringency", and "sweetness". "Bitterness" consists of, for example, 10 f ~f 1 ~f 10 . "Astringency" consists of a 1 ~a 10 included in the teacher data TH1. "Sweetness" consists of d 1 ~d 10 included in the teacher data TH3.

[0321] (xxii) The personal computer PC performs a regression analysis with "astringency" and "sweetness" as explanatory variables and "bitterness" as the objective variable to obtain a regression equation, and determines the value multiplied by "astringency" (=ASTG) in the obtained regression equation as the coefficient k 7 in the value of Equation (9), and determines the value multiplied by "sweetness" (=SWT) as the coefficient k 8 in the value of Equation (9).

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

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

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

[0325] The personal computer PC determines the values of coefficients k 1 1 ~k 8 8 by the method described above, creates the correspondence table TBL1, and transmits the created correspondence table TBL1 to the diagnostic device 2.

[0326] The receiving unit 211 of the diagnostic device 2 receives the correspondence table TBL1 from the personal computer PC, and outputs the received correspondence table TBL1 to the control unit 212.

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

[0328] When the taste diagnostic unit 217 diagnoses "astringency", "aftertaste", "sweetness", "aroma", and "bitterness", it reads out the coefficients k 1 1 ~k 8 8 of the correspondence table TBL1 stored in the database 22, and diagnoses "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" by the method described above using the read coefficients k 1 1 ~k 8 8 .

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

[0330] In the teacher data TH1_up1 to TH5_up1, "1" in "up1" represents the update count of the teacher data TH1 to TH5.

[0331] Referring to Fig. 16(a), the teacher data TH1_up1 is obtained by adding "astringency a 1_add ~a v_add " and "Body index (+): b(+) 1_add ~b(+) v_add " to the teacher data TH1 shown in Fig. 14(a). Here, v is an integer greater than or equal to 1.

[0332] Referring to Fig. 16(b), the teacher data TH2_up1 is obtained by adding "aftertaste c 1_add ~c v_add " and "Body index (all): b(all) 1_add ~b(all) v_add " to the teacher data TH2 shown in Fig. 14(b).

[0333] Referring to Fig. 16(c), the teacher data TH3_up1 is obtained by adding "sweetness d 1_add ~d v_add " and "H(+)_sum: h(+) 1_sum_add ~h(+) v_sum_add " to the teacher data TH3 shown in Fig. 14(c).

[0334] Referring to Fig. 16(d), the teacher data TH4_up1 is obtained by adding "fragrance e 1_add ~e v_add " and "H(-)_sum: h(-) 1_sum_add ~h(-) v_sum_add " to the teacher data TH4 shown in Fig. 14(d).

[0335] Referring to Fig. 16(e), the teacher data TH5_up1 is obtained by adding "bitterness f 1_add ~f v_add ", "astringency: a 1_add ~a v_add " and "sweetness: d 1_add ~d v_add " to the teacher data TH5 shown in Fig. 14(e).

[0336] The taste diagnosis unit 217 calculates the coefficient k 1 ~k 8Using the above-described method to diagnose "astringency", "aftertaste", "sweetness", "aroma", and "bitterness", teacher data TH1 to TH5 are read from the database 22.

[0337] Then, the taste diagnosis unit 217 adds "astringency a 1_add ~a v_add " and "Body index (+): b(+) 1_add ~b(+) v_add " to the teacher data TH1 and updates the teacher data TH1 to teacher data TH1_up1.

[0338] Also, the taste diagnosis unit 217 adds "aftertaste c 1_add ~c v_add " and "Body index (all): b(all) 1_add ~b(all) v_add " to the teacher data TH2 and updates the teacher data TH2 to teacher data TH2_up1.

[0339] Furthermore, the taste diagnosis unit 217 adds "sweetness d 1_add ~d v_add " and "H(+)_sum: h(+) 1_sum_add ~h(+) v_sum_add " to the teacher data TH3 and updates the teacher data TH3 to teacher data TH3_up1.

[0340] Furthermore, the taste diagnosis unit 217 adds "aroma e 1_add ~e v_add " and "H(-)_sum: h(-) 1_sum_add ~h(-) v_sum_add " to the teacher data TH4 and updates the teacher data TH4 to teacher data TH4_up1.

[0341] Furthermore, the taste diagnosis unit 217 adds "bitterness f 1_add ~f v_add ", "astringency: a 1_add ~a v_add " and "sweetness: d 1_add ~d v_add " to the teacher data TH5 and updates the teacher data TH5 to teacher data TH5_up1.

[0342] Then, based on the updated teacher data TH1_up1, the taste diagnosis unit 217 determines the value of the coefficient k by the method described in the above "(A) Determination of coefficient k" 1 and updates the value of the coefficient k with the determined value. 1 1

[0343] Also, based on the updated teacher data TH2_up1, the taste diagnosis unit 217 determines the value of the coefficient k by the method described in the above "(B) Determination of coefficient k" 2 and updates the value of the coefficient k with the determined value. 2 2

[0344] Furthermore, based on the updated teacher data TH3_up1, the taste diagnosis unit 217 determines the values of the coefficients k 3 , k 4 by the method described in the above "(C) Determination of coefficients k 3 , k 4 and updates the values of the coefficients k 3 , k 4 with the determined values.

[0345] Furthermore, based on the updated teacher data TH4_up1, the taste diagnosis unit 217 determines the values of the coefficients k 5 , k 6 by the method described in the above "(D) Determination of coefficients k 5 , k 6 and updates the values of the coefficients k 5 , k 6 with the determined values.

[0346] Furthermore, based on the updated teacher data TH5_up1, the taste diagnosis unit 217 determines the values of the coefficients k 7 , k 8 by the method described in the above "(E) Determination of coefficients k 7 , k 8 and updates the values of the coefficients k 7 , k 8 ​​​​Update the value.

[0347] Then, when the taste diagnosis unit 217 updates the values of the coefficients k 1 ~k 8 it stores the updated teacher data TH1_up1 to TH5_up1 in the database 22.

[0348] FIG. 17 is a conceptual diagram of the correspondence table TBL1_up1 obtained by updating the correspondence table TBL1 shown in FIG. 15.

[0349] Referring to FIG. 17, the correspondence table TBL1_up1 includes the coefficients k 1_up1 ~k 8_up1 and the teacher data TH1_up1 to TH5_up1. The coefficients k 1_up1 ~k 8_up1 are the updated coefficients, and the teacher data TH1_up1 to TH5_up1 are the updated teacher data.

[0350] The teacher data TH1_up1 is associated with the coefficient k 1_up1 the teacher data TH2_up1 is associated with the coefficient k 2_up1 the teacher data TH3_up1 is associated with the coefficients k 3_up1 ,k 4_up1 the teacher data TH4_up1 is associated with the coefficients k 5_up1 ,k 6_up1 the teacher data TH5_up1 is associated with the coefficients k 7_up1 ,k 8_up1 respectively.

[0351] When the taste diagnosis unit 217 updates the values of the coefficients k 1 ~k 8 by the method described above, it updates the correspondence table TBL1 to the correspondence table TBL1_up1 based on the updated values of the coefficients k 1 ~k 8 and stores the updated correspondence table TBL1_up1 in the database 22.

[0352] Then, the taste diagnosis unit 217 uses the coefficients k 1_up1 ~k8_up1 Using the above-described method, diagnose "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" for one or more new shochus.

[0353] Thereafter, the taste diagnosis unit 217 repeats updating the teacher data TH1 to TH5, updating the coefficients k 1 ~k 8 using the updated teacher data TH1 to TH5, and diagnosing "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" for one or more new shochus.

[0354] (II) Taste diagnosis of grapes When the arithmetic unit 216 receives the arithmetic data CAL (= arithmetic data having the same configuration as the arithmetic data CAL shown in FIG. 12) from the control unit 212, the arithmetic unit 216 calculates the integrated value ITG uni from the arithmetic data CAL _Low (ITG 1_Low ~ITG n_Low ), the integrated value ITG _Middle (ITG 1_Middle ~ITG n_Middle ) and the integrated value ITG _High (ITG 1_High ~ITG n_High ).

[0355] Then, based on the class Cls 1 ~Cls n and the integrated value ITG _Low (ITG 1_Low ~ITG n_Low ), the arithmetic unit 216 detects a plurality of integrated values ITG th ~ITG th corresponding to a predetermined potential range including a potential V equal to or lower than the threshold value V U_Low_th in the negative predetermined potential range. U indicates the class Cls n_Low_th corresponding to the predetermined potential range including the potential V corresponding to the threshold value V th . th

[0356] Also, the arithmetic unit 216 determines the class Cls 1 ~Clsn and integral value ITG _Middle (ITG 1_Middle ~ITG n_Middle ), based on the negative predetermined potential range, among the threshold value V th The following predetermined potential range (= class Cls th ) corresponding to a plurality of integral values ITG U_Middle_th ~ITG n_Middle_th are detected.

[0357] Furthermore, the arithmetic unit 216 is the class Cls 1 ~Cls n and integral value ITG _High (ITG 1_High ~ITG n_High ), based on the negative predetermined potential range, among the threshold value V th The following predetermined potential range (= class Cls) corresponding to a plurality of integral values ITG U_High_th ~ITG n_High_th are detected.

[0358] The subscript "U" in the 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 represents a predetermined potential range including the potential V corresponding to the threshold value V th , so each of the integral values ITG U_Low_th , ITG U_Middle_th , ITG U_High_th is the integral value in the predetermined potential range including the potential V corresponding to the threshold value V th .

[0359] When the arithmetic unit 216 detects 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 , it calculates the sum L(-)_sum_th of the plurality of integral values ITG U_Low_th ~ITG n_Low_th , and the plurality of integral values ITG U_Middle_th ~ITG n_Middle_thCalculate the total sum M(-)_sum_th and the total sum H(-)_sum_th of a plurality of integral values ITG U_High_th ~ITG n_High_th Thereafter, the arithmetic unit 216 calculates the Body index (-)_th by substituting the total sums L(-)_sum_th, M(-)_sum_th, and H(-)_sum_th of the integral values into the following formula.

[0360] Once this is done, the arithmetic unit 216 calculates the Body index (-)_th by substituting the total sums L(-)_sum_th, M(-)_sum_th, and H(-)_sum_th of the integral values into the following formula.

[0361]

Equation

[0362] When the arithmetic unit 216 calculates the Body index (-)_th, it 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 arithmetic unit 216, it calculates the value ASTG_GRP of the "astringency" of the grape by substituting the Body index (-)_th and the coefficient k 9 into the following formula, and diagnoses the calculated value ASTG_GRP as the "astringency" of the grape.

[0364]

Equation

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

[0366] Then, the taste diagnosis unit 217 determines the value of the coefficient k 1 by the same method as the method for determining the value of the coefficient k 9 described above.

[0367] Fig. 18 is a conceptual diagram for diagnosing the taste (astringency, aftertaste, sweetness, aroma, and bitterness) of shochu. With reference to Fig. 18, the taste diagnosis unit 217 uses a plurality of shochus whose "astringency", "aftertaste", "sweetness", "aroma" and "bitterness" are known as teacher data TH1 to TH5, and calculates the coefficient k 1 ~k 8 Value of vle 1_0 ~vle 8_0 (block BLK1).

[0368] And the taste diagnosis unit 217 is a coefficient k 1 ~k 8 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 teaching data TH1_up_Q to TH5_up_Q to calculate the coefficient k 1 ~k 8 Value of vle 1_Q ~vle 8_Q Determine the coefficient k 1 ~k 8 Value of vle 1_Q-1 ~vle 8_Q-1 Each value 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 transitioning from block BLK1 to block BLK2, the coefficient k 1 ~k 8 The value vle 1_Q ~vle 8_Q Are each the value vle 1_0 ~vle 8_0 Since it is set to, the taste diagnosis unit 217, in block BLK2, the coefficient k 1 ~k 8 The value vle 1_0 ~vle 8_0 Is used to diagnose the v "astringencies", v "aftertastes", v "sweetnesses", v "aromas", and v "bitterness" of the v shochus to be diagnosed. That is, the taste diagnosis unit 217 uses a plurality of shochus with known "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" as teacher data TH1~TH5 to determine the coefficient k 1 ~k 8 The value vle 1_0 ~vle 8_0 Is used to diagnose the v "astringencies", v "aftertastes", v "sweetnesses", v "aromas", and v "bitterness" of the v shochus 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, in block BLK3, since Q = Q + 1 = 0 + 1 = 1 is set, the taste diagnosis unit 217 updates the teacher data TH1~TH5 to teacher data TH1_up_1~TH5_up_1 respectively in block BLK4.

[0375] Then, in block BLK5, the taste diagnosis unit 217 uses the updated teacher data TH1_up_1~TH5_up_1 by the method described above to determine the coefficient k 1 ~k 8 The value vle 1_1 ~vle 8_1 And determines the coefficient k 1 ~k 8 The value vle 1_0 ~vle 8_0to the value vle 1_1 ~vle 8_1 and update them.

[0376] After block BLK5, the taste diagnosis unit 217 determines the values vle 1 ~k 8 of the coefficient k 1_1 ~vle 8_1 and diagnoses the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of the v shochus to be diagnosed for the v shochus by the method described above (block BLK2). That is, the taste diagnosis unit 217 diagnoses the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of the v shochus to be diagnosed for the v shochus using the updated teacher data TH1_up_1 to TH5_up_1 determined in block BLK2. 1 ~k 8 of the coefficient k 1_1 ~vle 8_1 and diagnoses the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of the v shochus to be diagnosed for the v shochus.

[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 the teacher data TH1_up_2 to TH5_up_2 respectively in block BLK4.

[0379] Then, the taste diagnosis unit 217 determines the values vle 1 ~k 8 of the coefficient k 1_2 ~vle 8_2 using the updated teacher data TH1_up_2 to TH5_up_2 by the method described above in block BLK5, and the values vle 1 ~k 8 of the coefficient k 1_1 ~vle 8_1 to the value vle 1_2 ~vle 8_2Update it to.

[0380] After block BLK5, the taste diagnosis unit 217 uses the coefficients k 1 ~k 8 value vle 1_2 ~vle 8_2 to diagnose the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of v shochus of the subject to be diagnosed by the method described above (block BLK2). That is, the taste diagnosis unit 217, in block BLK2, uses the updated teacher data TH1_up_2 to TH5_up_2 to determine the coefficients k 1 ~k 8 value vle 1_2 ~vle 8_2 to diagnose the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of v shochus of the subject 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, in block BLK3, since Q = Q + 1 = 2 + 1 = 3 is set, the taste diagnosis unit 217 updates the teacher data TH1_up_2 to TH5_up_2 to the 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 once, v "astringency" and v "Body index (+)" are added to the teacher data TH1, v "aftertaste" and v "Body index (all)" are added to the teacher data TH2, v "sweetness" and v "H(+)_sum" are added to the teacher data TH3, v "aroma" and v "H(-)_sum" are added to the teacher data TH4, and v "bitterness", v "astringency" and v "sweetness" are added to the teacher data TH5.

[0385] Therefore, as the number of times of repeatedly executing blocks BLK2 to BLK5 increases, in block BLK5, the coefficients k 1 ~k 8 with values vle 1 ~vle 8 can have their accuracy increased.

[0386] In FIG. 18, when executing blocks BLK2 to BLK5 for the g-th (where g is an integer of 1 or more) time, set v = v Q (v Q is an integer of 1 or more) and sequentially execute blocks BLK2 to BLK5. When executing blocks BLK2 to BLK5 for the (g + 1)-th time, set v = v R (v R is an integer of 1 or more, and is different from v Q ) and sequentially execute blocks BLK2 to BLK5. That is, the number of "shochu" diagnosed at the (g + 1)-th time (v = v R ) may be different from the number of "shochu" diagnosed at the g-th time (v = v Q ).

[0387] Also, when the taste diagnosis unit 217 diagnoses the "astringency" of grapes, it also diagnoses the "astringency" of grapes while executing blocks BLK1 to BLK5 shown in FIG. 18 and updating the teacher data and the values of the coefficients k 9 .

[0388] Also in this case, the number of "grapes" diagnosed at the (g + 1)-th time (v = v R) may be different from the number (v = v Q ) diagnosed at the g-th time.

[0389] (Example) For the case where the analysis target is shochu and grapes, the curve CUR created by the diagnostic device 2 will be described.

[0390] (1) Shochu The shochu targeted for creating the curve CUR is shown in Table 2.

[0391]

Table 2

[0392] Table 3 shows the measurement conditions of the cyclic voltammogram CVG for the shochu of sample Nos. 1 to 20 shown in Table 2.

[0393]

Table 3

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

[0395] And at the potential scanning speed, 300mV / s corresponds to the potential scanning speed V r_Low corresponds to, 500mV / s corresponds to the potential scanning speed V r_Middle corresponds to, and 600mV / s corresponds to the potential scanning speed V r_High corresponds to.

[0396] Figure 19 is a diagram showing the integral value spectrum for the shochu of sample Nos. 1 to 3 shown in Table 2.

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

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

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

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

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

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

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

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

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

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

[0407] Referring to FIG. 24, each of the curves k15 and k16 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic apparatus 2 by the method described above. The curve k15 shows the integrated value spectrum of the rice shochu of sample No. 15, and the curve k16 shows the integrated value spectrum of the rice shochu of sample No. 16.

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

[0409] Referring to FIG. 25, each of the curves k17 to k20 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. Curve k17 shows the integrated value spectrum of sake lees - used shochu of sample No. 17, curve k18 shows the integrated value spectrum of barley shochu with full - volume 3 - year storage, full - volume barrel storage, and partial cherry - wood barrel storage of sample No. 18, curve k19 shows the integrated value spectrum of barley shochu with full - volume 15 - year storage, all - koji barley, full - volume barrel storage, and after - heating of sample No. 19, and curve k20 shows the rice shochu with full - volume 3 - year storage and full - volume barrel storage of sample No. 20.

[0410] FIGS. 26 to 28 are the first to third figures showing the determination results of whether the curves k1 to k20 shown in FIGS. 19 to 25 are different from each other.

[0411] FIG. 26 shows the determination result of whether the 10 curves k1 to k10 are different from each other, FIG. 27 shows the determination result of whether the 10 curves k1 to k10 and the 10 curves k11 to k20 are different from each other, and FIG. 28 shows the determination result of whether the 10 curves k11 to k20 are different from each other.

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

[0413] And the determination of whether two different curves CUR i , CUR j (i ≠ j) are different is based on a plurality of integrated values ITG i in curve CUR 1_i ~ITG n_i and a plurality of integrated values ITG j in curve CUR 1_j ~ITG n_j and, based on the difference DF between the integrated value ITG k in one class Cls k_i and the integrated value ITG k_j ​k Calculate all levels Cls 1 ~Cls n for the difference DF of n by executing 1 ~DF n and calculate the standard deviation σ of the n calculated differences DF 1 ~DF n to determine whether it is greater than the threshold value σ DFk (= 6%). th This is done by determining whether it is greater than the threshold value σ

[0414] Referring to FIG. 26, for all 45 cases of two different curves selected from 10 curves k1 to k10, the standard deviation σ of the 45 differences DFk1,k2 ~σDF k9,k10 of all are greater than the threshold value σ th (= 6%).

[0415] Referring to FIG. 27, for all 100 cases of two different curves selected from 10 curves k1 to k10 and 10 curves k11 to k20, the standard deviation σ of the 100 differences DFk1,k11 ~σ DFk10,k20 of all are greater than the threshold value σ th (= 6%).

[0416] Referring to FIG. 28, for all 45 cases of two different curves selected from 10 curves k11 to k20, the standard deviation σ of the 45 differences DFk11,k12 ~σ DFk19,k20 of all are greater than the threshold value σ th (= 6%).

[0417] Therefore, for all 190 cases of two different curves selected from 20 curves k1 to k20, the standard deviation σ of the 190 differences DFk1,k2 ~σ DFk19,k20 of all are greater than the threshold value σ th (= 6%).

[0418] Therefore, the 20 curves k1 to k20 are mutually different curves. When it is determined that the 20 curves k1 to k20 are mutually different, the curves k1 to k20 are, respectively, curves for uniquely identifying Shochu No. 1 to Shochu No. 20. And the curves k1 to k20 are, respectively, fingerprints representing feature amounts based on integral values for Shochu No. 1 to Shochu No. 20.

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

[0420] The values of "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" for Shochu No. 1 to Shochu No. 20 shown in FIG. 29 are such that the taste diagnosis unit 217 uses coefficients 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 = 0.4 for diagnosis.

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

[0422] The values of "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" for Shochu No. 1 to Shochu No. 20 shown in FIG. 30 are the average values of "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" diagnosed by 7 people.

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

[0424] The differences in "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" shown in Fig. 31 are obtained by subtracting the values of "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" shown in Fig. 30 from the respective values of "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" shown in Fig. 29.

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

[0426] Referring to Fig. 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] Also, for shochu No. 1 to No. 20, the standard deviation of the difference in "astringency" is 0.271, the standard deviation of the difference in "aftertaste" is 0.408, the standard deviation of the difference in "sweetness" is 0.646, the standard deviation of the difference in "aroma" is 0.393, and the standard deviation of the difference in "bitterness" is 0.419.

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

[0429] Therefore, it has been found that the taste diagnosis unit 217 can diagnose the "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" of shochu in the same way as humans by the method described above.

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

[0431] Each of Crimson Seedless (skin + pulp), Green Seedless (skin + pulp), and Shine Muscat (skin + pulp) is the grape juice obtained by pressing the skin and pulp together, and each of Crimson Seedless (pulp), Green Seedless (pulp), and Shine Muscat (pulp) is the grape juice obtained by pressing only the pulp.

[0432] Also, the measurement conditions for the cyclic voltammogram CVG of Crimson Seedless (skin + pulp), Green Seedless (skin + pulp), Shine Muscat (skin + pulp), Crimson Seedless (pulp), Green Seedless (pulp), and Shine Muscat (pulp) are the same as the measurement conditions shown in Table 3.

[0433] Figure 32 is a diagram showing the integral value spectra for Crimson Seedless (skin + pulp), Green Seedless (skin + pulp), and Shine Muscat (skin + pulp).

[0434] Referring to Figure 32, each of the curves k21 to k23 is a curve CUR (index curve) created by the diagnostic device 2 by the method described above. And the curve k21 shows the integral value spectrum of Crimson Seedless (skin + pulp), the curve k22 shows the integral value spectrum of Green Seedless (skin + pulp), and the curve k23 shows the integral value spectrum of Shine Muscat (skin + pulp).

[0435] FIG. 33 is a diagram showing the integral value spectra for Crimson Seedless (only the fruit), Green Seedless (only the fruit), and Shine Muscat (only the fruit).

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

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

[0438] Whether the curves k21 to k26 are different from each other is determined by performing, for all combinations of two different curves among the curves k21 to k26, a determination as to whether two different curves among the curves k21 to k26 are different from each other.

[0439] The combinations of two different curves among the six curves k21 to k26 are 15 combinations: (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), (k25, k26).

[0440] Referring to FIG. 34, the standard deviation σ of the difference between a plurality of integral values on curve k21 and a plurality of integral values on curve k22 DF_k21,k22 is σ DF_k21,k22 = 58.9%, and the standard deviation σ of the difference between a plurality of integral values on curve k21 and a plurality of integral values on curve k3 DF_k21,k23 is σ DF_k21,k23 = 31.2%, and the standard deviation σ of the difference between a plurality of integral values on curve k21 and a plurality of integral values on curve k24 DF_k21,k24is σ DF_k21,k24 = 62.0%, and the standard deviation σ of the difference between the multiple integral values on curve k21 and the multiple integral values on curve k25 DF_21,k25 is σ DF_k21,k25 = 24.1%, and the standard deviation σ of the difference between the multiple integral values on curve k21 and the multiple integral values on curve k26 DF_k21,k26 is σ DF_k21,k26 = 62.5%.

[0441] Also, the standard deviation σ of the difference between the multiple integral values on curve k22 and the multiple integral values on curve k23 DF_k22,k23 is σ DF_k22,k23 = 65.3%, and the standard deviation σ of the difference between the multiple integral values on curve k22 and the multiple integral values on curve k24 DF_k22,k24 is σ DF_k22,k24 = 16.0%, and the standard deviation σ of the difference between the multiple integral values on curve k22 and the multiple integral values on curve k25 DF_k22,k25 is σ DF_k22,k25 = 16.0%, and the standard deviation σ of the difference between the multiple integral values on curve k22 and the multiple integral values on curve k26 DF_k22,k26 is σ DF_k22,k26 = 23.0%.

[0442] Furthermore, the standard deviation σ of the difference between the multiple integral values on curve k23 and the multiple integral values on curve k24 DF_k23,k24 is σ DF_k23,k24 = 30.1%, and the standard deviation σ of the difference between the multiple integral values on line k23 and the multiple integral values on curve k25 DF_k23,k25 is σ DF_k23,k25 = 31.4%, and the standard deviation σ of the difference between the multiple integral values on curve k23 and the multiple integral values on curve k26 DF_k23,k26 is σ DF_k23,k26 = 24.9%.

[0443] Furthermore, the standard deviation σ of the difference between the multiple integral values on curve k24 and the multiple integral values on curve k25 DF_k24,k25 is σ DF_k24,k24 = 30.3%, and the standard deviation σ of the difference between the multiple integral values on curve k24 and the multiple integral values on curve k26DF_k24,k26 is σ DF_k24,k26 = 22.9%.

[0444] Furthermore, the standard deviation σ of the difference between the plurality of integral values on curve k25 and the plurality of integral values on curve k26 DF_k25,k26 is σ DF_k25,k26 = 24.3%.

[0445] As a result, the standard deviation σ of the difference DF_k21,k22 (= 58.9%), the standard deviation σ of the difference DF_k21,k23 (= 31.2%), the standard deviation σ of the difference DF_k21,k24 (= 62.0%), the standard deviation σ of the difference DF_k21,k25 (= 24.1%), the standard deviation σ of the difference DF_k21,k26 (= 62.5%), the standard deviation σ of the difference DF_k22,k23 (= 65.3%), the standard deviation σ of the difference DF_k22,k24 (= 16.0%), the standard deviation σ of the difference DF_k22,k25 (= 16.0%), the standard deviation σ of the difference DF_k22,k26 (= 23.0%), the standard deviation σ of the difference DF_k23,k24 (= 30.1%), the standard deviation σ of the difference DF_k23,k25 (= 31.4%), the standard deviation σ of the difference DF_k23,k26 (= 24.9%), the standard deviation σ of the difference DF_k24,k25 (= 30.3%), the standard deviation σ of the difference DF_k24,k26 (= 22.9%), and the standard deviation σ of the difference DF_k25,k26 (= 24.3%) are all greater than the threshold value 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 curves that are different from each other.

[0447] When it is determined that the six curves k21 to k26 are different from each other, the curves k21 to k26 are curves for uniquely identifying Crimson Seed Dress (body + skin), Green Seed Dress (body + skin), Shine Muscat (body + skin), Crimson Seed Dress (body only), Green Seed Dress (body only), and Shine Muscat (body only), respectively. And the curves k21 to k26 are fingerprints representing feature amounts by integral values for Crimson Seed Dress (body + skin), Green Seed Dress (body + skin), Shine Muscat (body + skin), Crimson Seed Dress (body only), Green Seed Dress (body only), and Shine Muscat (body only), respectively.

[0448] FIG. 35 is a diagram showing the diagnosis results of the "astringency" of Green Seed Dress (body only), Crimson Seed Dress (body only), Shine Muscat (body only), Green Seed Dress (body + skin), Crimson Seed Dress (body + skin), and Shine Muscat (body + skin).

[0449] Note that the "astringency" shown in FIG. 35 is calculated using L(-)_sum_th, M(-)_sum_th, and H(-)_sum_th calculated based on the integral values in a predetermined potential interval (= class) in the range of -1362 mV to -2501 mV with the threshold V of the above-described potential V th set to "-1362 mV".

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

[0451] Also, the astringency of the crimson seedless (only the flesh) is 2.96, and the astringency of the crimson seedless (flesh + skin) is 4.65.

[0452] Furthermore, the astringency of the shine muscat (only the flesh) is 2.06, and the astringency of the shine muscat (flesh + skin) is 2.47.

[0453] As a result, the "astringency" values increase in the order of shine muscat (only the flesh), green seedless (only the flesh), shine muscat (flesh + skin), green seedless (flesh + skin), crimson seedless (only the flesh), and crimson seedless (flesh + skin).

[0454] Also, the astringency of the green seedless (only the flesh) is smaller than that of the green seedless (flesh + skin), the astringency of the crimson seedless (only the flesh) is smaller than that of the crimson seedless (flesh + skin), and the astringency of the shine muscat (only the flesh) is smaller than that of the shine muscat (flesh + skin).

[0455] Therefore, the "astringency" of the green seedless (only the flesh), crimson seedless (only the flesh), and shine muscat (only the flesh), which are fruit juices obtained by squeezing the flesh of grapes, is smaller than that of the green seedless (flesh + skin), crimson seedless (flesh + skin), and shine muscat (flesh + skin), which are fruit juices obtained by squeezing the flesh and skin of grapes, respectively.

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

[0457] [Relationship between Taste Diagnosis in Shochu and the Number of Integral Values] (I) Number of integrated values: 138 FIG. 36 is a diagram showing the integrated value spectra of the shochus of Sample Nos. 1 to 3 shown in Table 2 when the number of integrated values is 138.

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

[0459] FIG. 37 is a diagram showing the integrated value spectra of the shochus of Sample Nos. 4 to 6 shown in Table 2 when the number of integrated values is 138.

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

[0461] FIG. 38 is a diagram showing the integrated value spectra of the shochus of Sample Nos. 7 to 9 shown in Table 2 when the number of integrated values is 138.

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

[0463] FIG. 39 is a diagram showing the integral value spectra of the shochu of Sample Nos. 10 to 12 shown in Table 2 when the number of integral values is 138.

[0464] Referring to FIG. 39, each of the curves k36 to k38 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. And the curve k36 shows the integral value spectrum of the potato shochu of Sample No. 10, the curve k37 shows the integral value spectrum of the potato shochu of Sample No. 11, and the curve k38 shows the integral value spectrum of the potato shochu of Sample No. 12.

[0465] FIG. 40 is a diagram showing the integral value spectra of the shochu of Sample Nos. 12 to 14 shown in Table 2 when the number of integral values is 138.

[0466] Referring to FIG. 40, each of the curves k38 to k40 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. And the curve k38 shows the integral value spectrum of the potato shochu of Sample No. 12, the curve k39 shows the integral value spectrum of the potato shochu of Sample No. 13, and the curve k40 shows the integral value spectrum of the potato shochu of Sample No. 14.

[0467] FIG. 41 is a diagram showing the integral value spectra of the shochu of Sample Nos. 15 and 16 shown in Table 2 when the number of integral values is 138.

[0468] Referring to FIG. 41, each of the curves k41 and k42 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. And the curve k41 shows the integral value spectrum of the rice shochu of Sample No. 15, and the curve k42 shows the integral value spectrum of the rice shochu of Sample No. 16.

[0469] FIG. 42 is a diagram showing the integral value spectra of the shochu of Sample Nos. 17 to 20 shown in Table 2 when the number of integral values is 138.

[0470] Referring to FIG. 42, each of the curves k43 to k46 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. The curve k43 shows the integrated value spectrum of sake lees shochu of sample No. 17, the curve k44 shows the integrated value spectrum of barley shochu stored for 3 years in total, stored in barrels in total, and partially stored in cherry wood barrels of sample No. 18, the curve k45 shows the integrated value spectrum of barley shochu stored for 15 years in total, made from whole koji wheat, stored in barrels in total, and heat-treated of sample No. 19, and the curve k46 shows rice shochu stored for 3 years in total and stored in barrels in total of sample No. 20.

[0471] From FIG. 36 to FIG. 42, each of classes 1 to 138 consists of a predetermined potential range (integrated value extraction potential) of 36.2 mV. Classes 1 to 69 are classes in the positive predetermined potential range, and classes 70 to 138 are classes in the negative predetermined potential range.

[0472] As a result, for all classes, an integrated value spectrum (a curve CUR similar to curves k27 to k46) consisting of a predetermined potential range (integrated value extraction potential) of 36.2 mV _Low_k27 ~CUR _Low_k46 ,CUR _Middle_k27 ~CUR _Middle_k46 ,CUR _High_k27 ~CUR _High_k46 ), the total sum SMT_ Low(+) ,SMT_ Middle(+) ,SMT _High(+) of the integrated values in the positive predetermined potential range can be calculated. Therefore, based on the integrated value spectrum (a curve CUR similar to curves k27 to k46) consisting of a predetermined potential range (integrated value extraction potential) of 36.2 mV _Low_k27 ~CUR _Low_k46 ,CUR _Middle_k27 ~CUR _Middle_k46 ,CUR _High_k27 ~CUR _High_k46 ), the Body index (+) can be calculated and the astringency ASTG of shochu can be diagnosed by Equation (5). Based on the integrated value spectrum (a curve CUR similar to curves k27 to k46) consisting of a predetermined potential range (integrated value extraction potential) of 36.2 mV for all classes_Low_k27 ~CUR _Low_k46 ,CUR _Middle_k27 ~CUR _Middle_k46 ,CUR _High_k27 ~CUR _High_k46 ) is based on H(+)_sum(=SMT_ High(+) ) is calculated, and the sweetness SWT of shochu can be diagnosed by Equation (7). All levels consist of an integral value spectrum from a predetermined potential range (integral value extraction potential) of 36.2 mV (a curve similar to curves k27 to k46, curve CUR _Low_k27 ~CUR _Low_k46 ,CUR _Middle_k27 ~CUR _Middle_k46 ,CUR _High_k27 ~CUR _High_k46 ) is based on H(-)_sum(=SMT_ High(-) ) is calculated, and the aroma SCT of shochu can be diagnosed by Equation (8). As a result, the bitterness BIT of shochu can be diagnosed by Equation (9).

[0473] Figures 43 to 45 are the first to third figures showing the determination results of whether the curves k27 to k46 shown in Figures 36 to 42 are different from each other, respectively.

[0474] Figure 43 shows the determination result of whether 10 curves k27 to k36 are different from each other. Figure 44 shows the determination result of whether 10 curves k27 to k36 and 10 curves k37 to k46 are different from each other. Figure 45 shows the determination result of whether 10 curves k37 to k46 are different from each other.

[0475] The determination of whether 20 curves k27 to k46 are different from each other is based on determining whether two different curves are different for all combinations ( 20 C 2 =190) when selecting two different curves from 20 curves k27 to k46.

[0476] Referring to Figure 43, for all 45 cases of two different curves when selecting two different curves from 10 curves k27 to k36, the standard deviation σ of the 45 differences DF_k27,k28 ~σ DF_k35,k36 of all are the threshold value σth Greater than (=5%).

[0477] Referring to FIG. 44, for all 100 cases of two different curves selected from 10 curves k27 to k36 and 10 curves k37 to k46, the standard deviation σ of the 100 differences DF_k27,k37 ~σ DF_k36,k46 All of them are greater than the threshold value σ th (=5%).

[0478] Referring to FIG. 45, for all 45 cases of two different curves selected from 10 curves k37 to k46, the standard deviation σ of the 45 differences DF_k37,k38 ~σ DF_k45,k46 All of them are greater than the threshold value σ th (=5%).

[0479] Therefore, for all 190 cases of two different curves selected from 20 curves k27 to k46, the standard deviation σ of the 190 differences DF_k27,k28 ~σ DF_k45,k46 All of them are greater than the threshold value σ th (=5%).

[0480] Therefore, the 20 curves k27 to k46 are curves that are different from each other. When it is determined that the 20 curves k27 to k46 are different from each other, the curves k27 to k46 are, respectively, curves for uniquely identifying Shochu No. 1 to No. 20. And when the number of integral values is 138, the curves k27 to k46 are, respectively, fingerprints representing feature amounts by integral values for Shochu No. 1 to No. 20 of Shochu. (II) Number of integral values: 70 FIG. 46 is a diagram showing integral value spectra for Shochu of sample Nos. 1 to 3 shown in Table 2 when the number of integral values is 70.

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

[0482] FIG. 47 is a diagram showing the integrated value spectra of the shochu of samples No. 4 to No. 6 shown in Table 2 when the number of integrated values is 70.

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

[0484] FIG. 48 is a diagram showing the integrated value spectra of the shochu of samples No. 7 to No. 9 shown in Table 2 when the number of integrated values is 70.

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

[0486] FIG. 49 is a diagram showing the integrated value spectra of the shochu of samples No. 10 to No. 12 shown in Table 2 when the number of integrated values is 70.

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

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

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

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

[0491] Referring to FIG. 51, each of the curves k61 and k62 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. Curve k61 shows the integral value spectrum of the rice shochu of sample No. 15, and curve k62 shows the integral value spectrum of the rice shochu of sample No. 16.

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

[0493] Referring to FIG. 52, each of the curves k63 to k66 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. The curve k63 shows the integrated value spectrum of sake lees - used shochu of sample No. 17, the curve k64 shows the integrated value spectrum of barley shochu of sample No. 18 stored for 3 years in total, stored in barrels in total, and partially stored in cherry - wood barrels, the curve k65 shows the integrated value spectrum of barley shochu of sample No. 19 stored for 15 years in total, made from all - malt, stored in barrels in total, and heat - chased, and the curve k66 shows rice shochu of sample No. 20 stored for 3 years in total and stored in barrels in total.

[0494] In FIGS. 46 to 52, each of class 1 to class 34 consists of a predetermined potential range (integrated value extraction potential) of 72.4 mV, class 35 consists of a predetermined potential range (integrated value extraction potential) of 36.2 mV, each of class 36 to class 69 consists of a predetermined potential range (integrated value extraction potential) of 72.4 mV, and class 70 consists of a predetermined potential range (integrated value extraction potential) of 36.2 mV. And class 1 to class 35 are classes in the positive potential range, and class 36 to class 70 are classes in the negative potential range.

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

[0496] That is, if class 35 consists of the same predetermined potential range (integrated value extraction potential) as the predetermined potential range (integrated value extraction potential) of 72.4 mV in each of class 1 to class 34, the integrated value in class 35 will be the value obtained by summing the integrated value in the positive predetermined potential range and the integrated value in the negative predetermined potential range. Based on the integrated value spectrum in which all classes consist of a predetermined potential range (integrated value extraction potential) of 72.4 mV, the total sum SMT_ Low(+) ,SMT_ Middle(+) ,SMT_ High(+)Since it is impossible to calculate, the Body index (+) cannot be calculated based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 72.4 mV, and the astringency ASTG of shochu cannot be diagnosed by Equation (5). Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 72.4 mV, H(+)_sum (= SMT_ High(+) ) cannot be calculated, and the sweetness SWT of shochu cannot be diagnosed by Equation (7). Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 72.4 mV, H(-)_sum (= SMT_ High(-) ) cannot be calculated, and the aroma SCT of shochu cannot be diagnosed by Equation (8). As a result, the bitterness BIT of shochu cannot be diagnosed by Equation (9).

[0497] Figures 53 to 55 are the first to third figures showing the determination results of whether the curves k47 to k66 shown in Figures 46 to 52 are different from each other, respectively.

[0498] Figure 53 shows the determination result of whether 10 curves k47 to k56 are different from each other. Figure 54 shows the determination result of whether 10 curves k47 to k56 and 10 curves k57 to k66 are different from each other. Figure 55 shows the determination result of whether 10 curves k57 to k66 are different from each other.

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

[0500] Referring to Figure 53, for all 45 differences of 45 different pairs of curves when selecting two different curves from 10 curves k47 to k56, the standard deviations σ DF_k47,k48 ~σ DF_k55,k56 of all are greater than the threshold value σ th (= 5%).

[0501] Referring to FIG. 54, for all 100 different pairs of two curves selected from the 10 curves k47 to k56 and the 10 curves k57 to k66, the standard deviation σ of the 100 differences DF_k47,k57 ~σ DF_k56,k66 of all are greater than the threshold value σ th (=5%).

[0502] Referring to FIG. 55, for all 45 different pairs of two curves selected from the 10 curves k57 to k66, the standard deviation σ of the 45 differences DF_k57,k58 ~σ DF_k65,k66 of all are greater than the threshold value σ th (=5%).

[0503] Therefore, for all 190 different pairs of two curves selected from the 20 curves k47 to k66, the standard deviation σ of the 190 differences DF_k47,k48 ~σ DF_k65,k66 of all are greater than the threshold value σ th (=5%).

[0504] Thus, the 20 curves k47 to k66 are curves that are different from each other. When it is determined that the 20 curves k47 to k66 are different from each other, the curves k47 to k66 are, respectively, curves for uniquely identifying the shochu of No. 1 to No. 20. And when the number of integral values is 70, the curves k47 to k66 are, respectively, fingerprints representing the feature quantities by integral values for the shochu of No. 1 to No. 20 of shochu.

[0505] Other explanations for the case where the number of integral values is 70 are the same as the explanations for the case where the number of integral values is 138. (III) Number of integral values: 36 FIG. 56 is a diagram showing the integral value spectra for the shochu of sample Nos. 1 to 3 shown in Table 2 for the case where the number of integral values is 36.

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

[0507] FIG. 57 is a diagram showing the integrated value spectra of the shochu of samples No. 4 to No. 6 shown in Table 2 when the number of integrated values is 36.

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

[0509] FIG. 58 is a diagram showing the integrated value spectra of the shochu of samples No. 7 to No. 9 shown in Table 2 when the number of integrated values is 36.

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

[0511] FIG. 59 is a diagram showing the integrated value spectra of the shochu of samples No. 10 to No. 12 shown in Table 2 when the number of integrated values is 36.

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

[0513] FIG. 60 is a diagram showing the integrated value spectra of the shochu of samples No. 12 to No. 14 shown in Table 2 when the number of integrated values is 36.

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

[0515] FIG. 61 is a diagram showing the integrated value spectra of the shochu of samples No. 15 and No. 16 shown in Table 2 when the number of integrated values is 36.

[0516] Referring to FIG. 61, each of the curves k81 and k82 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic apparatus 2 by the method described above. Curve k81 shows the integrated value spectrum of the rice shochu of sample No. 15, and curve k82 shows the integrated value spectrum of the rice shochu of sample No. 16.

[0517] FIG. 62 is a diagram showing the integrated value spectra of the shochu of samples No. 17 to No. 20 shown in Table 2 when the number of integrated values is 36.

[0518] Referring to FIG. 62, each of the curves k83 to k86 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. Curve k83 shows the integrated value spectrum of sake lees - used shochu of sample No. 17, curve k84 shows the integrated value spectrum of barley shochu stored for 3 years in total, stored in barrels in total, and partially stored in cherry - wood barrels of sample No. 18, curve k85 shows the integrated value spectrum of barley shochu stored for 15 years in total, made from whole - koji barley, stored in barrels in total, and heat - chased of sample No. 19, and curve k86 shows rice shochu stored for 3 years in total and stored in barrels in total of sample No. 20.

[0519] In FIGS. 56 to 62, each of classes 1 to 17 consists of a predetermined potential range (integrated value extraction potential) of 144.8 mV, class 18 consists of a predetermined potential range (integrated value extraction potential) of 36.2 mV, each of classes 19 to 35 consists of a predetermined potential range (integrated value extraction potential) of 144.8 mV, and class 36 consists of a predetermined potential range (integrated value extraction potential) of 36.2 mV. And classes 1 to 18 are classes in the positive potential range, and classes 19 to 36 are classes in the negative potential range.

[0520] The reason why class 18 consists of a predetermined potential range (integrated value extraction potential) of 36.2 mV, which is smaller than the predetermined potential range (integrated 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 range".

[0521] That is, if class 18 consists of the same predetermined potential range (integrated value extraction potential) as the predetermined potential range (integrated value extraction potential) of 144.8 mV in each of classes 1 to 17, the integrated value in class 18 will be the value obtained by summing the integrated value in the positive predetermined potential range and the integrated value in the negative predetermined potential range. Based on the integrated value spectrum in which all classes consist of a predetermined potential range (integrated value extraction potential) of 144.8 mV, the total sum SMT_ Low(+) ,SMT_ Middle(+) ,SMT_ High(+)Since it is impossible to calculate, the Body index (+) cannot be calculated based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 144.8 mV, and the astringency ASTG of shochu cannot be diagnosed by Equation (5). Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 144.8 mV, H(+)_sum (= SMT_ High(+) ) cannot be calculated, and the sweetness SWT of shochu cannot be diagnosed by Equation (7). Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 144.8 mV, H(-)_sum (= SMT _High(-) ) cannot be calculated, and the aroma SCT of shochu cannot be diagnosed by Equation (8). As a result, the bitterness BIT of shochu cannot be diagnosed by Equation (9).

[0522] Figures 63 to 65 are the first to third figures respectively showing the determination results of whether the curves k67 to k86 shown in Figures 56 to 62 are different from each other.

[0523] Figure 63 shows the determination result of whether 10 curves k67 to k76 are different from each other. Figure 64 shows the determination result of whether 10 curves k67 to k76 and 10 curves k77 to k86 are different from each other. Figure 65 shows the determination result of whether 10 curves k77 to k86 are different from each other.

[0524] The determination of whether 20 curves k67 to k86 are different from each other is performed by determining whether two different curves are different for all of the combination numbers ( 20 C 2 = 190) when two different curves are selected from 20 curves k67 to k86.

[0525] Referring to Figure 63, for all 45 differences of 45 pairs of different curves when two different curves are selected from 10 curves k67 to k76, the standard deviations σ DF_k67,k68 ~σ DF_k75,k76 of all are greater than the threshold value σ th (= 5%).

[0526] Referring to FIG. 64, for all 100 different pairs of two curves selected from the 10 curves k67 to k76 and the 10 curves k77 to k86, the standard deviation σ of the 100 differences DF_k67,k77 ~σ DF_k76,k86 of all are greater than the threshold value σ th (=5%).

[0527] Referring to FIG. 65, for all 45 different pairs of two curves selected from the 10 curves k77 to k86, the standard deviation σ of the 45 differences DF_k77,k78 ~σ DF_k85,k86 of all are greater than the threshold value σ th (=5%).

[0528] Therefore, for all 190 different pairs of two curves selected from the 20 curves k67 to k86, the standard deviation σ of the 190 differences DF_k67,k68 ~σ DF_k85,k86 of all are greater than the threshold value σ th (=5%).

[0529] Thus, the 20 curves k67 to k86 are curves that are different from each other. When it is determined that the 20 curves k67 to k86 are different from each other, the curves k67 to k86 are, respectively, curves for uniquely identifying the shochu of No. 1 to No. 20. And when the number of integral values is 36, the curves k67 to k86 are, respectively, fingerprints representing feature quantities by integral values for the shochu of No. 1 to No. 20 of shochu.

[0530] Other explanations for the case where the number of integral values is 36 are the same as the explanations for the case where the number of integral values is 138. (IV) Number of integral values: 18 FIG. 66 is a diagram showing the integral value spectra for the shochu of sample Nos. 1 to 3 shown in Table 2 for the case where the number of integral values is 18.

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

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

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

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

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

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

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

[0538] FIG. 70 is a diagram showing the integrated value spectra of the shochu of samples No. 12 to No. 14 shown in Table 2 when the number of integrated values is 18.

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

[0540] FIG. 71 is a diagram showing the integrated value spectra of the shochu of samples No. 15 and No. 16 shown in Table 2 when the number of integrated values is 18.

[0541] Referring to FIG. 71, each of the curves k101 and k102 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic apparatus 2 by the method described above. Curve k101 shows the integrated value spectrum of the rice shochu of sample No. 15, and curve k102 shows the integrated value spectrum of the rice shochu of sample No. 16.

[0542] FIG. 72 is a diagram showing the integrated value spectra of the shochu of samples No. 17 to No. 20 shown in Table 2 when the number of integrated values is 18.

[0543] Referring to FIG. 72, each of the curves k103 to k106 is a curve CUR (index curve) created by the creation unit 215 of the diagnostic device 2 by the method described above. The curve k103 shows the integral value spectrum of sake lees - used shochu of sample No. 17, the curve k104 shows the integral value spectrum of barley shochu of sample No. 18 stored for 3 years in total, stored in barrels in total, and partially stored in cherry - wood barrels, the curve k105 shows the integral value spectrum of barley shochu of sample No. 19 stored for 15 years in total, made entirely from barley koji, stored in barrels in total, and with after - heating, and the curve k106 shows rice shochu of sample No. 20 stored for 3 years in total and stored in barrels in total.

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

[0545] The reason why class 9 consists of 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 307.7 mV in each of class 1 to class 8, is to make class 9 "the last class in the positive predetermined potential range".

[0546] That is, when class 9 consists of the same predetermined potential range (integral value extraction potential) as the predetermined potential range (integral value extraction potential) of 307.7 mV in each of class 1 to class 8, the integral value in class 9 is the value obtained by summing the integral value in the positive predetermined potential range and the integral value in the negative predetermined potential range. Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 307.7 mV, the total sum SMT_ Low(+) ,SMT_ Middle(+) ,SMT_ High(+)Since it is impossible to calculate, based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 307.7 mV, the Body index (+) cannot be calculated, and the astringency ASTG of shochu cannot be diagnosed by Equation (5). Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 307.7 mV, H(+)_sum (= SMT_ High(+) ) cannot be calculated, and the sweetness SWT of shochu cannot be diagnosed by Equation (7). Based on the integral value spectrum in which all classes consist of a predetermined potential range (integral value extraction potential) of 307.7 mV, H(-)_sum (= SMT_ High(-) ) cannot be calculated, and the aroma SCT of shochu cannot be diagnosed by Equation (8). As a result, the bitterness BIT of shochu cannot be diagnosed by Equation (9).

[0547] Figures 73 to 75 are the first to third figures respectively showing the determination results as to whether the curves k87 to k106 shown in Figures 66 to 72 are different from each other.

[0548] Figure 73 shows the determination result as to whether the 10 curves k87 to k96 are different from each other. Figure 74 shows the determination result as to whether the 10 curves k87 to k96 and the 10 curves k97 to k106 are different from each other. Figure 75 shows the determination result as to whether the 10 curves k97 to k106 are different from each other.

[0549] The determination as to whether the 20 curves k87 to k106 are different from each other is made by determining whether two different curves are different for all of the combinations ( 20 C 2 = 190) when selecting two different curves from the 20 curves k87 to k106.

[0550] Referring to Figure 73, for all 45 differences of 45 different pairs of curves when selecting two different curves from the 10 curves k87 to k96, the standard deviations σ DF_k87,k88 ~σ DF_k95,k96 of all are greater than the threshold value σ th (= 5%).

[0551] Referring to FIG. 74, for all 100 cases of two different curves selected from the 10 curves k87 to k96 and the 10 curves k97 to k106, the standard deviation σ of the 100 differences DF_k87,k97 ~σ DF_k96,k106 of all are greater than the threshold value σ th (=5%).

[0552] Referring to FIG. 75, for all 45 cases of two different curves selected from the 10 curves k97 to k106, the standard deviation σ of the 45 differences DF_k97,k98 ~σ DF_k105,k106 of all are greater than the threshold value σ th (=5%).

[0553] Therefore, for all 190 cases of two different curves selected from the 20 curves k87 to k106, the standard deviation σ of the 190 differences DF_k87,k88 ~σ DF_k105,k106 of all are greater than the threshold value σ th (=5%).

[0554] Thus, the 20 curves k87 to k106 are curves that are different from each other. When it is determined that the 20 curves k87 to k106 are different from each other, the curves k87 to k106 are, respectively, curves for uniquely identifying Shochu No. 1 to No. 20. And when the number of integral values is 18, the curves k87 to k106 are, respectively, fingerprints representing feature quantities by integral values for Shochu No. 1 to No. 20 of Shochu.

[0555] Although not shown, also for the cases where the number of integral values is 92, 56, 32, and 20, when selecting two different curves from the 20 curves, the combination number( 20 C 2 =190) of all, it was confirmed that the standard deviation of the differences is greater than the threshold value σ th (5%).

[0556] The 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 Shochu No.1 consists of a plurality of curves k1, k27, k47, k67, k87 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.2 consists of a plurality of curves k2, k28, k48, k68, k88 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.3 consists of a plurality of curves k3, k29, k49, k69, k89 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.4 consists of a plurality of curves k4, k30, k50, k70, k90 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.5 consists of a plurality of curves k5, k31, k51, k71, k91 with mutually different numbers of integral values.

[0558] Also, the index curve for uniquely identifying Shochu No.6 consists of a plurality of curves k6, k32, k52, k72, k92 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.7 consists of a plurality of curves k7, k33, k53, k73, k93 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.8 consists of a plurality of curves k8, k34, k54, k74, k94 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.9 consists of a plurality of curves k9, k35, k55, k75, k95 with mutually different numbers of integral values; the index curve for uniquely identifying Shochu No.10 consists of a plurality of curves k10, k36, k56, k76, k96 with mutually different numbers of integral values.

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

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

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

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

[0563] In formula (3), the taste diagnosis unit 217 uses the scanning speed V of the potential r_Low to calculate the total sum SMT_ of the integral values in the positive potential interval of the integral value spectrum created based on the cyclic voltammogram measured using Low(+) the scanning speed V of the potential r_Middle to calculate the total sum SMT_ of the integral values in the positive potential interval of the integral value spectrum created based on the cyclic voltammogram measured using Middle(+) the scanning speed V of the potential r_High to calculate the total sum SMT_ of the integral values in the positive potential interval of the integral value spectrum created based on the cyclic voltammogram measured using High(+) to calculate the Body index (+).

[0564] Also, in formula (4), the taste diagnosis unit 217 uses the scanning speed V of the potential r_Low to calculate the total sum SMT_ of the integral values in all potential intervals of the integral value spectrum created based on the cyclic voltammogram measured using Low(all) the scanning speed V of the potential r_Middle to calculate the total sum SMT_ of the integral values in all potential intervals of the integral value spectrum created based on the cyclic voltammogram measured using Middle(all) the scanning speed V of the potential r_High to calculate the total sum SMT_ of the integral values in all potential intervals of the integral value spectrum created based on the cyclic voltammogram measured using High(all) to calculate the Body index (all).

[0565] Figure 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] Referring to FIGS. 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 sum of the integral values ITG in classes 1 and 2 when the number of integral values (= the number of classes) is 276 1_276 , ITG 2_276 , and the integral value ITG in class 2 2_138 is the sum of the integral values ITG in classes 3 and 4 when the number of integral values (= the number of classes) is 276 3_276 , ITG 4_276 , and so on. The integral value ITG in class 68 68_138 is the sum of the integral values ITG in classes 137 and 138 when the number of integral values (= the number of classes) is 276 137_276 , ITG 138_276 , and the integral value ITG in class 69 69_138 is the sum of the integral values ITG in classes 139 and 140 when the number of integral values (= the number of classes) is 276 139_276 , ITG 140_276 , and so on. The integral value ITG in class 138 138_138 is the sum of the integral values ITG in classes 275 and 276 when the number of integral values (= the number of classes) is 276 275_276 , ITG 276_276 .

[0567] And in FIG. 76(a), classes 1 to 138 are the classes in the positive potential range, and the integral values ITG 1_276 ~ITG 138_276 are the integral values in the positive potential range. Classes 139 to 276 are the classes in the negative potential range, and the integral values ITG 139_276 ~ITG 276_276 are the integral values in the negative potential range.

[0568] As a result, in (b) of FIG. 76, classes 1 to 68 are classes in the positive potential range, and the integrated values ITG 1_138 ~ITG 68_138 are integrated values in the positive potential range. Classes 69 to 138 are classes in the negative potential range, and the integrated values ITG 69_138 ~ITG 138_138 are integrated values in the negative potential range.

[0569] Then, the sum of the integrated values ITG 1_138 ~ITG 68_138 in classes 1 to 68 in (b) of FIG. 76 is equal to the sum of the integrated values ITG 1_276 ~ITG 138_276 in classes 1 to 138 in (a) of FIG. 76.

[0570] Also, the sum of the integrated values ITG 69_138 ~ITG 138_138 in classes 69 to 138 in (b) of FIG. 76 is equal to the sum of the integrated values ITG 139_276 ~ITG 276_276 in classes 139 to 276 in (a) of FIG. 76.

[0571] Furthermore, the sum of the integrated values ITG 1_138 ~ITG 138_138 in classes 1 to 138 in (b) of FIG. 76 is equal to the sum of the integrated values ITG 1_276 ~ITG 276_276 in classes 1 to 276 in (a) of FIG. 76.

[0572] Therefore, the sum SMT_ r_Low of the integrated values in the positive potential range of the integrated value spectrum composed of 138 integrated values created based on the cyclic voltammogram measured using the potential scanning speed V Low(+)_138 agrees with the sum SMT_ r_Low of the integrated values in the positive potential range of the integrated value spectrum composed of 276 integrated values created based on the cyclic voltammogram measured using the potential scanning speed V Low(+)_276 in the positive potential range.

[0573] The potential scanning speed V r_MiddleThe sum SMT_ of the integral values in the positive potential range of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using Middle(+)_138 and the scanning speed V of the potential r_High The sum SMT_ of the integral values in the positive potential range of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using High(+)_138 is the same.

[0574] As a result, in Equation (3), the sum SMT_ of the integral values Low(+)_138 , SMT_ Middle(+)_138 , SMT_ High(+)_138 The Body index (+) calculated based on _138 is the same as the Body index (+) calculated based on the sum SMT_ of the integral values Low(+)_276 , SMT_ Middle(+)_276 , SMT_ High(+)_276 The Body index (+) calculated based on _276 matches.

[0575] Also, the sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential r_Low is the same as the sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 276 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential Low(all)_138 The sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential r_Low is the same as the sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 276 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential Low(all)_276 matches.

[0576] The sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential r_Middle is the same as the sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential Middle(all)_138 and the sum SMT_ of the integral values in all potential ranges of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using the scanning speed V of the potential r_High is the same. High(all)_138 is the same.

[0577] As a result, in Equation (4), the total sum SMT of the integral values _Low(all)_138 , SMT_ Middle(all)_138 , SMT_ High(all)_138 The Body index (all) calculated using _138 is the total sum SMT_ Low(all)_276 , SMT_ Middle(all)_276 , SMT_ High(all)_276 The Body index (all) calculated using _276 coincides with it.

[0578] Furthermore, the scanning speed V of the potential r_High The total sum SMT_ of the integral values in the positive potential range of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using High(+) which is H(+)_sum _138 is the total sum SMT_ of the integral values in the positive potential range of the integral value spectrum consisting of 276 integral values created based on the cyclic voltammogram measured using the potential scanning speed V r_High which is H(+)_sum High(+) coincides with it. _276

[0579] Furthermore, the scanning speed V of the potential r_High The total sum SMT_ of the integral values in the negative potential range of the integral value spectrum consisting of 138 integral values created based on the cyclic voltammogram measured using High(-) which is H(-)_sum _138 is the total sum SMT_ of the integral values in the negative potential range of the integral value spectrum consisting of 276 integral values created based on the cyclic voltammogram measured using the potential scanning speed V r_High which is H(-)_sum High(-) coincides with it. _276

[0580] Even if the number of integral values changes, the coefficients k in Equations (5) to (9) 1 ~k 8 do not change. Therefore, in Equation (5), the Body index (+) _138 ​​The astringency ASTG of shochu calculated using _138 is the Body Mass Index (+) _276 The astringency ASTG of shochu calculated using _276 is consistent with that of the Body Mass Index (all). In Equation (6), the aftertaste LNGS of shochu calculated using the Body Mass Index (all) _138 is the aftertaste LNGS of shochu calculated using the Body Mass Index (all) _138 is consistent with that of the Body Mass Index (all). In Equation (7), the sweetness SWT of shochu calculated using H(+)_sum _276 (=SMT_ _276 ) is the sweetness SWT of shochu calculated using H(+)_sum _138 (=SMT_ High(+)_138 ) is consistent with that of the sweetness SWT of shochu calculated using H(+)_sum _138 In Equation (8), the aroma SCT of shochu calculated using H(-)_sum _276 (=SMT_ High(+)_276 ) is the aroma SCT of shochu calculated using H(-)_sum _276 is consistent with that of the aroma SCT of shochu calculated using H(-)_sum _138 (=SMT_ High(-)_138 ) In Equation (9), the bitterness BIT of shochu calculated using the astringency ASTG _138 and sweetness SSWT _276 (=SMT_ High(-)_276 ) is the bitterness BIT of shochu calculated using the astringency ASTG _276 and sweetness SWT _138 is consistent with that of the bitterness BIT of shochu calculated using the astringency ASTG _138 and sweetness SWT _138 of shochu calculated using _276 and sweetness SWT _276 is consistent with that of the bitterness BIT of shochu calculated using _276 .

[0581] The same applies when the number of integral values is any of 92, 70, 56, 36, 32, 20, and 18.

[0582] Therefore, even if the number of integral values in the integral value spectrum, which is the fingerprint of Shochu No. 1 to Shochu No. 20, changes, the results of the taste diagnosis of Shochu No. 1 to Shochu No. 20 are consistent with "astringency", "aftertaste", "sweetness", "aroma", and "bitterness" shown in Figure 29.

[0583] As a result, the diagnostic device 2 has an integrated value spectrum ITG in which the number of integrated values is any one of 18, 20, 32, 36, 56, 70, 92, 138, and 276 _SPC_n Based on this, for sake of No.1 to No.20 of shochu, taste diagnosis may be performed by the method described above.

[0584] In this case, the integrated value spectrum ITG _SPC_n is composed of an integrated value spectrum consisting of integrated values ITG 1_Low ~ITG n_Low , an integrated value spectrum consisting of integrated values ITG 1_Middle ~ITG n_Middle , and an integrated value spectrum consisting of integrated values ITG 1_High ~ITG n_High , and n is any one of n = 18, 20, 32, 36, 56, 70, 92, 138, 276.

[0585] [Relationship between taste diagnosis in grapes and the number of integrated 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 calculated based on the integrated values in a predetermined potential interval (= class) in the range of -1362 mV to -2501 mV are used.

[0586] Therefore, an integrated value spectrum in which the number of integrated values in the predetermined potential interval (= class) in the range of -1362 mV to -2501 mV used for taste diagnosis is changed will be described. (I) Number of integrated values: 74 FIG. 77 is a diagram showing the integrated value spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integrated values in the predetermined potential interval in the range of -1362 mV to -2501 mV is 74.

[0587] Referring to FIG. 77, each of the curves k107 to k109 is a curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. And the curve k107 shows the integrated value spectrum of crimson seedless (skin + flesh), the curve k108 shows the integrated value spectrum of green seedless (skin + flesh), and the curve k109 shows the integrated value spectrum of shine muscat (skin + flesh).

[0588] FIG. 78 is a diagram showing the integrated value spectra of crimson seedless (flesh only), green seedless (flesh only), and shine muscat (flesh only) when the number of integrated values in a predetermined potential range of -1362 mV to -2501 mV is 74.

[0589] Referring to FIG. 78, each of the curves k110 to k112 is a curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. And the curve k110 shows the integrated value spectrum of crimson seedless (flesh only), the curve k111 shows the integrated value spectrum of green seedless (flesh only), and the curve k112 shows the integrated value spectrum of shine muscat (flesh only).

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

[0591] FIG. 79 is a diagram showing the determination result as to whether the curves k107 to k112 shown in FIGS. 77 and 78 are different from each other.

[0592] Whether the curves k107 to k112 are different from each other is determined by performing, for all combinations of different two curves among the curves k107 to k112, a determination as to whether two different curves among the curves k107 to k112 are different from each other.

[0593] Among the six curves k107 to k112, the combinations of two different curves are (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), (k111, k112), a total of 15 combinations.

[0594] Referring to FIG. 79, the standard deviations σ of the 15 differences DF_k107,k108 , σ DF_k107,k109 , ···, σ DF_k111,k112 Among them, the minimum value is 8.69% and the maximum value is 89.09%.

[0595] Therefore, all of the standard deviations σ of the 15 differences DF_k107,k108 , σ DF_k107,k109 , ···, σ DF_k111,k112 are greater than the threshold value σ th (=8%).

[0596] Therefore, the curves k107 to k112 are mutually different curves, and each represents a fingerprint of the integrated value characteristics 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 integrated values: 37 FIG. 80 is a diagram showing the integrated value spectra of Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), and Shine Muscat (skin + flesh) when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 37.

[0597] Referring to FIG. 80, each of the curves k113 to k115 is the curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. And the curve k113 shows the integral value spectrum of crimson seedless (skin + flesh), the curve k114 shows the integral value spectrum of green seedless (skin + flesh), and the curve k115 shows the integral value spectrum of shine muscat (skin + flesh).

[0598] FIG. 81 is a diagram showing the integral value spectra of crimson seedless (flesh only), green seedless (flesh only), and shine muscat (flesh only) when the number of integral values in a predetermined potential range of -1362 mV to -2501 mV is 37.

[0599] Referring to FIG. 81, each of the curves k116 to k118 is the curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. And the curve k116 shows the integral value spectrum of crimson seedless (flesh only), the curve k117 shows the integral value spectrum of green seedless (flesh only), and the curve k118 shows the integral value spectrum of shine muscat (flesh only).

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

[0601] FIG. 82 is a diagram showing the determination result as to whether the curves k113 to k118 shown in FIGS. 80 and 81 are different from each other.

[0602] Whether the curves k113 to k118 are different from each other is determined by performing, for all combinations of different two curves among the curves k113 to k118, a determination as to whether any two different curves among the curves k113 to k118 are different.

[0603] Among the six curves k113 to k118, the combinations of two different curves are 15 cases: (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), (k117, k118).

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

[0605] Therefore, all of the 15 standard deviations of differences σ DF_k113,k114 , σ DF_k113,k115 , ···, σ DF_k117,k118 are greater than the threshold value σ th (= 8%).

[0606] Therefore, the curves k113 to k118 are mutually different curves, and each represents a fingerprint of the feature quantity by the integral value for Crimson Seedless (skin + flesh), Green Seedless (skin + flesh), Shine Muscat (skin + flesh), Crimson Seedless (only flesh), Green Seedless (only flesh), and Shine Muscat (only flesh). (III) Number of integral values: 19 FIG. 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 the predetermined potential range from -1362 mV to -2501 mV is 19.

[0607] Referring to Fig. 83, each of the curves k119 to k121 is the curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. Curve k119 shows the integral value spectrum of crimson seedless (skin + pulp), curve k120 shows the integral value spectrum of green seedless (skin + pulp), and curve k121 shows the integral value spectrum of shine muscat (skin + pulp).

[0608] Fig. 84 is a diagram showing the integral value 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 19.

[0609] Referring to Fig. 84, each of the curves k122 to k124 is the curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. Curve k122 shows the integral value spectrum of crimson seedless (pulp only), curve k123 shows the integral value spectrum of green seedless (pulp only), and curve k124 shows the integral value spectrum of shine muscat (pulp only).

[0610] In Figs. 83 and 84, each of classes 1 to 18 consists of a predetermined potential range of 72.4 mV, and class 19 consists of a predetermined potential range of 36.2 mV.

[0611] Fig. 85 is a diagram showing the determination result of whether the curves k119 to k124 shown in Figs. 83 and 84 are different from each other.

[0612] Whether the curves k119 to k124 are different from each other is determined by performing, for all combinations of different pairs of the curves k119 to k124, a determination of whether two different curves among the curves k119 to k124 are different.

[0613] Among the six curves k119 to k124, the combinations of two different curves are 15 cases: (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), (k123, k124).

[0614] Referring to FIG. 85, the standard deviations σ of the 15 differences DF_k119,k120 , σ DF_k119,k121 , ···, σ DF_k123,k124 Among them, the minimum value is 8.54% and the maximum value is 120.43%.

[0615] Therefore, all of the standard deviations σ of the 15 differences DF_k119,k120 , σ DF_k119,k121 , ···, σ DF_k123,k124 are greater than the threshold value σ th (= 8%).

[0616] Thus, the curves k119 to k124 are mutually different curves, and each represents a fingerprint of the feature quantity by the integral value 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 FIG. 86 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 9.

[0617] Referring to FIG. 86, each of the curves k125 to k127 is the curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. And the curve k125 shows the integral value spectrum of crimson seedless (skin + flesh), the curve k126 shows the integral value spectrum of green seedless (skin + flesh), and the curve k127 shows the integral value spectrum of shine muscat (skin + flesh).

[0618] FIG. 87 is a diagram showing the integral value spectra of crimson seedless (flesh only), green seedless (flesh only), and shine muscat (flesh only) when the number of integral values in a predetermined potential range of -1362 mV to -2501 mV is 9.

[0619] Referring to FIG. 87, each of the curves k128 to k130 is the curve CUR created by the diagnostic device 2 by the method described above in a predetermined potential range of -1362 mV to -2501 mV. And the curve k128 shows the integral value spectrum of crimson seedless (flesh only), the curve k129 shows the integral value spectrum of green seedless (flesh only), and the curve k130 shows the integral value spectrum of shine muscat (flesh only).

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

[0621] FIG. 88 is a diagram showing the determination result as to whether or not 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 each other is determined by performing, for all combinations of different two curves among the curves k125 to k130, a determination as to whether or not two different curves among the curves k125 to k130 are different.

[0623] Among the six curves k125 to k130, the combinations of two different curves are 15 cases of (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), (k129, k130).

[0624] Referring to FIG. 88, the standard deviations σ of the 15 differences DF_k125,k126 , σ DF_k125,k127 , ···, σ DF_k129,k130 Among them, the minimum value is 8.18% and the maximum value is 129.56%.

[0625] Therefore, all of the standard deviations σ of the 15 differences DF_k125,k126 , σ DF_k125,k127 , ···, σ DF_k129,k130 are all greater than the threshold value σ th (= 8%).

[0626] Thus, the curves k125 to k130 are mutually different curves, and each represents a fingerprint of the feature quantity by the integral value 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 the illustration is omitted, 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, for all of the combination numbers ( 6 C 2 = 15) when selecting two different curves from the six curves, it was confirmed that the standard deviation of the difference is greater than the threshold value (8%).

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

[0629] As described above, the "astringency" of grapes is the Body index (-) shown in Equation (10) th is calculated, and the calculated Body index (-) th is substituted into Equation (11) for calculation.

[0630] Body index (-) th is calculated based on the total integral values L(-)_sum_th, M(-)_sum_th, H(-)_sum_th shown in Equation (10).

[0631] The total integral value L(-)_sum_th is the sum of the integral values in the predetermined potential range of -1362 mV to -2501 mV among the plurality of integral values calculated based on the cyclic voltammogram measured with the potential scanning speed set to the potential scanning speed V r_Low The total integral value M(-)_sum_th is the sum of the integral values in the predetermined potential range of -1362 mV to -2501 mV among the plurality of integral values calculated based on the cyclic voltammogram measured with the potential scanning speed set to the potential scanning speed V r_MiddleOf the plurality of integrated values calculated based on the cyclic voltammogram measured by setting to, it is the sum of the integrated values in a predetermined potential range of -1362 mV to -2501 mV, and the sum of the integrated values H(-)_sum_th is the scanning speed of the potential to the scanning speed V of the potential r_High Of the plurality of integrated values calculated based on the cyclic voltammogram measured by setting to, it is the sum of the integrated values in a predetermined potential range of -1362 mV to -2501 mV.

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

[0633] Referring to FIG. 89, when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 74, the integrated values in classes 1 to 74 are respectively ITG 1_74 ~ITG 74_74 respectively.

[0634] When the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 37, the integrated value ITG in class 1 1_37 is the sum of the integrated value ITG 1_74 , ITG 2_74 when the number of integrated values is 74, and the integrated value ITG in class 2 2_37 is the sum of the integrated value ITG 3_74 , ITG 4_74 when the number of integrated values is 74, ···, the integrated value ITG in class 36 36_37 is the sum of the integrated value ITG 71_74 , ITG 72_74 when the number of integrated values is 74, and the integrated value ITG in class 37 37_37 is the sum of the integrated value ITG 73_74 , ITG 74_74 when the number of integrated values is 74.

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

[0636] Then, although not shown in the figure, the sum of the 19 integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 19 is equal to the sum of the 74 integrated values when the number of integrated values is 74, and the sum of the 9 integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 9 is equal to the sum of the 74 integrated values when the number of integrated values is 74.

[0637] Therefore, the sum of the integrated values L(-)_sum_th, which is the sum of the integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 74 _74 the sum of the integrated values L(-)_sum_th, which is the sum of the integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 37 _37 the sum of the integrated values L(-)_sum_th, which is the sum of the integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 19 _19 and the sum of the integrated values L(-)_sum_th, which is the sum of the integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 9 _9 are equal to each other.

[0638] Similarly, the sum of the integrated values M(-)_sum_th, which is the sum of the integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 74 _74 the sum of the integrated values M(-)_sum_th, which is the sum of the integrated values when the number of integrated values in the predetermined potential range of -1362 mV to -2501 mV is 37 _37The sum of integral values M(-)_sum_th, which is the sum of integral values when the number of integral values in a predetermined potential range of -1362 mV to -2501 mV is 19 _19 and the sum of integral values M(-)_sum_th, which is the sum of integral values 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, which is the sum of integral values 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, which is the sum of integral values when the number of integral values in a predetermined potential range of -1362 mV to -2501 mV is 37 _37 the sum of integral values H(-)_sum_th, which is the sum of integral values when the number of integral values in a predetermined potential range of -1362 mV to -2501 mV is 19 _19 and the sum of integral values H(-)_sum_th, which is the sum of integral values 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 a predetermined potential range of -1362 mV to -2501 mV, the Body index (-) when the number of integral values is 74 th_74 the Body index (-) when the number of integral values is 37 th_37 the 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] Then, even if the number of integral values changes, the coefficient k shown in formula (11) 9Since it does not change, the "astringency" of grapes when the number of integral values in a predetermined potential range within -1362 mV to -2501 mV is 74, the "astringency" of grapes when the number of integral values in a predetermined potential range within -1362 mV to -2501 mV is 37, the "astringency" of grapes when the number of integral values in a predetermined potential range within -1362 mV to -2501 mV is 19, and the "astringency" of grapes when the number of integral values in a predetermined potential range within -1362 mV to -2501 mV is 9 are equal to each other.

[0641] For the same reason, even when the number of integral values in a predetermined potential range within -1362 mV to -2501 mV is 25, 15, 13, 11, and 7, the "astringency" of grapes is equal to each other.

[0642] Therefore, the diagnostic device 2 may perform taste diagnosis of "grapes" by the above-described method based on an integral value spectrum in which the number of integral values in a predetermined potential range within -1362 mV to -2501 mV is any one 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. 1.

[0644] Referring to FIG. 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 scanning speed V r_Low , V r_Middle , V r_High input to the measuring instrument 12 by the user of the sensor device 1 (step S1).

[0645] Also, the supply unit 121 of the sensor device 1 receives the start signal input to the measuring instrument 12 by the user of the sensor device 1, and the measurement unit 122 of the sensor device 1 receives the 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 applies the potential V within the potential scan range V_s to the solution (the analyte) while changing the potential to be applied to the solution at each of the scan rates V r_Low , V r_Middle , V r_High . The measurement unit 122 measures the potential V of the working electrode 112 with reference to the potential of the reference electrode 114, and measures the current value I from the counter electrode 113 to measure the current-potential characteristics [I-V]_Low, [I-V]_Middle, [I-V]_High of the cyclic voltammogram (step S2).

[0647] In this case, the supply unit 121 applies the potential V within the potential scan range V_s to the solution while changing the potential V at the scan rate V r_Low . The measurement unit 122 measures the potential V of the working electrode 112 with reference to the potential of the reference electrode 114, and measures the current value I from the counter electrode 113 to measure the current-potential characteristic [I-V]_Low of the cyclic voltammogram.

[0648] Also, the supply unit 121 applies the potential V within the potential scan range V_s to the solution while changing the potential V at the scan rate V r_Middle . The measurement unit 122 measures the potential V of the working electrode 112 with reference to the potential of the reference electrode 114, and measures the current value I from the counter electrode 113 to measure the current-potential characteristic [I-V]_Middle of the cyclic voltammogram.

[0649] Furthermore, the supply unit 121 applies the potential V within the potential scan range V_s to the solution while changing the potential V at the scan rate V r_High . The measurement unit 122 measures the potential V of the working electrode 112 with reference to the potential of the reference electrode 114, and 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 values I 1_Low ~I d_Lowand potential V 1_Low ~V d_Low the correspondence with ~V, the current value I in the current-potential characteristic [I-V]_Middle 1_Middle ~I d_Middle and potential V 1_Middle ~V d_Middle the correspondence with ~V, and the current value I in the current-potential characteristic [I-V]_High 1_High ~I d_High and potential V 1_High ~V d_High Create measurement data MRS including the correspondence with ~V (step S3).

[0651] Then, the measurement unit 122 determines whether 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 the end signal, it determines not to end the measurement.

[0653] In step S4, when it is determined not to end the measurement of the cyclic voltammogram of the solution, the operation of the sensor device 1 shifts to step S1, and steps S1 to S4 are repeatedly executed until it is determined in step S4 to end the measurement of the cyclic voltammogram of the solution.

[0654] In this case, in step S4, every time it is determined not to end the measurement, the sensor 11 used for the measurement of the cyclic voltammogram is discarded, the sensor 11 not used for the measurement of the cyclic voltammogram is attached to the measuring instrument 12, and the above-described steps S1 to S4 are sequentially executed.

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

[0656] The reception unit 211 of the diagnostic device 2 receives the m measurement data MRS_1 to MRS_m from the transmission unit 123 of the sensor device 1 by wired communication or wireless communication (step S6), and outputs the received m 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 measurement data MRS_1 to MRS_m from the reception unit 211. Then, based on the m measurement data MRS_1 to MRS_m, the control unit 212 creates m analysis data ALY_D 1 ~ALY_D m and updates the m analysis data ALY_D 1 ~ALY_D m to m index data IDX 1 ~IDX m (step S7).

[0658] Then, the control unit 212 outputs the m index data IDX 1 ~IDX m to the diagnostic unit (consisting of the arithmetic unit 216 and the taste diagnostic unit 217).

[0659] The diagnostic unit (consisting of the arithmetic unit 216 and the taste diagnostic unit 217) has m index data IDX 1 ~IDX mIt receives from the control unit 212. And the diagnostic unit (which consists of the arithmetic unit 216 and the taste diagnostic unit 217) receives m pieces of arithmetic data CAL 1 ~IDX m respectively from m pieces of index data IDX 1 ~CAL m detects them, and based on the m pieces of detected arithmetic data CAL 1 ~CAL m diagnoses the taste of m objects to be analyzed respectively (step S8). Thereby, the operation of the diagnostic system 10 ends.

[0660] FIG. 91 is a flowchart for explaining the detailed operation of step S7 in FIG. 90.

[0661] Referring to FIG. 91, after step S6 in FIG. 90, the control unit 212 determines whether it has received a plurality of measurement data from the reception unit 211 (step S71).

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

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

[0664] The arithmetic unit 213 uses the analysis data ALY_D uniIt receives from the control unit 212. Then, the arithmetic unit 213 and the creation unit 215 use the analysis data ALY_D uni to create a curve CUR uni indicating the class dependence of the integral value as an index curve (step S73).

[0665] Then, the creation unit 215 creates an analysis result ALY_RLS uni by associating the identification information ID uni with the calculation data CAL uni and the curve CUR uni = ID uni / CAL uni / CUR uni , and outputs 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 uni from the creation unit 215, and based on the received analysis result ALY_RLS uni , updates the analysis data ALY_D uni to the index data IDX uni (step S74), and stores the updated index data IDX uni in the database 22 instead of the analysis data ALY_D uni .

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

[0668] The arithmetic unit 213 receives P pieces of analysis data ALY_D 1 ~ALY_D P from the control unit 212. Then, the arithmetic unit 213 and the creation unit 215 use the P pieces of analysis data ALY_D 1 ~ALY_D P to create P curves CUR 1 ~CUR P indicating the class dependence of the integral values as the P index curves of the P objects to be analyzed (step S76).

[0669] Then, the determination unit 214 creates a determination result (the determination result shown in Table 1) indicating whether the P curves CUR 1 ~CUR P are different (step S77), and outputs the created determination result (the determination result shown in Table 1) to the creation unit 215.

[0670] When the creation unit 215 receives the determination result (the determination result shown in Table 1) from the determination unit 214, it adds the P curves CUR 1 =[ID 1 / CAL 1 ~CAL_RLS P =[ID P / CAL P to the P calculation results CAL_RLS 1 ~CUR P respectively to create P analysis results ALY_RLS 1 =[ID 1 / CAL 1 / CUR 1 ~ALY_RLS P =[ID P / CAL P / CUR P . Then, the creation unit 215 outputs the P analysis results ALY_RLS 1 ~ALY_RLS P and the determination result (the determination result shown in Table 1) to the control unit 212.

[0671] Then, when the control unit 212 receives the P analysis results ALY_RLS 1 ~ALY_RLS P and the determination result (the determination result shown in Table 1) from the creation unit 215, based on the P analysis results ALY_RLS 1 ~ALY_RLS P it updates each of the P analysis data ALY_D 1 ~ALY_D P to the P index data IDX 1 ~IDX P (step S78), and stores the updated P index data IDX 1 ~IDX P in the database 22 instead of the P analysis data ALY_D 1 ~ALY_D P respectively.

[0672] In this case, the control unit 212 detects the identification information ID p (where p is any one of 1 to P), the calculation data CAL p and the curve CUR p from the analysis result ALY_RLS p , reads out the analysis data ALY_D p having the same identification information as the detected identification information ID p from the database 22, and adds the calculation data CAL p and the curve CUR p to the read analysis data ALY_D p to update the analysis data ALY_D p to the index data IDX p for all of the P analysis data ALY_D 1 ~ALY_D P .

[0673] As a result, each of the P analysis data ALY_D 1 ~ALY_D P is updated to the P index data IDX 1 ~IDX P respectively.

[0674] Then, the control unit 212 stores the P index data IDX 1 ~IDX P in the database 22 instead of the P analysis data ALY_D 1 ~ALY_D P respectively, and stores the determination result (the determination result shown in Table 1) in the database 22 in association with the P index data IDX 1 ~IDX P .

[0675] Then, after step S74 or step S78, the operation of the diagnostic device 2 proceeds to step S8 in FIG. 90.

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

[0677] Therefore, the diagnostic device 2 can create the curve CUR uni (or the P curves CUR 1 ~CUR P ) as the index curve that is the index for identifying the object to be analyzed.

[0678] FIG. 92 is a flowchart for explaining the detailed operation of step S73 in FIG. 91.

[0679] In FIG. 92, the scanning speed V r of the potential is represented by "S". And "S = 1" represents the scanning speed V r_Low of the potential, "S = 2" represents the scanning speed V r_Middle of the potential, and "S = 3" represents the scanning speed V r_Highshall represent.

[0680] Referring to FIG. 92, after step S72 of FIG. 91, the arithmetic unit 213 receives one piece of analysis data ALY_D uni from the control unit 212 (step S731).

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

[0682] After step S733, the arithmetic unit 213 uni detects N combinations (Iox_1_k_S, Ird_1_k_S) to (Iox_N_k_S, Ird_N_k_S) of N current values {Iox_1_k_S to Iox_N_k_S} of the oxidation wave and N current values {Ird_1_k_S to Ird_N_k_S} of the reduction wave in a predetermined potential range V S -V S ) uni from the current-potential characteristic (I k ) (step S734).

[0683] Here, N represents the total number of unit potentials (for example, 1 mV) in one predetermined potential range V k .

[0684] Also, for example, when one predetermined potential range V k is [0 to 100 mV], the current-potential characteristic (I S -V S ) uni includes the current value I 0→100 when the potential V is scanned from 0 mV to 100 mV and the current value I 100→0 when the potential V is scanned from 100 mV to 0 mV. Therefore, the arithmetic unit 213 detects the current value I 0→100 when the potential V is scanned from 0 mV to 100 mV as [N current values {Iox_1_k_S to Iox_N_k_S} of the oxidation wave], and the current value I 100→0Detect them as [N current values of the reduction wave {Ird_1_k_S to Ird_N_k_S}], and detect N combinations (Iox_1_k_S, Ird_1_k_S) to (Iox_N_k_S, Ird_N_k_S). For one predetermined potential range V k is the same even when it is outside [0 to 100 mV].

[0685] After step S734, the arithmetic unit 23 sets n uip = 1 (step S735). Here, n uip is an argument representing each of the N unit potentials in one predetermined potential range V k

[0686] After step S735, the arithmetic unit 213 subtracts the current value of the reduction wave (Ird_n uip _k_S) from the current value of the oxidation wave (Iox_n uip _k_S) and calculates the subtraction result (R_sbt_n uip _k_S) (step S736).

[0687] Then, the arithmetic unit 213 determines whether n uip = N (step S737).

[0688] In step S737, when it is determined that n uip ≠ N, the arithmetic unit 213 sets n uip = n uip + 1 (step S738). Thereafter, the operation of the diagnostic device 2 proceeds to step S736, and in step S737, steps S736 to S738 are repeatedly executed until it is determined that n uip = N

[0689] And in step S737, when it is determined that n uip = N, the arithmetic unit 213 adds the N subtraction results (R_sbt_1_k_S to R_sbt_N_k_S) to obtain the integral value ITG k in the predetermined potential range V k_S ​Calculate it (Step S739).

[0690] After that, the arithmetic unit 213 sets a predetermined potential range V k as a class Cls_k_S, and creates a combination (Cls_k_S, ITG k_S ) with the integral value ITG k_S (Step S740).

[0691] Then, the arithmetic unit 213 determines whether k = n (Step S741). Here, n is the total number of the predetermined potential ranges V k .

[0692] When it is determined in Step S741 that k ≠ n, the arithmetic unit 213 sets k = k + 1 (Step S742). After that, 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 = n.

[0693] And when it is determined in Step S741 that k = n, the arithmetic unit 213 generates n combinations (Cls_1_S, ITG 1_S ) to (Cls_n_S, ITG n_S ) (Step S743).

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

[0695] When it is determined in Step S744 that S ≠ 3, the arithmetic unit 213 sets S = S + 1 (Step S745). After that, the operation of the diagnostic device 2 proceeds to Step S733. And Steps S733 to S745 are repeatedly executed until it is determined in Step S744 that S = 3.

[0696] And when it is determined in Step S744 that S = 3, the arithmetic unit 213 determines three integral values ITG in the class Cls_11_1 , ITG 1_2 , ITG 1_3 Add them to calculate the total integral value ITG 1_1 + ITG 1_2 + ITG 1_3 Calculate, and for the three integral values ITG in class Cls_2 2_1 , ITG 2_2 , ITG 2_3 Add them to calculate the total integral value ITG 2_1 + ITG 2_2 + ITG 2_3 Calculate, and then, in the same way, for the three integral values ITG in class Cls_n n_1 , ITG n_2 , ITG n_3 Add them to calculate the total integral value ITG n_1 + ITG n_2 + ITG n_3 Calculate.

[0697] Then, the arithmetic unit 213 generates 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 ) (step S746).

[0698] And then, the arithmetic unit 213 creates arithmetic data CAL consisting of 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 ), and outputs the arithmetic result CAL_RLS = [identification information ID / arithmetic data CAL] with the arithmetic data CAL associated with the identification information ID of the object to be analyzed 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 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 ) and detect the 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 generate 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 flow chart shown in FIG. 92, the calculation unit 213 calculates the potential scanning speed V r_Low Based on the cyclic voltammogram measured by the , n integral values ​​ITG in n classes Cls_1_1 to Cls_n_1 are 1_1 ~ITG n_1 Calculate 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 by the , n integral values ​​in n classes Cls_1_2 to Cls_n_2 are calculated. 1_2 ~ITG n_2 Calculate the n combinations of class and integral value (Cls_1_2, ITG1_2 ) to (Cls_n_2, ITG n_2 ) is created, and the scanning speed V of the potential r_High Based on the cyclic voltammogram measured at, n integral values ITG in n classes Cls_1_3 to Cls_n_3 1_3 ~ITG n_3 are calculated to create n combinations of class and integral value (Cls_1_3, ITG 1_3 ) to (Cls_n_3, ITG n_3 ).

[0702] Then, the arithmetic unit 213 uses the n combinations (Cls_1_1, ITG 1_1 ) to (Cls_n_1, ITG n_1 ), the n combinations (Cls_1_2, ITG 1_2 ) to (Cls_n_2, ITG n_2 ) and the n combinations (Cls_1_3, ITG 1_3 ) to (Cls_n_3, ITG n_3 ) to calculate the sum (ITG k ) of the three integral values ITG k_1 , ITG k_2 , ITG k_3 in one class k (=predetermined potential range V k_1 + ITG k_2 + ITG k_3 ) for all of the n classes, and calculates n sums (ITG 1_1 + ITG 1_2 + ITG 1_3 ) to (ITG n_1 + ITG n_2 + ITG n_3 ).

[0703] Then, the arithmetic unit 213 uses the n classes Cls_1 to Cls_n and the n sums (ITG 1_1 + ITG 1_2 + ITG 1_3 ) to (ITG n_1 + ITG n_2 + ITG n_3 ) to create n combinations (Cls_1, ITG 1_1 + ITG 1_2 + ITG1_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 n generated 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 ) are output to the creation unit 215.

[0704] The creation unit 215 receives the 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 plots the received 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 create a curve CUR showing the class dependence of the integral value (see step S747).

[0705] FIG. 93 is a flowchart for explaining the detailed operation of step S72 in FIG. 91.

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

[0707] Then, the control unit 212 detects from the measurement data MRS_uni the name ALY_Na of the analysis object, the type ALY_Kd of the analysis object, the scanning speed V of the potential, uni uni r_Low_uni , V r_Middle_uni , V r_High_uni and the current-potential characteristics (I Low -V Low ) uni , (I Middle -V Middle ) uni , (I High -V High ) uni (step S723).

[0708] Then, the control unit 212 creates measurement data MRS_Low_uni in which the scanning speed V of the potential is associated with the current-potential characteristics (I r_Low_uni -V Low ) Low ) uni (step S724).

[0709] Also, the control unit 212 creates measurement data MRS_Middle_uni in which the scanning speed V of the potential is associated with the current-potential characteristics (I r_Middle_uni -V Middle ) Middle ) uni (step S725).

[0710] Furthermore, the control unit 212 creates measurement data MRS_High_uni in which the scanning speed V of the potential is associated with the current-potential characteristics (I r_High_uni -V High ​​​​​High ) uni Create measurement data MRS_High_uni associated with them (step S726).

[0711] Then, the control unit 212, at time t uni , identification information ID uni , name of the analysis object ALY_Na uni , type of the analysis object ALY_Kd uni and create 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] (step S727).

[0712] After step S727, the operation of the diagnostic device 2 proceeds to step S73 in FIG. 91.

[0713] FIG. 94 is a flowchart for explaining the detailed operation of step S75 in FIG. 91.

[0714] Referring to FIG. 94, in step S71 of FIG. 91, when it is determined that a plurality of measurement data have been received, the control unit 212 of the diagnostic device 2 has received P pieces of measurement data MRS_1 to MRS_P from the reception unit 211. Then, the control unit 212 sets p = 1 (step S751). Here, 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 detects the time t p when the measurement data MRS_p is received (step S752), and issues identification information ID p for identifying the measurement data MRS_p (step S753).

[0716] Then, the control unit 212 determines the name ALY_Na of the analyte, p the type ALY_Kd of the analyte, p the scanning speed V of the potential, r_Low_p , V r_Middle_p , V r_High_p and the current-potential characteristics (I Low -V Low ) p , (I Middle -V Middle ) p , (I High -V High ) p from the measurement data MRS_p (step S754).

[0717] Then, the control unit 212 creates measurement data MRS_Low_p in which the scanning speed V of the potential is associated with the current-potential characteristics (I r_Low_p -V Low -V Low ) p (step S755).

[0718] Also, the control unit 212 creates measurement data MRS_Middle_p in which the scanning speed V of the potential is associated with the current-potential characteristics (I r_Middle_p -V Middle -V Middle ) p (step S756).

[0719] Furthermore, the control unit 212 creates measurement data MRS_High_p in which the scanning speed V of the potential is associated with the current-potential characteristics (I r_High_p -V High -V High ) p (step S757).

[0720] Then, the control unit 212 determines the time t p , the identification information ID p , the name ALY_Na of the analyte, p , the type ALY_Kd of the analyte, pAnd analysis data ALY_D in which measurement data MRS_Low_p, MRS_Middle_p, and MRS_High_p are mutually 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] And the control unit 212 determines whether p = P (step S759).

[0722] In step S759, when it is determined that p ≠ P, the control unit 212 sets p = p + 1 (step S760). Thereafter, the operation of the diagnostic device 2 shifts to step S752, and steps S752 to S760 are repeatedly executed until it is determined that p = P in step S759.

[0723] ...

Claims

1. a first arithmetic unit that calculates a sum (L_sum) of the first integral values ​​in a plurality of predetermined potential intervals based on a plurality of first integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a first cyclic voltammogram measured while changing a potential at a first potential scanning rate, calculates a sum (M_sum) of the second integral values ​​in the plurality of predetermined potential intervals 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 a potential at a second potential scanning rate faster than the first potential scanning rate, and calculates a sum (H_sum) of the third integral values ​​in the plurality of predetermined potential intervals based on a plurality of third integral values ​​in the plurality of predetermined potential intervals calculated using a current-potential characteristic of a third cyclic voltammogram measured while changing a potential at a third potential scanning rate faster than the second potential scanning rate; A diagnostic device comprising: a taste diagnostic unit that diagnoses the taste of a first analyzed object based on the sum (L_sum) of the first integral values, the sum (M_sum) of the second integral values, and the sum (H_sum) of the third integral values.

2. The first arithmetic unit calculates, based on the plurality of first integral values, a first sum (L(+)_sum) which is a sum of the first integral values ​​in the positive predetermined potential interval and a fifth sum (L(all)_sum) which is a sum of the first integral values ​​in all of the predetermined potential intervals as a sum of the first integral values ​​(L_sum); and, based on the plurality of second integral values, calculates a second sum (M(+)_sum) which is a sum of the second integral values ​​in the positive predetermined potential interval and a fifth sum (L(all)_sum) which is a sum of the second integral values ​​in all of the predetermined potential intervals as a sum of the first integral values ​​(L_sum). a sixth sum (M(all)_sum) which is a sum of the third integral values, and a sixth sum (M(all)_sum) which is a sum of the second integral values, are calculated as the sum (M_sum) of the second integral values, and based on the plurality of third integral values, a third sum (H(+)_sum) which is a sum of the third integral values ​​in the positive predetermined potential section, a fourth sum (H(-)_sum) which is a sum of the third integral values ​​in the negative predetermined potential section, and a seventh sum (H(all)_sum) which is the third integral value in all of the predetermined potential sections are calculated as the sum (H_sum) of the third integral values; 2. The diagnostic device of claim 1, wherein the taste diagnosis unit diagnoses the taste of the 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).

3. 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); 3. The diagnostic device according to claim 2, 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.

4. 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 analysis object, 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 4. The diagnostic device according to claim 3, 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.

5. The taste diagnostic unit comprises: A regression equation is obtained by performing a regression analysis using the first factor (Body index (+)) as an explanatory variable and the "astringency" as a response variable, and the value obtained by multiplying the first factor (Body index (+)) in the obtained regression equation is defined as the coefficient k 1 is determined as the value of A regression equation is obtained by performing a regression analysis using the second factor (Body index (all)) as an explanatory variable and the "aftertaste" as a response variable, and a value obtained by multiplying the second factor (Body index (all)) which is an explanatory variable in the obtained regression equation is defined as the coefficient k 2 The value of is determined as A regression equation is obtained by executing 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 obtained regression equation is defined as the coefficient k 3 The coefficient k is determined by multiplying the explanatory variable (=the third sum (H(+)_sum)). 4 is determined as the value of A regression equation is obtained 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 obtained regression equation is defined as the coefficient k 5 The coefficient k is determined by multiplying the explanatory variable (the fourth sum (H(-)_sum)). 6 is determined as the value of A regression equation is obtained by performing a regression analysis with the "astringency" and the "sweetness" as explanatory variables and the "bitterness" as a response variable, and the value multiplied by the "astringency" in the obtained regression equation is defined as the coefficient k 7 The value multiplied by the "sweetness" is the coefficient k 8 The diagnostic device according to claim 4, wherein the value of

6. 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, 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 diagnostic device according to claim 4, wherein the "astringency", the "aftertaste", the "sweetness", the "aroma" and the "bitterness" of the first analyte are diagnosed using the values ​​of

7. 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) of the first integral values, 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 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) second integral values, The 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 3. The diagnostic device according to claim 2, wherein a is any one of a number of third integral values ​​(a, b is an integer equal to or greater than 2).

8. 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 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, calculating 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 the current-potential characteristic of a 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(-)_sum_th), the ninth sum (M(-)_sum_th) and the tenth sum (H(-)_sum_th); 3. The diagnostic device according to claim 2, wherein said taste diagnostic unit further diagnoses the "astringency" of said second analyte based on said third factor (Body index (-)_th).

9. The taste diagnostic unit adds a coefficient k 9 9. The diagnostic device according to claim 8, wherein the multiplication result is diagnosed as "astringency" of the second analysis object.

10. 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 9, wherein the value of

11. 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 diagnostic device according to claim 9, wherein the "astringency" of the second analyte is diagnosed using a value of

12. The sum of the first integral values ​​in the plurality of negative potential sections consisting of negative potentials equal to or less than 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) of the first integral values, 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 9. The diagnostic device according to claim 8, wherein a is a sum of a number of third integral values ​​(a, b is an integer equal to or greater than 2).

13. a second arithmetic unit that 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, 3. The diagnostic device according to claim 2, 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.

14. a creating unit that creates a curve showing the dependency of the plurality of total integral values ​​on the plurality of predetermined potential sections based on the plurality of predetermined potential sections and a plurality of total integral values ​​respectively corresponding to the plurality of predetermined potential sections as a feature amount of the first analysis object or the second analysis object, The diagnostic device according to claim 13, wherein the second calculation unit further calculates a total integral value, which is a sum of the first integral value, the second integral value, and the third integral value in one of the specified potential sections, for all of the multiple specified potential sections to calculate the multiple total integral values, and outputs the multiple specified potential sections and the multiple total integral values ​​respectively corresponding to the multiple specified potential sections to the creation unit.

15. the calculation data includes the plurality of predetermined potential sections and the plurality of total integral values ​​respectively associated with the plurality of predetermined potential sections, a judgment unit that judges whether or not P pieces of the calculation data (P is an integer equal to or greater than 2) of the plurality of sum integral values ​​are different from each other; The diagnostic device according to claim 14 , wherein the creation unit creates the P curves when the determination unit determines that the P plurality of total integral values ​​are different from one another.

16. A diagnostic system comprising the diagnostic device according to any one of claims 1 to 15.

17. a first calculation unit calculating a sum (L_sum) of the first integral values ​​in a plurality of predetermined potential intervals based on a plurality of first integral values ​​in the 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, calculating a sum (M_sum) of the second integral values ​​in the plurality of predetermined potential intervals 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 calculating a sum (H_sum) of the third integral values ​​in the plurality of predetermined potential intervals based on a plurality of third 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 program for causing a computer to execute a taste diagnosis unit to diagnose the taste of the first analyzed object based on the sum of the first integral values ​​(L_sum), the sum of the second integral values ​​(M_sum) and the sum of the third integral values ​​(H_sum).

18. In the first step, the first arithmetic unit calculates, based on the plurality of first integral values, a first sum (L(+)_sum) which is a sum of the first integral values ​​in the positive predetermined potential interval and a fifth sum (L(all)_sum) which is a sum of the first integral values ​​in all of the predetermined potential intervals as the sum of the first integral values ​​(L_sum), and calculates, based on the plurality of second integral values, a second sum (M(+)_sum) which is a sum of the second integral values ​​in the positive predetermined potential interval and a fifth sum (M(all)_sum) which is a sum of the second integral values ​​in all of the predetermined potential intervals as the sum of the first integral values ​​(L_sum). a sixth sum (M(all)_sum) which is a sum of the second integral values ​​in the predetermined potential interval of positive potential, a fourth sum (H(-)_sum) which is a sum of the third integral values ​​in the predetermined potential interval of negative potential, and a seventh sum (H(all)_sum) which is the third integral value in all of the predetermined potential intervals are calculated as the sum (H_sum) of the third integral values, based on the plurality of third integral values; 18. The program for causing a computer to execute the program of claim 17, wherein in the second step, 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).

19. The first arithmetic unit, in the first step, 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); 19. The program for causing a computer to execute the program of claim 18, 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.

20. 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 analysis object, 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 20. The program for causing a computer to execute the program according to claim 19, wherein the program diagnoses a subtraction result obtained by multiplying the first analyte by the first concentration and subtracting the multiplication result as the "bitterness" of the first analyte.

21. The taste diagnostic unit, in the second step, A regression equation is obtained by performing a regression analysis using the first factor (Body index (+)) as an explanatory variable and the "astringency" as a response variable, and the value obtained by multiplying the first factor (Body index (+)) in the obtained regression equation is defined as the coefficient k 1 is determined as the value of A regression equation is obtained by performing a regression analysis using the second factor (Body index (all)) as an explanatory variable and the "aftertaste" as a response variable, and a value obtained by multiplying the second factor (Body index (all)) which is an explanatory variable in the obtained regression equation is defined as the coefficient k 2 is determined as the value of A regression equation is obtained by executing 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 obtained regression equation is defined as the coefficient k 3 The coefficient k is determined by multiplying the explanatory variable (=the third sum (H(+)_sum)). 4 is determined as the value of A regression equation is obtained 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 obtained regression equation is defined as the coefficient k 5 The coefficient k is determined by multiplying the explanatory variable (the fourth sum (H(-)_sum)). 6 is determined as the value of A regression equation is obtained by performing a regression analysis with the "astringency" and the "sweetness" as explanatory variables and the "bitterness" as a response variable, and the value multiplied by the "astringency" in the obtained regression equation is defined as the coefficient k 7 and determining the value multiplied by the "sweetness" as the value of the coefficient k8.

22. 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 20, wherein the "astringency", the "aftertaste", the "sweetness", the "aroma" and the "bitterness" of the first analysis object are diagnosed using the values ​​of

23. 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) of the first integral values, 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 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) second integral values, The 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 19. The program for causing a computer to execute the program according to claim 18, wherein a is any one of a number of third integral values ​​(a, b is an integer equal to or greater than 2).

24. The first arithmetic unit, in the first step, 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 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. calculate 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 the current-potential characteristic of the third cyclic voltammogram, and calculate 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(-)_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 analysis object based on the third factor (Body index (-)_th) in the second step.

25. In the second step, the taste diagnostic unit adds a coefficient k 9 and diagnosing the multiplication result as being "astringency" of the second analysis object.

26. 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 defines a value multiplied by 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 25, wherein the program product is determined as a value of

27. 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 25, wherein the "astringency" of the second analyte is diagnosed using the value of

28. The sum of the first integral values ​​in the plurality of negative potential sections consisting of negative potentials equal to or less than 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) of the first integral values, 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 25. The program for causing a computer to execute the program according to claim 24, wherein the program is any one of sums of a number of third integral values ​​(a, b is an integer of 2 or more).

29. a second arithmetic unit calculates the first integral values ​​in the predetermined potential intervals based on the current-potential characteristic of the first cyclic voltammogram, calculates the second integral values ​​in the predetermined potential intervals based on the current-potential characteristic of the second cyclic voltammogram, and calculates the third integral values ​​in the predetermined potential intervals based on the current-potential characteristic of the third cyclic voltammogram; 19. The program for causing a computer to execute the program of claim 18, 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.

30. a fourth step in which a creating unit creates a curve showing the dependency of the plurality of total integral values ​​on the plurality of predetermined potential sections based on the plurality of predetermined potential sections and a plurality of total integral values ​​respectively corresponding to the plurality of predetermined potential sections, as a feature amount of the first analysis object or the second analysis object; 30. A program for causing a computer to execute the program described in claim 29, wherein in the third step, the second calculation unit further calculates the total integral value, which is the sum of the first integral value, the second integral value, and the third integral value in one of the specified potential sections, for all of the multiple specified potential sections to calculate the multiple total integral values, and outputs the multiple specified potential sections and the multiple total integral values ​​respectively corresponding to the multiple specified potential sections to the creation unit.

31. the calculation data includes the plurality of predetermined potential sections and the plurality of total integral values ​​respectively associated with the plurality of predetermined potential sections, a fifth step in which a determination unit determines whether or not P pieces of the operation data (P is an integer equal to or greater than 2) of the plurality of sum integral values ​​are different from each other; 31. The program for causing a computer to execute the program according to claim 30, wherein the creation unit creates the P curves in the fourth step when the judgment unit judges in the fifth step that the P [plurality of total integral values] are different from each other.

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