Sensor, analysis device, terminal device, analysis system using these, and program for executing on a computer

Abstract

The sensor 11 is used for measuring a cyclic voltammogram of an analysis object made of a liquid, is detachable from a cyclic voltammogram measuring device, and is discarded every time the measurement of the cyclic voltammogram is performed. The sensor 11 includes a substrate 111 having a flat plate shape, a working electrode 112, a counter electrode 113, a reference electrode 114, and wirings 115 to 117. The wirings 115 to 117 are disposed on one surface of the substrate 111. The working electrode 112 is disposed on one surface of the substrate 111 and is electrically connected to one end of the wiring 115. The counter electrode 113 is disposed on one surface of the substrate 111 and is electrically connected to one end of the wiring 116. The reference electrode 114 is disposed on one surface of the substrate 111 and is electrically connected to one end of the wiring 117. The planar shape of the working electrode 112 is circular or rectangular.

Description

[Technical Field]

[0001] The present invention relates to a sensor, an analysis device, a terminal device, an analysis system using these, and a program to be executed by a computer. [Background technology]

[0002] In the technology for analyzing solutions of soft drinks, alcoholic beverages, tap water, urine, blood, and the like, analytical methods such as Fourier Transform Infrared Spectroscopy (FTIR), gas chromatography, taste sensors, and Raman spectroscopy are used (Non-Patent Documents 1 to 3).

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

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

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

[0006] [Non-Patent Document 1] H. Yu, Y. Zhang, J. Zhao, and H. Tian, ​​“Taste characteristics of Chinese bayberry juice characterized by sensory evaluation, chromatography analysis, and an electronic tongue,” J Food Sci Technol 55(5), 1624-1631 (2018). [Non-patent document 2] GF Abreu, FM Borem, LFC Oliveira, MR Almeida, and APC Alves, “Raman spectroscopy: A new strategy for monitoring the quality of green coffee beans during storage,” Food Chem 287, 241-248 (2019). [Non-patent document 3] R. Ferrer-Gallego, JM Hernandez-Hierro, JC Rivas-Gonzalo, and MT Escribano-Bailon, “Evaluation of sensory parameters of grapes using near infrared spectroscopy,” J Food Eng 118(3), 333-339 (2013). Summary of the Invention [Problem to be solved by the invention]

[0007] However, because electrochemical sensor systems must detect "specific chemical substances" selectively and with high sensitivity, careful sample evaluation is required, including sample pretreatment, solvent extraction, and pH adjustment using a buffer solution.

[0008] Furthermore, the surface of the working electrode used in the electrochemical sensor becomes contaminated with organic matter and radicals after repeated use, resulting in a decrease in signal strength, and therefore the electrode must be regenerated by physical polishing or the like.

[0009] Therefore, according to the embodiment of the present invention, a sensor is provided that does not require electrode regeneration by physical polishing or other such processes and does not require sample pretreatment.

[0010] Furthermore, according to an embodiment of the present invention, an analytical device is provided that can identify an object to be analyzed based on characteristics measured using a sensor that does not require electrode regeneration by physical polishing or other such processes or sample pretreatment.

[0011] Furthermore, according to an embodiment of the present invention, a terminal device is provided that can obtain indicators for identifying an object to be analyzed based on characteristics measured using a sensor that does not require electrode regeneration by physical polishing or other such means or sample pretreatment.

[0012] Furthermore, according to an embodiment of the present invention, an analysis system is provided that can identify an object to be analyzed based on characteristics measured using a sensor that does not require electrode regeneration by physical polishing or other such processes or sample pretreatment.

[0013] Furthermore, according to an embodiment of the present invention, a program is provided for causing a computer to execute the creation of an index when identifying an object to be analyzed based on characteristics measured using a sensor that does not require electrode regeneration by physical polishing or other methods or sample pretreatment. [Means for solving the problem]

[0014] (Configuration 1) According to an embodiment of the present invention, the sensor is used to measure a cyclic voltammogram of an analyte made of a liquid, is attachable to and detachable from a measuring instrument that measures the cyclic voltammogram, and is discarded after each cyclic voltammogram measurement. The sensor includes a substrate, a first wire, a second wire, a third wire, a working electrode, a reference electrode, and a counter electrode. The substrate has a flat plate shape. The first wire is arranged on one side of the substrate along a first direction. The second wire is arranged on one side of the substrate along the first direction, with a predetermined gap between them in a second direction perpendicular to the first direction. The third wire is arranged on one side of the substrate along the first direction, with a predetermined gap between them in the second direction. The working electrode is an electrode that is arranged on one side of the substrate, is electrically connected to one end of the first wire, and exchanges electrons with the analyte. The reference electrode is disposed on one side of the substrate, electrically connected to one end of the second wire, and serves as a reference for determining the potential of the working electrode. The counter electrode is disposed on one side of the substrate, electrically connected to one end of the third wire, and serves as an electrode for returning to the system a current value equal to the current value generated at the working electrode. The first wire, second wire, and third wire are electrically connected to the measuring instrument when the other end of the substrate in the first direction is inserted into the recess of the measuring instrument. The counter electrode is disposed between the working electrode and the reference electrode in the second direction. The working electrode has a circular or rectangular planar shape.

[0015] (Configuration 2) According to an embodiment of the present invention, an analytical device creates an index curve, which is a curve that serves as an index for identifying an analyte made of a liquid, based on the current-potential characteristics of a cyclic voltammogram of the analyte, measured using cyclic voltammetry, and includes a calculation unit and a creation unit. The calculation unit executes a calculation process to calculate an integral value in a predetermined potential interval of the current-potential characteristics for all predetermined potential intervals based on the current-potential characteristics, thereby calculating a plurality of integral values ​​in the plurality of predetermined potential intervals. The creation unit creates, as the index curve, a curve that indicates the dependency of the integral value on the predetermined potential interval, based on the plurality of integral values ​​in the plurality of predetermined potential intervals calculated by the calculation unit.

[0016] (Configuration 3) In configuration 2, the calculation unit calculates the area of ​​the cyclic voltammogram in one predetermined potential interval for all of the plurality of predetermined potential intervals in the calculation process, thereby calculating a plurality of integral values.

[0017] (Configuration 4) In configuration 3, the calculation unit performs a subtraction process in the calculation process to subtract 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 section, thereby calculating the intensity of the cyclic voltammogram at one unit potential, for all unit potentials in one predetermined potential section, thereby calculating multiple intensities in one predetermined potential section, and then calculates the sum of the multiple calculated intensities as the area of ​​the cyclic voltammogram in one predetermined potential section.

[0018] (Configuration 5) In configuration 4, the calculation unit calculates the intensity of the cyclic voltammogram at one unit potential in one predetermined potential section 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 the calculation process.

[0019] (Configuration 6) In configuration 5, when the current value of the reduction wave at one unit potential is greater than the current value of the oxidation wave at one unit potential in the calculation process, the calculation unit calculates an intensity consisting of a negative value as the intensity of the cyclic voltammogram at one unit potential.

[0020] (Configuration 7) In configuration 2, the analysis device further includes a determination unit. When the calculation unit calculates multiple integral values ​​in multiple predetermined potential intervals for each of P (P is an integer equal to or greater than 2) analysis objects in calculation processing, the determination unit determines whether the P [multiple integral values] differ from each other. When the determination unit determines that the P [multiple integral values] differ from each other, the creation unit creates P curves based on the P [multiple integral values].

[0021] (Configuration 8) In the configuration 7, Z (Z is the number of combinations of two [multiple integral values] when extracting two [multiple integral values] from P [multiple integral values]) P C2. When extracting two [multiple integral values] of Z sets, two [multiple integral values] of Z sets are extracted as two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] (where n represents the total number of predetermined potential sections, and i≠j), The decision unit is a set of two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] contains two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] for all combinations of two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j] are determined to be different, then P [plurality of integral values] are determined to be different from each other.

[0022] (Configuration 9) In configuration 8, [n integral values ​​ITG 1_i ~ITG n_i ] are n classes Cls1 to Cls n and the n integrals ITG 1_j ~ITG n_j ] are n classes Cls1 to Cls n It is associated with The decision unit is composed of two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] based on one class Cls k (k is any value from 1 to n) k_i and the integral value ITG k_j Difference DF k Calculating n classes Cls1 to Cls n DF1 to DF2 n Calculate the n calculated differences DF1 to DF n When it is determined that the standard deviation of is greater than the threshold, the two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] is determined to be different.

[0023] (Configuration 10) In configuration 7, when the P analysis objects have P names that are different from one another, and two analysis objects with different names among the P analysis objects are designated as the first and second analysis objects, and two analysis objects that are included in the first analysis object and are different in type from one another are designated as the third and fourth analysis objects, The judgment unit judges that the [multiple integral values] for the first analysis object and the [multiple integral values] for the second analysis object differ from each other when a first standard deviation, which is the standard deviation of the difference between the [multiple integral values] for the first analysis object and the [multiple integral values] for the second analysis object, is greater than a first threshold value, and judges that the [multiple integral values] for the third analysis object and the [multiple integral values] for the fourth analysis object differ from each other when a second standard deviation, which is the standard deviation of the difference between the [multiple integral values] for the third analysis object and the [multiple integral values] for the fourth analysis object, is greater than a second threshold value that is smaller than the first threshold value.

[0024] (Configuration 11) In configuration 2, the analysis device further includes a display unit, which displays the curve generated by the generation unit.

[0025] (Configuration 12) Furthermore, according to an embodiment of the present invention, the terminal device includes a receiving unit, a transmitting unit, and a display unit. The receiving unit receives measurement data of a cyclic voltammogram of an analysis object made of a liquid measured using cyclic voltammetry from the sensor device via wired or wireless communication, and receives a curve created based on the measurement data, which shows the dependency of multiple integral values ​​in multiple predetermined potential intervals of the cyclic voltammogram on the predetermined potential intervals, from the analysis device via a network as an "index curve, which is a curve that serves as an index for identifying the analysis object." The transmitting unit transmits analysis data including the current-potential characteristics of the measurement data received by the receiving unit to the analysis device via the network. The display unit displays the curve received by the receiving unit as the index curve.

[0026] (Configuration 13) Furthermore, according to an embodiment of the present invention, an analysis system includes a sensor device and an analysis device. The sensor device includes the sensor according to configuration 1 and a measuring instrument that measures a cyclic voltammogram of an analysis object made of a liquid using the sensor. The analysis device is the analysis device according to any one of configurations 2 to 11.

[0027] (Configuration 14) Furthermore, according to an embodiment of the present invention, an analysis system includes a sensor device, an analysis device, and a terminal device. The sensor device includes the sensor according to configuration 1 and a measuring instrument that measures a cyclic voltammogram of an analysis object made of a liquid using the sensor. The analysis device is the analysis device according to any one of configurations 2 to 11. The terminal device is the terminal device according to configuration 12.

[0028] (Configuration 15) Furthermore, according to an embodiment of the present invention, the program is a program for causing a computer to create an index curve, which is a curve that serves as an index when identifying an analyte made of a liquid, based on the current-potential characteristics of a cyclic voltammogram of the analyte measured using cyclic voltammetry, and includes: a first step in which the calculation unit executes a calculation process in which, based on the current-potential characteristic, an integral value in a predetermined potential section of the current-potential characteristic is calculated for all predetermined potential sections to calculate a plurality of integral values ​​in a plurality of predetermined potential sections; and a second step in which the creation unit creates, as an index curve, a curve indicating the dependency of the integral value on a predetermined potential interval based on the multiple integral values ​​in the multiple predetermined potential intervals calculated in the calculation processing of the first step.

[0029] (Configuration 16) In configuration 15, the calculation unit calculates the area of ​​the cyclic voltammogram in one predetermined potential interval for all of the plurality of predetermined potential intervals in the calculation process of the first step, thereby calculating a plurality of integral values.

[0030] In configuration 16, in the first step of the calculation process, the calculation unit performs a subtraction process to subtract 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 section to calculate the intensity of the cyclic voltammogram at one unit potential, and performs this process for all unit potentials in one predetermined potential section to calculate multiple intensities in one predetermined potential section, and calculates the sum of the multiple calculated intensities as the area of ​​the cyclic voltammogram in one predetermined potential section.

[0031] (Configuration 18) In configuration 17, in the first step of the calculation process, the calculation unit subtracts 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 section to calculate the intensity of the cyclic voltammogram at one unit potential.

[0032] (Configuration 19) In configuration 18, when the current value of the reduction wave at one unit potential is greater than the current value of the oxidation wave at the first step of the calculation process, the calculation unit calculates an intensity consisting of a negative value as the intensity of the cyclic voltammogram at one unit potential.

[0033] (Configuration 20) In configuration 15, the determining unit further causes the computer to execute a third step of determining whether or not the P number of [plurality of integral values] differ from each other when the calculation unit calculates a plurality of integral values ​​in a plurality of predetermined potential intervals for each of P (P is an integer of 2 or more) number of analysis objects in the calculation process of the first step; When the determination unit determines in the third step that the P number of [plurality of integral values] differ from each other, the creation unit creates P number of curves based on the P number of [plurality of integral values] in the second step.

[0034] (Configuration 21) In the configuration 20, Z (Z is the number of combinations of two [multiple integral values] when extracting two [multiple integral values] from P [multiple integral values]) P C2. When extracting two [multiple integral values] of Z sets, two [multiple integral values] of Z sets are extracted as two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] (where n represents the total number of predetermined potential sections, and i≠j), In the third step, the decision unit calculates the Z sets of two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] contains two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] for all combinations of two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] are determined to be different, then P [plurality of integral values] are determined to be different from each other.

[0035] (Configuration 22) In the configuration 21, [n integral values ​​ITG 1_i ~ITG n_i ] are n classes Cls1 to Cls n and the n integrals ITG 1_j ~ITG n_j ] are n classes Cls1 to Cls n It is associated with In the third step, the decision unit calculates the two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] based on one class Cls k(k is any value from 1 to n) k_i and the integral value ITG k_j Difference DF k Calculating n classes Cls1 to Cls n DF1 to DF2 n Calculate the n calculated differences DF1 to DF n When it is determined that the standard deviation of is greater than the threshold, the two [n integral values ​​ITG 1_i ~ITG n_i ],[n integral values ​​ITG 1_j ~ITG n_j ] is determined to be different.

[0036] (Configuration 23) In configuration 20, the P analysis objects have P names that are different from one another, When two of the P analysis objects that have different names are designated as the first and second analysis objects, and two analysis objects that are included in the first analysis object and are different in type are designated as the third and fourth analysis objects, In the third step, the judgment unit judges that the [multiple integral values] for the first analysis object and the [multiple integral values] for the second analysis object differ from each other when the first standard deviation, which is the standard deviation of the difference between the [multiple integral values] for the first analysis object and the [multiple integral values] for the second analysis object, is greater than a first threshold value, and judges that the [multiple integral values] for the third analysis object and the [multiple integral values] for the fourth analysis object differ from each other when the second standard deviation, which is the standard deviation of the difference between the [multiple integral values] for the third analysis object and the [multiple integral values] for the fourth analysis object, is greater than a second threshold value that is smaller than the first threshold value.

[0037] (Configuration 24) In configuration 15, the computer further executes a fourth step in which the display unit displays the curve created by the creation unit. [Effects of the Invention]

[0038] According to the embodiment of the present invention, the cyclic voltammogram of an object to be analyzed, which is a liquid, can be measured using cyclic voltammetry without regenerating the electrodes by physical polishing or pretreating the sample.

[0039] Furthermore, according to an embodiment of the present invention, an index curve can be created that serves as an index when identifying an object to be analyzed based on characteristics measured using a sensor that does not require electrode regeneration by physical polishing or other such procedures or sample pretreatment.

[0040] Furthermore, according to an embodiment of the present invention, an index curve can be obtained that serves as an index when identifying an object to be analyzed based on characteristics measured using a sensor that does not require electrode regeneration by physical polishing or other such procedures or sample pretreatment. [Brief explanation of the drawings]

[0041] [Figure 1] 1 is a schematic diagram of an analysis system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a schematic diagram of the sensor device 1 shown in FIG. [Figure 3] FIG. 3 is a perspective view of the measuring instrument 12 shown in FIG. 2. [Figure 4] FIG. 3 is a schematic diagram of the measuring instrument 12 shown in FIG. 2. [Figure 5] 3 is a schematic diagram showing a timing chart of the potential supplied to the sensor 11 shown in FIG. 2. FIG. [Figure 6] FIG. 1 is a schematic diagram of measurement data MRS. [Figure 7] FIG. 2 is a schematic diagram of the analysis device 2 shown in FIG. [Figure 8] FIG. 10 is a diagram for explaining a method for creating a curve CUR that shows the relationship between a plurality of classes Cls and a plurality of integral values ​​ITG. [Figure 9] FIG. 10 is a first conceptual diagram for explaining a method for calculating an integral value. [Figure 10] FIG. 10 is a second conceptual diagram for explaining a method for calculating an integral value. [Figure 11]FIG. 10 is a diagram illustrating an example of calculation data. [Figure 12] FIG. 1 shows a portion of a cyclic voltammogram. [Figure 13] FIG. 1 shows a cyclic voltammogram of Calpis. [Figure 14] FIG. 1 shows the integrated spectra of undiluted Calpis, Calpis diluted two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water. [Figure 15] FIG. 15 is a diagram showing the results of determining whether or not the curves k1 to k4 shown in FIG. 14 are different from one another. [Figure 16] 1 shows the integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013). [Figure 17] FIG. 17 is a diagram showing the results of determining whether or not the curves k5 to k7 shown in FIG. 16 are different from one another. [Figure 18] This figure shows the integrated spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, age unknown). [Figure 19] FIG. 19 is a diagram showing the results of determining whether or not the curves k8 to k10 shown in FIG. 18 are different from one another. [Figure 20] FIG. 1 shows the integrated spectra for WONDA, Craft Boss, and Gold Brew. [Figure 21] FIG. 21 is a diagram showing the results of determining whether or not the curves k11 to k13 shown in FIG. 20 are different from one another. [Figure 22] FIG. 10 shows integral spectra of human urine collected on different days. [Figure 23] FIG. 23 is a diagram showing the results of determining whether or not the curves k14 to k16 shown in FIG. 22 are different from one another. [Figure 24] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days. [Figure 25] FIG. 25 is a diagram showing the results of determining whether or not the curves k17 to k19 shown in FIG. 24 are different from one another. [Figure 26] FIG. 1 shows two integral spectra for the same wine. [Figure 27] FIG. 27 is a diagram showing the result of determining whether or not the curves k20 and k21 shown in FIG. 26 are different from each other. [Figure 28] FIG. 10 is a diagram showing an integrated spectrum created based on a cyclic voltammogram CVG measured while changing the potential scanning rate. [Figure 29] FIG. 29 is a diagram showing the result of determining whether or not the curves k22, k23, and k24 shown in FIG. 28 are different from one another. [Figure 30] FIG. 1 is a first schematic diagram showing a new index curve when identifying an analyte. [Figure 31] FIG. 31 is a diagram showing the result of determining whether or not the curves k25, k26, and k27 shown in FIG. 30 are different from one another. [Figure 32] FIG. 10 is a second schematic diagram showing a new index curve when identifying an analyte. [Figure 33] FIG. 33 is a diagram showing the result of determining whether or not the curves k28, k29, and k30 shown in FIG. 32 are different from one another. [Figure 34] This figure shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water, when the number of integral values ​​is three. [Figure 35] FIG. 35 is a diagram showing the results of determining whether or not the curves k31 to k34 shown in FIG. 34 are different from one another. [Figure 36] This figure shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water, when the number of integral values ​​is four. [Figure 37] FIG. 37 is a diagram showing the results of determining whether or not the curves k35 to k38 shown in FIG. 36 are different from one another. [Figure 38]This figure shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water, when the number of integral values ​​is 5. [Figure 39] FIG. 39 is a diagram showing the results of determining whether or not the curves k43 to k46 shown in FIG. 38 are different from one another. [Figure 40] This figure shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water, when the number of integral values ​​is 6. [Figure 41] FIG. 41 is a diagram showing the results of determining whether or not the curves k47 to k50 shown in FIG. 40 are different from one another. [Figure 42] This figure shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water, when the number of integral values ​​is 8. [Figure 43] FIG. 43 is a diagram showing the results of determining whether or not the curves k51 to k54 shown in FIG. 42 are different from one another. [Figure 44] This figure shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water, when the number of integral values ​​is 13. [Figure 45] FIG. 45 is a diagram showing the results of determining whether or not the curves k55 to k58 shown in FIG. 44 are different from one another. [Figure 46] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is three. [Figure 47] FIG. 47 is a diagram showing the results of determining whether or not the curves k62 to k64 shown in FIG. 46 are different from one another. [Figure 48]FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is four. [Figure 49] FIG. 49 is a diagram showing the results of determining whether or not the curves k65 to k67 shown in FIG. 48 are different from one another. [Figure 50] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is six. [Figure 51] FIG. 51 is a diagram showing the results of determining whether or not the curves k68 to k70 shown in FIG. 50 are different from one another. [Figure 52] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is eight. [Figure 53] FIG. 53 is a diagram showing the results of determining whether or not the curves k71 to k73 shown in FIG. 52 are different from one another. [Figure 54] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 10. [Figure 55] FIG. 55 is a diagram showing the results of determining whether or not the curves k74 to k76 shown in FIG. 54 are different from one another. [Figure 56] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 14. [Figure 57] FIG. 57 is a diagram showing the results of determining whether or not the curves k77 to k79 shown in FIG. 56 are different from one another. [Figure 58] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 28. [Figure 59] FIG. 59 is a diagram showing the results of determining whether or not the curves k80 to k82 shown in FIG. 58 are different from one another. [Figure 60] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 57. [Figure 61] FIG. 61 is a diagram showing the results of determining whether or not the curves k83 to k85 shown in FIG. 60 are different from one another. [Figure 62] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 95. [Figure 63] FIG. 63 is a diagram showing the results of determining whether or not the curves k86 to k88 shown in FIG. 62 are different from one another. [Figure 64] FIG. 10 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 142. [Figure 65] FIG. 65 is a diagram showing the results of determining whether or not the curves k89 to k91 shown in FIG. 64 are different from one another. [Figure 66] This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is three. [Figure 67] FIG. 67 is a diagram showing the results of determining whether or not the curves k95 to k97 shown in FIG. 66 are different from one another. [Figure 68] This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is four. [Figure 69] FIG. 69 is a diagram showing the results of determining whether or not the curves k98 to k100 shown in FIG. 68 are different from one another. [Figure 70]This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is six. [Figure 71] FIG. 71 is a diagram showing the results of determining whether or not the curves k101 to k103 shown in FIG. 70 are different from one another. [Figure 72] This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is seven. [Figure 73] FIG. 73 is a diagram showing the results of determining whether or not the curves k104 to k106 shown in FIG. 72 are different from one another. [Figure 74] FIG. 10 shows integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italy, year unknown) when the number of integrals is eight. [Figure 75] FIG. 75 is a diagram showing the results of determining whether or not the curves k107 to k109 shown in FIG. 74 are different from one another. [Figure 76] This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 11. [Figure 77] FIG. 77 is a diagram showing the results of determining whether or not the curves k110 to k112 shown in FIG. 76 are different from one another. [Figure 78] FIG. 10 shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 16. [Figure 79] FIG. 79 is a diagram showing the results of determining whether or not the curves k113 to k115 shown in FIG. 78 are different from one another. [Figure 80] This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 19. [Figure 81] FIG. 81 is a diagram showing the results of determining whether or not the curves k116 to k118 shown in FIG. 80 are different from one another. [Figure 82] This figure shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 47. [Figure 83] FIG. 83 is a diagram showing the results of determining whether or not the curves k119 to k121 shown in FIG. 82 are different from one another. [Figure 84] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is three. [Figure 85] FIG. 85 is a diagram showing the results of determining whether or not the curves k125 to k127 shown in FIG. 84 are different from one another. [Figure 86] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is four. [Figure 87] FIG. 87 is a diagram showing the results of determining whether or not the curves k128 to k130 shown in FIG. 86 are different from one another. [Figure 88] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is five. [Figure 89] FIG. 89 is a diagram showing the results of determining whether or not the curves k131 to k133 shown in FIG. 88 are different from one another. [Figure 90] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is six. [Figure 91] FIG. 91 is a diagram showing the results of determining whether or not the curves k134 to k136 shown in FIG. 90 are different from one another. [Figure 92] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is seven. [Figure 93]FIG. 93 is a diagram showing the results of determining whether or not the curves k137 to k139 shown in FIG. 92 are different from one another. [Figure 94] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is eight. [Figure 95] FIG. 95 is a diagram showing the results of determining whether or not the curves k140 to k142 shown in FIG. 94 are different from one another. [Figure 96] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is nine. [Figure 97] FIG. 97 is a diagram showing the results of determining whether or not the curves k143 to k145 shown in FIG. 96 are different from one another. [Figure 98] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 10. [Figure 99] FIG. 99 is a diagram showing the results of determining whether or not the curves k146 to k148 shown in FIG. 98 are different from one another. [Figure 100] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 15. [Figure 101] 101 is a diagram showing the result of determining whether or not the curves k149 to k151 shown in FIG. 100 are different from one another. [Figure 102] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 20. [Figure 103] FIG. 103 is a diagram showing the results of determining whether or not the curves k152 to k154 shown in FIG. 102 are different from one another. [Figure 104] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 39. [Figure 105] FIG. 105 is a diagram showing the results of determining whether or not the curves k155 to k157 shown in FIG. 104 are different from one another. [Figure 106] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 54. [Figure 107] FIG. 115 is a diagram showing the results of determining whether or not the curves k158 to k160 shown in FIG. 114 are different from one another. [Figure 108] FIG. 10 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 135. [Figure 109] FIG. 109 is a diagram showing the results of determining whether or not the curves k161 to k163 shown in FIG. 108 are different from one another. [Figure 110] FIG. 10 is a diagram showing integral spectra of human urine collected on different days when the number of integrals is three. [Figure 111] FIG. 111 is a diagram showing the results of determining whether or not the curves k167 to k169 shown in FIG. 110 are different from one another. [Figure 112] FIG. 10 is a diagram showing integral spectra of human urine collected on different days when the number of integrals is four. [Figure 113] FIG. 113 is a diagram showing the results of determining whether or not the curves k170 to k172 shown in FIG. 112 are different from one another. [Figure 114] FIG. 10 is a diagram showing integral spectra of human urine collected on different days when the number of integrals is five. [Figure 115] FIG. 115 is a diagram showing the results of determining whether or not the curves k173 to k175 shown in FIG. 114 are different from one another. [Figure 116] FIG. 10 is a diagram showing integral spectra of human urine collected on different days when the number of integrals is seven. [Figure 117] FIG. 117 is a diagram showing the results of determining whether or not the curves k176 to k178 shown in FIG. 116 are different from one another. [Figure 118] FIG. 10 is a diagram showing integral spectra of human urine collected on different days when the number of integrals is nine. [Figure 119]FIG. 119 is a diagram showing the results of determining whether or not the curves k179 to k181 shown in FIG. 118 are different from one another. [Figure 120] FIG. 10 is a diagram showing integral spectra of human urine collected on different days when the number of integrals is 13. [Figure 121] FIG. 121 is a diagram showing the results of determining whether or not the curves k182 to k184 shown in FIG. 120 are different from one another. [Figure 122] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is three. [Figure 123] FIG. 123 is a diagram showing the results of determining whether or not the curves k188 to k190 shown in FIG. 122 are different from one another. [Figure 124] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is four. [Figure 125] FIG. 125 is a diagram showing the results of determining whether or not the curves k191 to k193 shown in FIG. 124 are different from one another. [Figure 126] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is five. [Figure 127] FIG. 127 is a diagram showing the results of determining whether or not the curves k194 to k196 shown in FIG. 126 are different from one another. [Figure 128] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is seven. [Figure 129] FIG. 129 is a diagram showing the results of determining whether or not the curves k197 to k199 shown in FIG. 128 are different from one another. [Figure 130] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is nine. [Figure 131] FIG. 131 is a diagram showing the results of determining whether or not the curves k200 to k202 shown in FIG. 130 are different from one another. [Figure 132] FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is 13. [Figure 133] FIG. 133 is a diagram showing the results of determining whether or not the curves k203 to k205 shown in FIG. 132 are different from one another. [Figure 134] FIG. 10 is a diagram showing two integral spectra for the same wine when the number of integrals is three. [Figure 135] FIG. 135 is a diagram showing the result of determining whether or not the curves k208 and k209 shown in FIG. 134 are different from each other. [Figure 136] FIG. 10 is a diagram showing two integral spectra for the same wine when the number of integrals is four. [Figure 137] FIG. 137 is a diagram showing the result of determining whether or not the curves k210 and k211 shown in FIG. 136 are different from each other. [Figure 138] FIG. 10 is a diagram showing two integral spectra for the same wine when the number of integrals is six. [Figure 139] FIG. 139 shows the result of determining whether or not the curves k212 and k213 shown in FIG. 138 are different from each other. [Figure 140] FIG. 10 is a diagram showing two integral spectra for the same wine when the number of integrals is eight. [Figure 141] FIG. 141 is a diagram showing the result of determining whether or not the curves k214 and k215 shown in FIG. 140 are different from each other. [Figure 142] FIG. 10 shows two integral spectra for the same wine when the number of integrals is 10. [Figure 143] FIG. 143 is a diagram showing the result of determining whether or not the curves k216 and k217 shown in FIG. 142 are different from each other. [Figure 144] FIG. 10 shows two integral spectra for the same wine when the number of integrals is 14. [Figure 145] FIG. 145 is a diagram showing the result of determining whether or not the curves k218 and k219 shown in FIG. 144 are different from each other. [Figure 146] FIG. 10 shows two integral spectra for the same wine when the number of integrals is 28. [Figure 147] FIG. 147 is a diagram showing the result of determining whether or not the curves k220 and k221 shown in FIG. 146 are different from each other. [Figure 148] FIG. 10 shows two integral spectra for the same wine when the number of integrals is 41. [Figure 149] FIG. 149 shows the result of determining whether or not the curves k222 and k223 shown in FIG. 148 are different from each other. [Figure 150] FIG. 10 shows two integral spectra for the same wine when the number of integrals is 95. [Figure 151] FIG. 151 is a diagram showing the result of determining whether or not the curves k224 and k225 shown in FIG. 150 are different from each other. [Figure 152] FIG. 10 shows two integral spectra for the same wine when the number of integrals is 142. [Figure 153] FIG. 153 is a diagram showing the result of determining whether or not the curves k226 and k227 shown in FIG. 152 are different from each other. [Fig. 154] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is three. [Figure 155] FIG. 155 is a diagram showing the results of determining whether or not the curves k231 to k233 shown in FIG. 154 are different from one another. [Figure 156] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is four. [Figure 157] FIG. 157 is a diagram showing the results of determining whether or not the curves k234 to k236 shown in FIG. 156 are different from one another. [Figure 158] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is five. [Figure 159]FIG. 159 is a diagram showing the results of determining whether or not the curves k237 to k239 shown in FIG. 158 are different from one another. [Figure 160] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is six. [Figure 161] FIG. 161 is a diagram showing the results of determining whether or not the curves k240 to k242 shown in FIG. 160 are different from one another. [Figure 162] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is seven. [Figure 163] FIG. 163 is a diagram showing the results of determining whether or not the curves k243 to k245 shown in FIG. 162 are different from one another. [Fig. 164] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is eight. [Figure 165] FIG. 165 is a diagram showing the results of determining whether or not the curves k246 to k248 shown in FIG. 164 are different from one another. [Figure 166] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is nine. [Figure 167] FIG. 167 is a diagram showing the results of determining whether or not the curves k249 to k251 shown in FIG. 166 are different from one another. [Figure 168] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate, in the case where the number of integral values ​​is 10. [Figure 169] FIG. 169 is a diagram showing the results of determining whether or not the curves k252 to k254 shown in FIG. 168 are different from one another. [Figure 170]FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is 15. [Figure 171] FIG. 171 is a diagram showing the results of determining whether or not the curves k255 to k257 shown in FIG. 170 are different from one another. [Fig. 172] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is 20. [Figure 173] FIG. 173 is a diagram showing the results of determining whether or not the curves k258 to k260 shown in FIG. 172 are different from one another. [Fig. 174] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is 39. [Figure 175] FIG. 175 is a diagram showing the results of determining whether or not the curves k261 to k263 shown in FIG. 174 are different from one another. [Figure 176] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is 54. [Figure 177] FIG. 177 is a diagram showing the results of determining whether or not the curves k264 to k266 shown in FIG. 176 are different from one another. [Figure 178] FIG. 10 is a diagram showing an integral spectrum created based on cyclic voltammograms CVG measured while changing the potential scanning rate in the case where the number of integral values ​​is 135. [Figure 179] FIG. 179 is a diagram showing the results of determining whether or not the curves k267 to k269 shown in FIG. 178 are different from one another. [Figure 180] 2 is a flowchart for explaining the operation of analysis system 10 shown in FIG. [Figure 181] 181 is a flowchart for explaining the detailed operation of step S9 in FIG. 180. [Figure 182]181 is a flowchart for explaining the detailed operation of step S12 in FIG. 180. [Figure 183] 181 is a flowchart for explaining the detailed operation of step S13 in FIG. 180. [Figure 184] 181 is a flowchart for explaining the detailed operation of step S14 in FIG. 180. [Figure 185] 185 is a flowchart for explaining the detailed operation of step S144 in FIG. 184. [Figure 186] 185 is another flowchart for explaining the detailed operation of step S144 in FIG. 184. [Figure 187] FIG. 10 is a conceptual diagram of a determination result showing whether two curves CURi and CURj of two PC pairs are different. [Figure 188] FIG. 181 is a conceptual diagram showing the update from the analysis data ALY_Duni to the index data IDXuni in step S11 of FIG. 180. [Figure 189] FIG. 181 is a conceptual diagram showing the update of P pieces of analysis data ALY_D1 to ALY_DP to P pieces of index data IDX1 to IDXP in step S16 of FIG. [Figure 190] 2 is another schematic diagram of the analysis device 2 shown in FIG. [Figure 191] FIG. 10 is a schematic diagram of an analysis system according to a second embodiment. [Figure 192] FIG. 192 is a schematic diagram of the analysis device 2B shown in FIG. 191. [Figure 193] FIG. 192 is a schematic diagram of the terminal device 3 shown in FIG. [Figure 194] 192 is a first flowchart for explaining the operation of analysis system 10A shown in FIG. 191. [Figure 195] 192 is a second flowchart for explaining the operation of analysis system 10A shown in FIG. 191. [Figure 196] 196 is a flowchart for explaining the detailed operation of step S24 in FIG. 195. [Figure 197] 196 is a flowchart for explaining the detailed operation of step S34 in FIG. 195. [Figure 198] 192 is another schematic diagram of the analysis device 2B shown in FIG. 191. [Figure 199] FIG. 192 is another schematic diagram of the terminal device 3 shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0044] The analysis system 10 is installed in, for example, a wine bar, a Japanese restaurant, a Western restaurant, a hospital, and the like.

[0045] The sensor device 1 measures the measurement data of the cyclic voltammogram of an object to be analyzed, which is a liquid such as soft drinks, wine, coffee, human urine, and human saliva, using the cyclic voltammetry method, and transmits the measured measurement data of the cyclic voltammogram CVG to the analysis device 2 via wireless communication or wired communication.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0062] The object to be analyzed, which is a liquid, is supplied to a region where a working electrode 112, a counter electrode 113, and a reference electrode 114 are arranged.

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

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

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

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

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

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

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

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

[0071] Here, users of the sensor device 1 are, for example, staff at a wine bar, a Japanese restaurant, a Western restaurant, or the like, or doctors and nurses at a hospital.

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

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

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

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

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

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

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

[0079] 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 current-potential characteristics (IV). The current-potential characteristics (IV) are configured so that potential V corresponds to current value I.

[0080] The name of the object to be analyzed is, for example, any one of soft drinks, wine, coffee, human urine, and human saliva.

[0081] When the name of the analysis object is wine, the types of the analysis object include, for example, red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013).

[0082] The potential V and the current value I are associated with each other. The potential V is expressed as V1 to V d The current value I is I1 to I d The current values ​​I1 to I d are the potentials V1 to V d can be associated with

[0083] When the scanning range of the potential V is -2.5V to +2.5V, the potentials V1, V2, V3, V4, . . ., V d-2 ,V d-1 ,V d are respectively 0V, 1mV, 2mV, 3mV, 2499mV, 2500mV, 2499mV, 2mV, 1mV, 0mV, -1mV, -2mV, -2499mV, -2500mV, -2499mV, -2mV, -1mV, 0V. That is, V1, V2, V3, V4, V d-2 ,V d-1 ,V d consists of potentials that change by unit potential (=1 mV).

[0084] As a result, d represents twice the total number of unit potentials in the scanning range of the potential V.

[0085] The measurement unit 122 receives the name and type of the analysis object from the user of the sensor device 1, and measures the current-potential characteristics (IV) of the analysis object. Then, the measurement unit 122 creates measurement data MRS including the name and type of the analysis object and the current-potential characteristics (IV) of the analysis object, and outputs the created measurement data MRS to the transmission unit 123.

[0086] The transmitting unit 123 receives the measurement data MRS from the measuring unit 122, and transmits the received measurement data MRS to the analyzing device 2 by wired communication or wireless communication.

[0087] When transmitting the measurement data MRS to the analysis device 2 by wireless communication, the transmission unit 123 transmits the measurement data MRS to the analysis device 2 by, for example, Bluetooth (registered trademark).

[0088] Furthermore, when the transmission unit 123 transmits the measurement data MRS to the analysis device 2 by wired communication, the transmission unit 123 is connected to the analysis device 2 by a cable.

[0089] When the sensor device 1 measures P cyclic voltammograms of P (P is an integer equal to or greater than 2) analysis objects by cyclic voltammetry, the measurement unit 122 of the sensor device 1 creates each of the P pieces of measurement data MRS_1 to MRS_P by the above-mentioned method, and the transmission unit 123 of the sensor device 1 transmits the P pieces of measurement data MRS_1 to MRS_P created by the measurement unit 122 to the analysis device 2 by wired or wireless communication. In this case, each of the P pieces of measurement data MRS_1 to MRS_P has the same configuration as the measurement data MRS shown in FIG. 6.

[0090] Fig. 7 is a schematic diagram of the analysis device 2 shown in Fig. 1. Referring to Fig. 7, the analysis device 2 includes a receiving unit 21, a control unit 22, a calculation unit 23, a determination unit 24, a display unit 26, a reception unit 27, and a database 28.

[0091] The receiving unit 21 receives the measurement data MRS from the measuring instrument 12 (transmitting unit 123) of the sensor device 1 by wireless communication or wired communication, and outputs the received measurement data MRS to the control unit 22.

[0092] Here, the measurement data MRS may be one piece of measurement data or may be a plurality of pieces of measurement data.

[0093] The control unit 22 has a built-in timer. When the control unit 22 receives one piece of measurement data MRS_uni from the receiving unit 21, the control unit 22 refers to the timer and calculates the time t uni Identification information ID for detecting the measurement data MRS_uni and identifying the measurement data MRS_uni uni Issue.

[0094] Then, the control unit 22 calculates the name of the object to be analyzed, ALY_Na uni and the type of object to be analyzed ALY_Kd uni and the current-potential characteristic (IV) showing the relationship between the current value I and the potential V. uni and are detected from the measurement data MRS_uni.

[0095] Then, the control unit 22 detects the time t uni , identification information ID uni , Name of the analyte ALY_Na uni , type of object to be analyzed ALY_Kd uni and current-potential characteristics (IV) uni The analysis data ALY_D is a data set that correlates the data with each other. uni =[t uni / ID uni / ALY_Na uni / ALY_Kd uni / (IV) uni ] to create a

[0096] Then, the control unit 22 calculates the analysis data ALY_D uni is stored in the database 28 and the analysis data ALY_D uniis output to the arithmetic unit 23.

[0097] Furthermore, when the control unit 22 receives P pieces of measurement data MRS_1 to MRS_P (i.e., a plurality of pieces of measurement data) from the receiving unit 21, the control unit 22 refers to the timer and calculates the times t1 to t2 when the P pieces of measurement data MRS_1 to MRS_P are received, respectively. P and P pieces of identification information ID1 to ID2 for identifying the P pieces of measurement data MRS_1 to MRS_P, respectively. P Issue.

[0098] Then, the control unit 22 calculates the name of the object to be analyzed, ALY_Na p and the type of object to be analyzed ALY_Kd p and the current-potential characteristic (IV) showing the relationship between current I and potential V. p and are detected from the measurement data MRS_p (p is any one of 1 to P) for all of the P measurement data MRS_1 to MRS_P (that is, a plurality of measurement data).

[0099] Then, the control unit 22 detects the time t p , identification information ID p , Name of the analyte ALY_Na p , type of object to be analyzed ALY_Kd p and current-potential characteristics (IV) p (p is any of 1 to P) p =[t p / ID p / ALY_Na p / ALY_Kd p / (IV) p ] is created for all p = 1 to P, and P analysis data ALY_D1 = [t1 / ID1 / ALY_Na1 / ALY_Kd1 / (IV)1] to ALY_D P =[t P / ID P / ALY_Na P / ALY_Kd P / (IV) P ] to create a

[0100] Then, the control unit 22 generates P pieces of analysis data ALY_D1 to ALY_D P are stored in the database 28, and P pieces of analysis data ALY_D1 to ALY_D P is output to the arithmetic unit 23.

[0101] Furthermore, the control unit 22 generates the analysis data ALY_D uni to the arithmetic unit 23, and then the identification information ID uni and calculation data CAL uni and curve CUR un Analysis results ALY_RLS associated with i uni =[ID uni / CAL uni / CUR uni ] is received from the creation unit 25, the analysis result ALY_RLS uni From Identification ID uni , calculation data CAL uni and the curve CUR uni and the detected identification information ID uni Analysis data ALY_D with the same identification information uni is read from the database 28.

[0102] Then, the control unit 22 reads out the analysis data ALY_D from the database 28. uni Calculation data CAL uni and the curve CUR uni Store the data for analysis ALY_D uni The index data IDX uni and the updated index data IDX uni The analysis data ALY_D uni Instead, the data is stored in the database 28.

[0103] Furthermore, the control unit 22 generates P pieces of analysis data ALY_D1 to ALY_D P to the arithmetic unit 23, and then P pieces of identification information ID1 to ID P and P calculation data CAL1 to CAL P and P curves CUR1 to CURP P analysis results ALY_RLS1=[ID1 / CAL1 / CUR1]~ALY_RLS P =[ID P / CAL P / CUR P ] is received from the creation unit 25, the analysis result ALY_RLS p =[ID p / CAL p / CUR p ] (p is one of 1 to P) to the identification information ID p , calculation data CAL p and the curve CUR p and the detected identification information ID p Analysis data ALY_D with the same identification information p is read from the database 28.

[0104] Then, the control unit 22 reads out the analysis data ALY_D from the database 28. p Calculation data CAL p and the curve CUR p Store the data for analysis ALY_D p The index data IDX p and the updated index data IDX p The analysis data ALY_D p Instead, the data is stored in the database 28.

[0105] The control unit 22 generates the analysis data ALY_D by the above-described method. p The index data IDX p The update process is performed on P analysis data ALY_D1 to ALY_D P , and P analysis data ALY_D1 to ALY_D P P index data IDX1 to IDX P The updated P index data IDX1 to IDX P Each of the P analysis data ALY_D1 to ALY_D P Instead, the data is stored in the database 28.

[0106] Furthermore, the control unit 22 determines the name of the object to be analyzed, ALY_Na uni And the name ALY_Na uni The curve CUR associated with uni and request to view RQT uni When the name of the object to be analyzed, ALY_Na, is received from the reception unit 27, uni Based on the analyte name ALY_Na uni The curve CUR associated with uni is detected from the database 28, and the name of the object to be analyzed is ALY_Na uni and curve CUR uni and is output to the display unit 26.

[0107] Furthermore, the control unit 22 calculates the P names ALY_Na1 to ALY_Na of the P analysis objects. P q (q is an integer satisfying 1≦q≦P) names ALY_Na1 to ALY_Na q and q names ALY_Na1~ALY_Na q q curves CUR1 to CUR2 respectively associated with q and request to view RQT q When the reception unit 27 receives the q names ALY_Na1 to ALY_Na q Based on this, q names ALY_Na1 to ALY_Na of q analytes are q and q names of q analysis objects ALY_Na1 to ALY_Na q q curves CUR1 to CUR2 respectively associated with q and q names ALY_Na1 to ALY_Na2 of the q analysis objects are detected from the database 28. q and q curves CUR1 to CUR q and is output to the display unit 26.

[0108] In this case, the request RQT q In the example, q names of q analysis objects are ALY_Na1 to ALY_Na q Instead of q types ALY_Kd1 to ALY_Kd of q analysis objects q may also be used.

[0109] The calculation unit 23 calculates the analysis data ALY_D uni =[t uni / ID uni / ALY_Na uni / ALY_Kd uni / (IV) uni ] is received from the control unit 22.

[0110] Then, the calculation unit 23 calculates the analysis data ALY_D uni =[t uni / ID uni / ALY_Na uni / ALY_Kd uni / (IV) uni Current-potential characteristics of (IV) uni Based on this, the integral value ITG of the cyclic voltammogram CVG is calculated for each predetermined potential interval V_ITV by a method to be described later, and this is executed for all predetermined potential intervals V_ITV.

[0111] Then, the calculation unit 23 calculates the number of predetermined potential sections V_ITV as N SEC If the number of predetermined potential sections is N, the first predetermined potential section V_ITV is set to "Class 1", the second predetermined potential section V_ITV is set to "Class 2", and so on. SEC -1)th predetermined potential section V_ITV is set as "class (N SEC -1)” and N SEC The th predetermined potential section V_ITV is set as "Class N SEC "

[0112] Then, the calculation unit 23 calculates N SEC Individual class and N SEC Calculation data CAL corresponding to the integral values uni Create the calculated data CAL uni and identification information ID uni CAL_RLS is the calculation result that corresponds to uni =[ID uni / CAL uni Then, the calculation unit 23 generates the calculation result CAL_RLS uni =[IDuni / CAL uni ] and a signal S_u indicating that there is one piece of calculation data to the creation unit 25.

[0113] The calculation unit 23 also calculates the P pieces of analysis data ALY_D1 to ALY_D P is received from the control unit 22.

[0114] Then, the calculation unit 23 calculates the analysis data ALY_D uni =[t uni / ID uni / ALY_Na uni / (IV) uni ] from the control unit 22, the analysis data ALY_D p =[t p / ID p / ALY_Na p / ALY_Kd p / (IV) p Current-potential characteristics of (IV) p (p is one of 1 to P) based on SEC Individual class and N SEC Calculation data CAL corresponding to the integral values p Create P analysis data ALY_D1 to ALY_D P Calculation data CAL1 to CAL2 are executed for all of the above. P Create a.

[0115] Then, the calculation unit 23 calculates the P pieces of identification information ID1 to ID P and P calculation data CAL1 to CAL P P calculation results CAL_RLS1=[ID1 / CAL1]~CAL_RLS P =[ID P / CAL P ] to the determination unit 24 and the creation unit 25.

[0116] In this way, the calculation unit 23 calculates one calculation data CAL uni When you create the calculation result CAL_RLS uni =[IDuni / CAL uni ] is output only to the creation unit 25, and P calculation results CAL_RLS1=[ID1 / CAL1]~CAL_RLS P =[ID P / CAL P ] (i.e., multiple calculation results) are created, P calculation results CAL_RLS1 to CAL_RLS P (i.e., a plurality of calculation results) are output to the determination unit 24 and the creation unit 25.

[0117] As mentioned above, the calculation data CAL uni is N SEC Individual class and N SEC It consists of a structure in which N integral values ​​are associated with each other. SEC Each of these classes is made up of a predetermined potential section V_ITV, so the calculation data CAL uni is N SEC The predetermined potential sections V_ITV and N SEC The P calculation data CAL1 to CAL2 are associated with the P integral values. P The same is true for each of the above.

[0118] The decision unit 24 calculates P calculation results CAL_RLS1 to CAL_RLS P (i.e., a plurality of calculation results) from the calculation unit 23. Then, the decision unit 24 determines the P calculation results CAL_RLS1 to CAL_RLS P P calculation data CAL1 to CAL P Detect.

[0119] Then, the judgment unit 24 calculates the P pieces of calculation data CAL1 to CAL P Based on P C2 sets of two calculation data CAL i ,CAL j Detect (i≠j), where P C2 is P pieces of calculation data CAL1 to CAL P Two different calculation data CAL i ,CAL j Two different calculation data CAL when extracting (i≠j)i ,CAL j The number of combinations where (i≠j).

[0120] Then, the decision unit 24 calculates the two pieces of calculation data CAL i ,CAL j Based on (i≠j), the calculation data CAL i Multiple integral values ​​and calculation data CAL included in j Calculate the difference between the multiple integral values ​​included in and calculate the standard deviation of the calculated difference. P C2 sets of two calculation data CAL i ,CAL j This is executed for all (i≠j).

[0121] The determination unit 24 determines the calculation data CAL by the following method. i Multiple integral values ​​and calculation data CAL included in j Calculate the difference between the multiple integral values ​​included in

[0122] The judgment unit 24 calculates the calculation data CAL i Multiple integral values ​​ITG included in 1_i ~ITG n_i and calculation data CAL j Multiple integral values ​​ITG included in 1_j ~ITG n_j Based on this, one class Cls is calculated by the following formula: k The integral value ITG k_i and the integral value ITG k_j Difference DF k Calculate the following.

[0123]

number

[0124] The difference DF calculated by equation (1) k The unit is "%".

[0125] Then, the decision unit 24 determines one class Cls by the formula (1). kThe difference DF of the integral value at k The calculation is performed for n classes Cls1 to Cls n DF1 to DF2 n Calculate.

[0126] Then, the decision unit 24 calculates the n differences DF1 to DF n Standard deviation σ DF Calculate.

[0127] Then, the decision unit 24 calculates the standard deviation σ of the differences. DF is the threshold σ th (=15%), the two calculation data CAL i ,CAL j (i≠j) is judged to be different, and the standard deviation of the difference σ DF is the threshold σ th (=15%) or less, two calculation data CAL i ,CAL j (i≠j) is determined to be not different. th (=15%) is held in advance.

[0128] The decision unit 24 determines the two calculation data CAL by the above-mentioned method. i ,CAL j Determine whether (i≠j) is different. P C2 sets of two calculation data CAL i ,CAL j Execute for all (i≠j) P C2 sets of two calculation data CAL i ,CAL j Two calculation data CAL for each pair (i≠j) i ,CAL j Determine whether (i≠j) is different.

[0129] In addition, two calculation data CAL i ,CAL j The determination that (i≠j) does not differ means that the two calculation data CAL i ,CAL jThis corresponds to determining that (i≠j) cannot be distinguished, and two calculation data CAL i ,CAL j The determination that (i≠j) is different means that the two calculation data CAL i ,CAL j This is equivalent to determining that (i≠j) can be distinguished.

[0130] The decision unit 24 determines, by the above-mentioned method, P C2 sets of two calculation data CAL i ,CAL j Two calculation data CAL for each pair (i≠j) i ,CAL j The determination unit 24 determines whether or not (i≠j) is different, and creates the determination result shown in Table 1. Then, the determination unit 24 outputs the determination result shown in Table 1 to the creation unit 25.

[0131] [Table 1]

[0132] P calculation data CAL1 to CAL P are not different from each other, the P pieces of calculation data CAL1 to CAL P This corresponds to determining that the P calculation data CAL1 to CAL P are determined to be different from each other, the P pieces of calculation data CAL1 to CAL P This corresponds to determining that the two images can be distinguished from each other.

[0133] The creation unit 25 generates one calculation result CAL_RLS uni and a signal S_u indicating that there is one calculation data item, the calculation data CAL is calculated by the following method. uni Based on the curve CUR uni Create a.

[0134] FIG. 8 is a diagram for explaining a method for creating a curve CUR that shows the relationship between a plurality of classes Cls and a plurality of integral values ​​ITG.

[0135] (a) of FIG. 8 shows one calculation data CAL. uni (b) of Figure 8 shows the curve CUR, which shows the relationship between the integral value and the class. uni Shows.

[0136] Referring to FIG. 8(a), the calculation data CAL uni is the name of the analyte ALY-Na uni and the type of analyte ALY-Kd uni The class Cls includes n classes Cls1 to Cls2, and an integral value ITG. n The integral value ITG is made up of n integral values ​​ITG1 to ITG n Here, n represents the total number of predetermined potential sections, and the potential scan range is [-V S1 ~+V S2 ], and one predetermined potential section is defined as V PTS Then, n=(|-V S1 |+|+V S2 |) / V PTS is.

[0137] n integral values ​​ITG1 to ITG n are n classes Cls1 to Cls n can be associated with

[0138] The creation unit 25 creates one calculation data CAL. uni and a signal S_u indicating that the number of pieces of calculation data is one, the calculation unit 23 determines that the number of pieces of calculation data CAL calculated by the calculation unit 23 is one based on the signal S_u.

[0139] The creation unit 25 then creates a set of (class Cls1, integral value ITG1) that are associated with each other as calculation data CAL uni Then, one set (class Cls2, integral value ITG2) is calculated as the data CAL. uni Then, in the same way, a set of (class Cls n-1 ,integral value ITG n-1 ) to calculate the data CAL uniDetect from one set of (class Cls n ,integral value ITG n ) to calculate the data CAL uni Detect from.

[0140] Then, the creation unit 25 displays one set of (class Cls1, integral value ITG1), one set of (class Cls2, integral value ITG2), one set of (class Cls3, integral value ITG3), ..., one set of (Cls n-2 ,integral value ITG n―2 ), 1 group (Class Cls n-1 ,integral value ITG n―1 ) and a set of (class Cls n ,integral value ITG n ) to plot.

[0141] Then, the creation unit 25 creates a curve CUR by connecting the n plotted points. uni In this case, the n plotted points are plotted for each class Cls, so the creation unit 25 creates a curve CUR consisting of a smooth curve by connecting the n plotted points. uni can be created.

[0142] After plotting n points, the creation unit 25 determines a regression curve using the class Cls as an explanatory variable and the integral value ITG as a response variable to create a curve CUR. uni You may also create a

[0143] Then, the creation unit 25 creates the curve CUR uni is defined as [an index curve, which is a curve that serves as an index when identifying the object to be analyzed].

[0144] The creation unit 25 also creates P calculation results CAL_RLS1 to CAL_RLS P (i.e., a plurality of calculation results) from the calculation unit 23 and P pieces of calculation data CAL1 to CAL P The determination unit 24 receives a determination result (determination result shown in Table 1) indicating whether or not the two are different.

[0145] Then, the creating unit 25 creates one piece of calculation data CAL by the method described with reference to FIG. p (p is one of 1 to P) p P calculation data CAL1 to CAL P , and P curves CUR1 to CUR2 are calculated. P (i.e., multiple curves CUR).

[0146] In this case, P pieces of calculation data CAL1 to CAL P (i.e., a plurality of calculation data) are calculated as shown in FIG. 8(a) by the calculation data CAL uni It has the same configuration as

[0147] In addition, the curve CUR uni is the n integral values ​​ITG1 to ITG n ] of [n classes Cls1~Cls n ] dependency. And, n classes Cls1 to Cls n Each of the curves CUR consists of a predetermined potential section. uni is a curve that represents the dependency of the integral value on a predetermined potential section. Similarly, P curves CUR1 to CUR P Each of the curves represents the dependency of the integral value on a predetermined potential interval.

[0148] The creation unit 25 creates one curve CUR uni When you create the calculation result CAL_RLS uni Curve CUR uni Add ALY_RLS to the analysis results uni =[ID uni / CAL uni / CUR uni ] and the analysis result ALY_RLS uni =[ID uni / CAL uni / CUR uni ] to the control unit 22, and the curve CUR uni is output to the display unit 26.

[0149] On the other hand, the creation unit 25 creates P curves CUR1 to CUR P When P calculation results CAL_RLS1 to CAL_RLS are created, P P curves CUR1 to CUR P Add P analysis results ALY_RLS1=[ID1 / CAL1 / CUR1]~ALY_RLS P =[ID P / CAL P / CUR P ] and P analysis results ALY_RLS1 to ALY_RLS P The determination result (determination result shown in FIG. 187 to be described later) is output to the control unit 22, and the P curves CUR1 to CUR P The determination result (determination result shown in FIG. 187 to be described later) is output to the display unit 26.

[0150] The display unit 26 displays one curve CUR uni When the created curve CUR is received from the creation unit 25, uni Display.

[0151] On the other hand, the display unit 26 displays P curves CUR1 to CUR P When the determination result (determination result shown in FIG. 187 to be described later) is received from the creation unit 25, P curves CUR1 to CUR P The judgment result (the judgment result shown in FIG. 187 to be described later) is displayed.

[0152] As a result, staff at a wine bar, Japanese restaurant, Western restaurant, or the like where the analysis system 10 is installed, or hospital staff (for example, doctors or nurses), can easily read the P curves CUR1 to CUR2 displayed on the display unit 26. P and P curves CUR1 to CUR2 are determined by referring to the determination results (determination results shown in FIG. 187, which will be described later). P are different from each other, or P curves CUR1 to CUR P Which curves are different from each other, and P curves CUR1 to CUR P It can be seen which of the curves do not differ from each other.

[0153] The display unit 26 also displays the name of the object to be analyzed, ALY_Na. uni and curve CUR uni When the name of the object to be analyzed is received from the control unit 22, the name of the object to be analyzed, ALY_Na, is uni and the curve CUR uni Display.

[0154] Furthermore, the display unit 26 displays q (q is an integer satisfying 1≦q≦P) names of the analysis objects ALY_Na1 to ALY_Na q and q curves CUR1 to CUR q When the control unit 22 receives the q names ALY_Na1 to ALY_Na q and q curves CUR1 to CUR q Display.

[0155] The reception unit 27 receives the name of the object to be analyzed, ALY_Na, from a user of the analysis device 2 (for example, a staff member at a wine bar, a Japanese restaurant, a Western restaurant, or the like, or a hospital staff member (for example, a doctor or nurse)). uni And the name ALY_Na uni The curve CUR associated with uni and request to view RQT uni Then, the reception unit 27 receives the request RQT uni to the control unit 22.

[0156] The reception unit 27 also receives P names ALY_Na1 to ALY_Na2 of the P analysis objects from a user of the analysis device 2 (for example, a staff member at a wine bar, a Japanese restaurant, a Western restaurant, or the like, or a hospital staff member (for example, a doctor or nurse)). P q names ALY_Na1~ALY_Na q and q names ALY_Na1~ALY_Na q q curves CUR1 to CUR q and request to view RQT q Then, the reception unit 27 receives the request RQT q to the control unit 22.

[0157] Database 28 is the analytical data ALY_D uni or P pieces of analysis data ALY_D1 to ALY_D P or analysis data ALY_D uni Instead of index data IDX uni or P pieces of analysis data ALY_D1 to ALY_D P Instead of these, P index data IDX1 to IDX P Remember.

[0158] 9 and 10 are first and second conceptual diagrams, respectively, for explaining a method for calculating an integral value.

[0159] Referring to FIG. 9, the cyclic voltammogram CVG is measured by, for example, scanning the potential V from 0 V to +2500 mV at a predetermined scanning rate, then scanning the potential V from +2500 mV to 0 V at a predetermined scanning rate, further scanning the potential V from 0 V to −2500 mV at a predetermined scanning rate, and further scanning the potential V from −2500 mV to 0 V at a predetermined scanning rate.

[0160] 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.

[0161] 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.

[0162] The predetermined scanning speed is, for example, one of 0.3 V / sec, 0.5 V / sec, and 0.6 V / sec.

[0163] When calculating the integral value ITG of the cyclic voltammogram CVG, the predetermined potential interval V_ITV is, for example, [0 to 100mV], [101 to 200mV], [201 to 300mV], ···, [2301 to 2400mV], [2401 to 2500mV], [0 to -100mV], [-101 to -200mV], ···, [-2301 to -2400mV], [-2401 to -2500mV].

[0164] FIG. 10 shows an enlarged view of a portion of the cyclic voltammogram CVG shown in FIG. 9 in a predetermined potential interval [V1 to V2].

[0165] Referring to Figure 10, when calculating the integral value ITG for a predetermined potential range [V1 to V2], the calculation unit 23 detects the current value Iox_1 of the oxidation wave and the current value Ird_1 of the reduction wave at potential V1, and calculates the difference (Iox_1-Ird_1) between the current value Iox_1 and the current value Ird_1 to calculate the intensity (Iox_1-Ird_1) of the cyclic voltammogram CVG at potential V1.

[0166] Then, the calculation unit 23 detects the current value Iox_2 of the oxidation wave and the current value Ird_2 of the reduction wave at a potential V1+1 obtained by adding a unit potential (=1 mV) to the potential V1, and calculates the difference (Iox_2-Ird_2) between the current value Iox_2 and the current value Ird_2 to calculate the intensity (Iox_2-Ird_2) of the cyclic voltammogram CVG at the potential V1+1.

[0167] Furthermore, the calculation unit 23 detects the current value Iox_3 of the oxidation wave and the current value Ird_3 of the reduction wave at a potential V1+2 obtained by adding a unit potential (=1 mV) to the potential V1+1, and calculates the difference (Iox_3-Ird_3) between the current value Iox_3 and the current value Ird_3 to calculate the intensity (Iox_3-Ird_3) of the cyclic voltammogram CVG at the potential V1+2.

[0168] Similarly, the calculation unit 23 detects the current value Iox_N of the oxidation wave and the current value Ird_N of the reduction wave at potential V2, which is obtained by adding a unit potential (=1 mV) to potential V2-1, and calculates the difference (Iox_N-Ird_N) between the current value Iox_N and the current value Ird_N to calculate the intensity (Iox_N-Ird_N) of the cyclic voltammogram CVG at potential V2.

[0169] Then, the calculation unit 23 calculates the integral value ITG in the predetermined potential section [V1 to V2] by the following equation.

[0170]

number

[0171] That is, the calculation unit 23 calculates multiple intensities ((Iox_1-Ird_1), (Iox_2-Ird_2), ···, (Iox_N-Ird_N)) of the cyclic voltammogram CVG for each unit potential (=1 mV) in the predetermined potential range [V1 to V2], and adds up the multiple calculated 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 [V1 to V2].

[0172] Here, in a predetermined potential interval [V1 to V2], calculating the difference (Iox_1 - Ird_1) at potential V1, calculating the difference (Iox_2 - Ird_2) at potential V1+1, calculating the difference (Iox_3 - Ird_3) at potential V1+2, ..., calculating the difference (Iox_N - Ird_N) at potential V2 corresponds to calculating the intensity of a cyclic voltammogram at one unit potential in one predetermined potential interval by performing a subtraction process to subtract the current value of the reduction wave from the current value of the oxidation wave of the cyclic voltammogram at one unit potential, and performing this process for all unit potentials in one predetermined potential interval to calculate multiple intensities in one predetermined potential interval.

[0173] Then, calculating the integral value ITG in the predetermined potential interval [V1 to V2] using equation (2) is equivalent to calculating the sum of the calculated intensities as the area of ​​the cyclic voltammogram in one predetermined potential interval.

[0174] Furthermore, calculating the difference (Iox_1-Ird_1) at potential V1, calculating the difference (Iox_2-Ird_2) at potential V1+1, calculating the difference (Iox_3-Ird_3) at potential V1+2, ..., calculating the difference (Iox_N-Ird_N) at potential V2 each corresponds to subtracting the current value of the reduction wave from the current value of the oxidation wave of the cyclic voltammogram at one unit potential in one specified potential range to calculate the intensity of the cyclic voltammogram at one unit potential.

[0175] The arithmetic unit 23 calculates a plurality of integral values ​​ITG1, ITG2, ITG3, ITG4 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] by the method of calculating the integral value ITG of the cyclic voltammogram CVG in the predetermined potential interval [V1 to V2] described above. 24 ,ITG 25 ,ITG 26 ,ITG 27 ,···,ITG 49 ,ITG 50 Calculate.

[0176] The plurality of integral values ​​ITG1 to ITG2 are calculated by the methods shown in FIGS. 9 and 10. 50 By calculating the above, it is possible to correct the characteristic variations between sensors used to measure the cyclic voltammogram CVG, and to clearly show the signal intensity differences between solutions of the object to be analyzed.

[0177] In the above formula (2), (Iox_1-Ird_1), (Iox_2-Ird_2), ..., (Iox_N-Ird_N) each represent the current value in the oxidation reaction and reduction reaction between the electrode (working electrode 112) and the object to be analyzed at each unit potential.

[0178] Furthermore, the sum (=integral value) of the subtraction results (Iox-Ird) in one predetermined potential section represents the total number of electrons in the oxidation and reduction reactions between the electrode (working electrode) and the object to be analyzed in one predetermined potential section.

[0179] Furthermore, the curve CUR represents the dependency of the total number of electrons (=integral value) in the oxidation reaction and reduction reaction between the electrode (working electrode) and the object to be analyzed on a predetermined potential interval.

[0180] In the embodiment of the present invention, the arithmetic unit 23 may calculate the integral value ITG in the predetermined potential section [V1 to V2] by the following formula.

[0181]

number

[0182] In formula (3), formula (3A) represents the sum of oxidation currents in the predetermined potential interval [V1-V2], and formula (3B) represents the sum of reduction currents in the predetermined potential interval [V1-V2]. In formulas (3A) and (3B), N represents the total number of unit potentials in the predetermined potential interval [V1-V2], where n = 1 to N.

[0183] As a result, equation (3C) represents the integral value ITG in the predetermined potential section [V1 to V2].

[0184] The calculation unit 23 calculates the sum I of oxidation currents in the predetermined potential range [V1 to V2] by the formula (3A). ITG ox The sum of the reduction currents in the predetermined potential range [V1 to V2] is calculated using equation (3B). ITG rd Calculate.

[0185] Then, the calculation unit 23 calculates the sum of the oxidation currents I by the formula (3C). ITG ox The sum of the reduction currents I ITG rd is subtracted to calculate the integral value ITG(V1-V2) in the predetermined potential section [V1 to V2].

[0186] The integral ITG(V1-V2) calculated by the formula (3) coincides with the integral ITG(V1-V2) calculated by the formula (2) described above.

[0187] 11 is a diagram showing an example of calculation data. Referring to FIG. 11, the calculation unit 23 calculates a plurality of integral values ​​ITG1, ITG2, ITG3, . . . , ITG 24 ,ITG 25 ,ITG 26 ,ITG 27 ,···,ITG 49 ,ITG 50 When the calculation is performed, a plurality of predetermined potential ranges [0 to 100mV], [101 to 200mV], [201 to 300mV], , [2301 to 2400mV], [2401 to 2500mV], [0 to -100mV], [-101 to -200mV], , [-2301 to -2400mV], [-2401 to -2500mV] are each divided into a plurality of classes Cls1 to Cls2. 50 Then, multiple classes Cls1 to Cls 50 are multiple integral values ​​ITG1, ITG2, ITG3, . . ., ITG 24 ,ITG 25 ,ITG 26 ,ITG 27 ,···,ITG 49 ,ITG 50 are associated to generate the calculation data CAL1.

[0188] Fig. 12 shows a portion of a cyclic voltammogram. Fig. 12 shows a cyclic voltammogram having a region REG where the oxidation wave is located below the reduction wave. Fig. 12(a) shows the case where region REG is located in the positive current region, Fig. 12(b) shows the case where, in region REG, the reduction wave is located in the positive current region and the oxidation wave is located in the negative current region, and Fig. 12(c) shows the case where region REG is located in the negative current region.

[0189] Here, the current value of the oxidation wave at the unit potential of the region REG is I ox_1_REG ,I ox_2_REG ,···,I ox_N’_REG (N' is the total number of unit potentials in the region REG), and the current value of the reduction wave at the unit potential of the region REG is I rd_1_REG ,I rd_2_REG ,···,I rd_N’_REG Let's say.

[0190] Referring to (a) of FIG. 12, when calculating the integral value of a predetermined potential section in the region REG, the current value I ox_1_REG ,I ox_2_REG ,···,I ox_N’_REG are the current values ​​of the reduction waves, I rd_1_REG ,I rd_2_REG ,···,I rd_N’_REG Since it is smaller than , the integral value ITG of the predetermined potential section in the region REG becomes a negative value.

[0191] Referring to (b) of FIG. 12, when calculating the integral value of a predetermined potential section in the region REG, the current value I ox_1_REG ,I ox_2_REG ,···,I ox_N’_REG Each of these is a negative current value, and the reduction wave current value I rd_1_REG ,I rd_2_REG ,···,I rd_N’_REG are positive current values, the integral value ITG of the predetermined potential section in the region REG becomes a negative value.

[0192] Referring to (c) of FIG. 12, when calculating the integral value of a predetermined potential section in the region REG, the current value I ox_1_REG,I ox_2_REG ,···,I ox_N’_REG Each of these is a negative current value, and the reduction wave current value I rd_1_REG ,I rd_2_REG ,···,I rd_N’_R Each of these currents is negative, and the oxidation current I ox_1_REG ,I ox_2_REG ,···,I ox_N’_REG Absolute value of |I ox_1_REG |,|I ox_2_REG |,···,|I ox_N’_REG | are the current values ​​of the reduction waves, I rd_1_REG ,I rd_2_REG ,···,I rd_N’_R Absolute value of |I rd_1_REG |,|I rd_2_REG |,···,|I rd_N’_R |, the integral ITG of the predetermined potential section in the region REG becomes a negative value.

[0193] Therefore, in this embodiment of the present invention, the integral value of a predetermined potential section of a cyclic voltammogram having a region REG where the oxidation wave is located below the reduction wave becomes a negative value in the region REG.

[0194] The arithmetic unit 23 may calculate a plurality of integral values ​​in a plurality of predetermined potential intervals using a method different from the above-described method.

[0195] For example, the calculation unit 23 may use the potential V as the explanatory variable and the current I as the target variable to calculate a regression curve RC1 (the curve indicated by the solid line) showing the oxidation wave of the cyclic voltammogram CVG in Figure 9 and a regression curve RC2 (the curve indicated by the dashed line) showing the reduction wave, calculate the integral value ITG_RC1 of the regression curve RC1 and the integral value ITG_RC2 of the regression curve RC2 in a predetermined potential interval [V1-V2], and calculate the integral value of the cyclic voltammogram CVG shown in Figure 9 in the predetermined potential interval [V1-V2] by subtracting the integral value ITG_RC2 from the integral value ITG_RC1 for all predetermined potential intervals, thereby calculating multiple integral values ​​in multiple predetermined potential intervals.

[0196] The arithmetic unit 23 may calculate the plurality of integral values ​​in the plurality of predetermined potential sections by any other method as long as the method can calculate the plurality of integral values ​​in the plurality of predetermined potential sections.

[0197] (Example) The curve CUR generated by the analysis device 2 will be described below for cases where the objects to be analyzed are Calpis, wine, coffee, human urine, and human saliva.

[0198] (1) Calpis Figure 13 shows cyclic voltammograms of Calpis. Figure 13(a) shows the cyclic voltammogram CVG_Cal_1 of undiluted Calpis, Figure 13(b) shows the cyclic voltammogram CVG_Cal_2 of Calpis diluted two-fold with tap water, and Figure 13(c) shows the cyclic voltammogram CVG_Cal_3 of Calpis diluted five-fold with tap water.

[0199] Referring to FIG. 13, the standard deviation σ of multiple current values ​​in the cyclic voltammogram CVG_Cal_1 CVG_Cal_1 is 163.81, and the standard deviation σ of multiple current values ​​in the cyclic voltammogram CVG_Cal_2 is CVG_Cal_2 is 160.75, and the standard deviation σ of multiple current values ​​in the cyclic voltammogram CVG_Cal_3 CVG_Cal_3 is 152.89.

[0200] And the standard deviation σ CVG_Cal_1 ~σ CVG_Cal_3 Based on this, the indices IDX1 to IDX3 are calculated using the following formulas.

[0201]

number

[0202] In each of formulas (4A) to (4C), σ CVG_AVERAGEis the standard deviation σ of the three cyclic voltammograms CVG_Cal_1 to CVG_Cal_3 to be compared. CVG_Cal_1 ~σ CVG_Cal_3 is the average of CVG_AVERAGE =(σ CVG_CAL_1 +σ CVG_CAL_2 +σ CVG_CAL_3 ) / 3.

[0203] And σ CVG_Cal_1 =163.81, σ CVG_Cal_2 =160.75 and σ CVG_Cal_3 When the indices IDX1 to IDX3 are calculated using the above formula, IDX1=2.93(%), IDX2=1.01(%), and IDX3=-3.93(%).

[0204] As a result, each of the indices IDX1 to IDX3 is determined based on the threshold value σ th (=15%).

[0205] Therefore, the three cyclic voltammograms CVG_Cal_1 to CVG_CAL_3 do not differ from one another.

[0206] As a result, it is difficult to distinguish between "undiluted Calpis," "Calpis diluted 2 times with tap water," and "Calpis diluted 5 times with tap water" using the three cyclic voltammograms CVG_Cal_1 to CVG_Cal_3.

[0207] FIG. 14 shows the integrated spectra of undiluted Calpis, Calpis diluted two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water.

[0208] 14, each of the curves k1 to k4 is a curve CUR (index curve) created by the above-mentioned method by the analysis device 2. The curve k1 represents the integral spectrum of undiluted Calpis, the curve k2 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k3 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k4 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0209] Table 2 shows the measurement conditions for the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k1 to k4.

[0210] [Table 2]

[0211] As shown in Table 2, the working electrode is made of diamond with a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scanning range when measuring the cyclic voltammogram is -2.5 V to +2.5 V, the predetermined potential interval when calculating the integral value (integral value extraction potential) is 192 mV, and the potential scanning rate is 500 mV / s.

[0212] FIG. 15 is a diagram showing the results of determining whether the curves k1 to k4 shown in FIG. 14 are different from one another.

[0213] Whether or not the curves k1 to k4 are different from one another is determined by determining whether or not two curves among the curves k1 to k4 are different for all combinations of two curves among the curves k1 to k4.

[0214] There are six combinations of two curves among the curves k1 to k4: (k1, k2), (k1, k3), (k1, k4), (k2, k3), (k2, k4), and (k3, k4).

[0215] Therefore, FIG. 15 shows whether or not the two curves differ for six cases: (k1, k2), (k1, k3), (k1, k4), (k2, k3), (k2, k4), and (k3, k4).

[0216] Referring to FIG. 15, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k1 and the plurality of integral values ​​on the curve k2 DF_k1,k2 is σ DF_k1,k2 = 49.37 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k1 and the multiple integral values ​​on the curve k3 DF_k1,k3 is σ DF_k1,k3 = 69.61 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k1 and the multiple integral values ​​on the curve k4 DF_k1,k4 is σ DF_k1,k4 =59.03(%).

[0217] In addition, the standard deviation σ of the difference between the multiple integral values ​​on the curve k2 and the multiple integral values ​​on the curve k3 DF_k2,k3 is σ DF_k2,k3 = 44.49 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k2 and the multiple integral values ​​on the curve k4 DF_k2,k4 is σ DF_k2,k4 = 25.14 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k3 and the multiple integral values ​​on the curve k4 DF_k3,k4 is σ DF_k3,k4 =48.83(%).

[0218] As a result, the standard deviation of the difference σ DF_k1,k2 (=49.37(%)), standard deviation of the difference σ DF_k1,k3 (=69.61(%)), standard deviation of the difference σ DF_k1,k4 (=59.03(%)), standard deviation of the difference σ DF_k2,k3 (=44.49(%)), standard deviation of the difference σ DF_k2,k4 (=25.14(%)), and the standard deviation of the difference σ DF_k3,k4 (=48.83(%)) is the threshold value σ th (=15%).

[0219] Therefore, the two curves k1 and k2 are different, the two curves k1 and k3 are different, the two curves k1 and k4 are different, the two curves k2 and k3 are different, the two curves k2 and k4 are different, and the two curves k3 and k4 are different (see "◯" in FIG. 15). Therefore, the curves k1 to k4 are mutually different curves.

[0220] Furthermore, since the curve k1 has two peaks and the curves k2 to k4 have three peaks, it is easy to understand that the curve k1 is different from the curves k2 to k4.

[0221] Curves k2 to k4 have in common the fact that they have the same number of peaks, but the positions of the peaks of curves k2 to k4 in the region of classes 15 to 25 are different from one another.

[0222] Therefore, it can also be determined that the curves k1 to k4 are different from one another by determining whether the number of peaks is the same and whether the peak positions are the same.

[0223] The analysis system 10 is placed in a soft drink store or the like, and the analysis device 2 of the analysis system 10 has a display unit 26. The display unit 26 displays the curves k1 to k4 and the determination result shown in Figure 15, allowing a salesperson at the soft drink store or the like to determine that the curves k1 to k4 are different from each other.

[0224] As described above, curves k1 to k4 allow the differentiation of undiluted Calpis, Calpis diluted twice with tap water, Calpis diluted three times with tap water, and Calpis diluted four times with tap water.

[0225] Therefore, when it is determined that the curves k1 to k4 are different from one another, the curve k1 is a curve for uniquely identifying [Undiluted Calpis], the curve k2 is a curve for uniquely identifying [Calpis diluted 2 times with tap water], the curve k3 is a curve for uniquely identifying [Calpis diluted 3 times with tap water], and the curve k4 is a curve for uniquely identifying [Calpis diluted 4 times with tap water]. The curve k1 represents the feature quantity based on the integral value for [Undiluted Calpis], the curve k2 represents the feature quantity based on the integral value for [Calpis diluted 2 times with tap water], the curve k3 represents the feature quantity based on the integral value for [Calpis diluted 3 times with tap water], and the curve k4 represents the feature quantity based on the integral value for [Calpis diluted 4 times with tap water].

[0226] As a result, it is possible to determine whether the product is genuine or fake by using the curves k1 to k4, which are index curves. (2) Wine FIG. 16 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013).

[0227] 16, each of the curves k5 to k7 is a curve CUR created by the analysis device 2 using the above-described method. The curve k5 represents the integral spectrum of a red wine (Chile 2020), the curve k6 represents the integral spectrum of a red wine (Australia 2010), and the curve k7 represents the integral spectrum of a red wine (France 2013).

[0228] Table 3 shows the measurement conditions for the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k5 to k7.

[0229] [Table 3]

[0230] As shown in Table 3, the working electrode is made of diamond with a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scanning range when measuring the cyclic voltammogram is -2.5 V to +2.5 V, the predetermined potential interval when calculating the integral value (integral value extraction potential) is 17.6 mV, and the potential scanning rate is 600 mV / s.

[0231] FIG. 17 is a diagram showing the results of determining whether or not the curves k5 to k7 shown in FIG. 16 are different from one another.

[0232] Whether or not the curves k5 to k7 are different from one another is determined by determining whether or not two curves among the curves k5 to k7 are different for all combinations of two curves among the curves k5 to k7.

[0233] There are three combinations of two curves among the curves k5 to k7: (k5, k6), (k5, k7), and (k6, k7).

[0234] Therefore, FIG. 17 shows whether the two curves differ for three cases: (k5, k6), (k5, k7), and (k6, k7).

[0235] Referring to FIG. 17, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k5 and the plurality of integral values ​​on the curve k6 DF_k5,k6 is σ DF_k5,k6 = 65.70 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k5 and the multiple integral values ​​on the curve k7 DF_k5,k7 is σ DF_k5,k7 =74.94(%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k6 and the multiple integral values ​​on the curve k7 DF_k6,k7 is σ DF_k6,k7 =93.19(%).

[0236] As a result, the standard deviation of the difference σ DF_k5,k6 (=65.70(%)), standard deviation of the difference σ DF_k5,k7 (=74.94(%)), and the standard deviation of the difference σ DF_k6,k7(=93.19(%)) are all within the threshold σ th (=15%).

[0237] Therefore, the two curves k5 and k6 are different, the two curves k5 and k7 are different, and the two curves k6 and k7 are different (see "◯" in FIG. 17). Therefore, the curves k5 to k7 are mutually different curves.

[0238] Moreover, since the curves k5 and k6 have two peaks and the curve k7 has three peaks, it is easy to understand that the curve k7 is different from the curves k5 and k6.

[0239] Curves k5 and k6 have in common the fact that they have the same number of peaks, but the positions of the peaks of curves k5 and k6 are different from each other.

[0240] Therefore, it can also be determined that the curves k5 to k7 are different from one another by determining whether the number of peaks is the same and whether the peak positions are the same.

[0241] The analysis system 10 is placed in a wine bar, a Japanese restaurant, a Western restaurant, etc., and the analysis device 2 of the analysis system 10 has a display unit 26. The display unit 26 displays the curves k5 to k7 and the determination result shown in Figure 17, allowing staff at the wine bar, Japanese restaurant, Western restaurant, etc. to determine that the curves k5 to k7 are different from each other.

[0242] As described above, the curves k5 to k7 allow the red wine (Chile 2020), the red wine (Australia 2010), and the red wine (France 2013) to be distinguished from one another.

[0243] Therefore, when it is determined that the curves k5 to k7 are different from one another, the curve k5 is a curve for uniquely identifying a red wine (Chile 2020), the curve k6 is a curve for uniquely identifying a red wine (Australia 2010), and the curve k7 is a curve for uniquely identifying a red wine (France 2013). The curve k5 represents the feature quantity based on the integral value for [red wine (Chile 2020)], the curve k6 represents the feature quantity based on the integral value for [red wine (Australia 2010)], and the curve k7 represents the feature quantity based on the integral value for [red wine (France 2013)].

[0244] As a result, the curves k5 to k7, which are index curves, can be used to determine whether the product is genuine or fake.

[0245] FIG. 18 is a diagram showing integral spectra of red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown).

[0246] 18, each of the curves k8 to k10 is a curve CUR created by the above-described method by the analysis device 2. The curve k8 represents the integral spectrum of a red wine (Italy 2015), the curve k9 represents the integral spectrum of a red wine (France 2016), and the curve k10 represents the integral spectrum of a red wine (Italy, year unknown).

[0247] Table 4 shows the measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k8 to k10.

[0248] [Table 4]

[0249] As shown in Table 4, the working electrode is made of gold and has a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scan range is −1.2 V to +2.0 V, the predetermined potential interval (integral value extraction potential) for calculating the integral value is 34 mV, and the potential scan rate is 600 mV / s.

[0250] As such, the measurement conditions for the cyclic voltammogram shown in Table 4 differ from those for the cyclic voltammogram shown in Table 3 in that a sensor 11 with a gold working electrode is used, the potential scan range is -1.2 V to +2.0 V, and the predetermined potential interval (integral value extraction potential) is 34 mV.

[0251] FIG. 19 is a diagram showing the results of determining whether or not the curves k8 to k10 shown in FIG. 18 are different from one another.

[0252] Whether or not the curves k8 to k10 are different from one another is determined by determining whether or not two curves among the curves k8 to k10 are different for all combinations of two curves among the curves k8 to k10.

[0253] There are three combinations of two curves among the curves k8 to k10: (k8, k9), (k8, k10), and (k9, k10).

[0254] Therefore, FIG. 19 shows whether the two curves differ for three cases: (k8, k9), (k8, k10), and (k9, k10).

[0255] Referring to FIG. 19, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k8 and the plurality of integral values ​​on the curve k9 DF_k8,k9 is σ DF_k8,k9 =57.13(%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k8 and the multiple integral values ​​on the curve k10 DF_k8,k10 is σ DF_k8,k10 = 40.56 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k9 and the multiple integral values ​​on the curve k10 DF_k9,k10 is σ DF_k9,k10=17.85(%).

[0256] As a result, the standard deviation of the difference σ DF_k8,k9 (=57.13(%)), standard deviation of the difference σ DF_k8,k10 (=40.56(%)), and the standard deviation of the difference σ DF_k9,k10 (=17.85(%)) are all within the threshold σ th (=15%).

[0257] Therefore, the two curves k8 and k9 are different, the two curves k8 and k10 are different, and the two curves k9 and k10 are different (see "◯" in FIG. 19). Therefore, the curves k8 to k10 are mutually different curves.

[0258] Furthermore, the curves k8 to k10 have three peaks in common, but the positions of the peaks of the curves k8 to k10 in the range of classes 0 to 20 are different from one another.

[0259] Therefore, it can also be determined that the curves k8 to k10 are different from one another by determining whether the peak positions are the same.

[0260] The analysis system 10 is placed in a wine bar, a Japanese restaurant, a Western restaurant, etc., and the analysis device 2 of the analysis system 10 has a display unit 26. The display unit 26 displays the curves k8 to k10 shown in FIG. 18 and the determination result shown in FIG. 19, allowing staff at the wine bar, Japanese restaurant, Western restaurant, etc. to determine that the curves k8 to k10 are different from each other.

[0261] As described above, curves k8 to k10 allow red wine (Italy 2015), red wine (France 2016), and red wine (Italy, unknown age) to be distinguished from one another.

[0262] Therefore, when it is determined that the curves k8 to k10 are different from one another, the curve k8 is a curve for uniquely identifying a red wine (Italy 2015), the curve k9 is a curve for uniquely identifying a red wine (France 2016), and the curve k10 is a curve for uniquely identifying a red wine (Italy, year unknown). The curve k8 represents the feature quantity based on the integral value for [red wine (Italy 2015)], the curve k9 represents the feature quantity based on the integral value for [red wine (France 2016)], and the curve k10 represents the feature quantity based on the integral value for [red wine (Italy, year unknown)].

[0263] As a result, it is possible to determine whether the product is genuine or fake by using the curves k8 to k10, which are index curves.

[0264] When a cyclic voltammogram was measured for the spoiled wine, the current-potential characteristics (IV) of the cyclic voltammogram included the region REG shown in Figure 12. As a result, the curve CUR, which shows the class dependency of the integral value (dependence on a specific potential interval), included negative integral values.

[0265] Therefore, it has been verified that if the curve CUR contains a negative integral value, the object being analyzed can be determined to be spoiled wine.

[0266] In this way, it is possible to determine whether or not wine as an object to be analyzed is spoiled based on the curve CUR created by the analysis device 2. In other words, it is possible to determine the quality of wine as an object to be analyzed based on the curve CUR.

[0267] In a cyclic voltammogram, it is difficult to see that the current value of the oxidation wave is below the current value of the reduction wave, but it is easy to see whether the curve CUR, which shows the class dependency of the integral value (dependence on a specified potential range), contains a negative integral value. Therefore, the quality of the wine being analyzed can be easily determined based on the curve CUR.

[0268] In this way, the method of creating a curve CUR using the analysis device 2 and judging the quality of wine based on the created curve CUR is an excellent method. (3) Coffee FIG. 20 shows the integrated spectra for WONDA, Craft Boss, and Gold Brew.

[0269] WONDA, Craft Boss and Gold Brew represent different types of coffee.

[0270] 20, each of the curves k11 to k13 is a curve CUR generated by the above-described method by the analysis device 2. The curve k11 represents the integral spectrum of coffee (WONDA), the curve k12 represents the integral spectrum of coffee (Craft Boss), and the curve k13 represents the integral spectrum of coffee (Gold Brew).

[0271] Table 5 shows the measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k11 to k13.

[0272] [Table 5]

[0273] As shown in Table 5, the working electrode is made of diamond with a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scan range is -2.5 V to +2.5 V, the predetermined potential interval (integral value extraction potential) for calculating the integral value is 18.4 mV, and the potential scan rate is 600 mV / s.

[0274] FIG. 21 is a diagram showing the results of determining whether or not the curves k11 to k13 shown in FIG. 20 are different from one another.

[0275] Whether or not the curves k11 to k13 are different from one another is determined by determining whether or not two curves among the curves k11 to k13 are different for all combinations of two curves among the curves k11 to k13.

[0276] There are three combinations of two curves among the curves k11 to k13: (k11, k12), (k11, k13), and (k12, k13).

[0277] Therefore, FIG. 21 shows whether or not the two curves differ for the three cases (k11, k12), (k11, k13), and (k12, k13).

[0278] Referring to FIG. 21, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k11 and the plurality of integral values ​​on the curve k12 DF_k11,k12 is σ DF_k11,k12 = 37.00 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k11 and the multiple integral values ​​on the curve k13 DF_k11,k13 is σ DF_k11,k13 = 66.20 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k12 and the multiple integral values ​​on the curve k13 DF_k12,k13 is σ DF_k12,k13 =50.55(%).

[0279] As a result, the standard deviation of the difference σ DF_k11,k12 (=37.00(%)), standard deviation of the difference σ DF_k11,k13 (=66.20(%)), and the standard deviation of the difference σ DF_k12,k13 (=50.55(%)) is the threshold value σ th (=15%).

[0280] Therefore, the two curves k11 and k12 are different, the two curves k11 and k13 are different, and the two curves k12 and k13 are different (see "◯" in FIG. 21). Therefore, the curves k11 to k13 are mutually different curves.

[0281] Curves k11 to k13 have three peaks in common, but in the range of classes 200 to 270, the positions of the two peaks of curve k13 differ from the positions of the two peaks of curves k11 and k12.

[0282] Furthermore, the curves k11 to k13 have different peak values ​​in the range of 50 to 100 classes, and also have different peak values ​​in the range of 230 to 270 classes.

[0283] Therefore, it can also be determined that the curves k11 to k13 are different from one another by determining whether the peak positions or peak values ​​are the same.

[0284] The analysis system 10 is placed in a coffee shop, and the analysis device 2 of the analysis system 10 has a display unit 26. The display unit 26 displays the curves k11 to k13 shown in FIG. 20 and the determination result shown in FIG. 21, allowing a staff member at the coffee shop to determine that the curves k11 to k13 are different from each other.

[0285] As described above, the curves k11 to k13 allow the coffee (WONDA), coffee (Craft Boss), and coffee (Gold Brew) to be distinguished from one another.

[0286] Therefore, when it is determined that the curves k11 to k13 are different from one another, the curve k11 is a curve for uniquely identifying coffee (WONDA), the curve k12 is a curve for uniquely identifying coffee (Craft Boss), and the curve k13 is a curve for uniquely identifying coffee (Gold Brew). The curve k11 represents the feature quantity based on the integral value for [Coffee (WONDA)], the curve k12 represents the feature quantity based on the integral value for [Coffee (Craft Boss)], and the curve k13 represents the feature quantity based on the integral value for [Coffee (Gold Brew)].

[0287] As a result, it is possible to determine whether the product is genuine or fake by using the curves k11 to k13, which are index curves. (4) Human urine Fig. 22 is a diagram showing integral spectra of human urine collected on different days. Referring to Fig. 22, each of curves k14 to k16 is a curve CUR created by the analysis device 2 using the above-mentioned method. Curves k14 to k16 represent integral spectra of urine from the same person, with curve k14 representing the integral spectrum of urine from the same person collected on the first day, curve k15 representing the integral spectrum of urine from the same person collected on the second day, and curve k16 representing the integral spectrum of urine from the same person collected on the third day.

[0288] Table 6 shows the measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k14 to k16.

[0289] [Table 6]

[0290] As shown in Table 6, the working electrode is made of diamond with a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scan range is -2.5 V to +2.5 V, the predetermined potential interval (integral value extraction potential) for calculating the integral value is 192 mV, and the potential scan rate is 600 mV / s.

[0291] FIG. 23 is a diagram showing the results of determining whether or not the curves k14 to k16 shown in FIG. 22 are different from one another.

[0292] Whether or not the curves k14 to k16 are different from one another is determined by determining whether or not two curves among the curves k14 to k16 are different for all combinations of two curves among the curves k14 to k16.

[0293] There are three combinations of two curves among the curves k14 to k16: (k14, k15), (k14, k16), and (k15, k16).

[0294] Therefore, FIG. 23 shows whether the two curves differ for the three cases (k14, k15), (k14, k16), and (k15, k16).

[0295] Referring to FIG. 23, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k14 and the plurality of integral values ​​on the curve k15 DF_k14,k15 is σ DF_k14,k15 = 38.45 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k14 and the multiple integral values ​​on the curve k16 DF_k14,k16 is σ DF_k14,k16 = 26.04 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k15 and the multiple integral values ​​on the curve k16 DF_k15,k16 is σ DF_k15,k16 =22.93(%).

[0296] As a result, the standard deviation of the difference σ DF_k14,k15 (=38.45(%)), standard deviation of the difference σ DF_k14,k16 (=26.04(%)), and the standard deviation of the difference σ DF_k15,k16 (=22.93(%)) are all within the threshold σ th (=15%).

[0297] Therefore, the two curves k14 and k15 are different, the two curves k14 and k16 are different, and the two curves k15 and k16 are different (see "◯" in FIG. 23). Therefore, the curves k14 to k16 are mutually different curves.

[0298] Curves k14 to k16 also have two peaks in common. However, curves k14 to k16 have different peak positions in the range of classes 5 to 10, and different peak positions in the range of classes 20 to 25.

[0299] Furthermore, the curves k14 to k16 have mutually different peak values ​​in the range of 5 to 10 and the range of 20 to 25.

[0300] Therefore, it can also be determined that the curves k14 to k16 are different from one another by determining whether the peak positions or peak values ​​are the same.

[0301] The analysis system 10 is placed in a hospital, and the analysis device 2 of the analysis system 10 has a display unit 26. The display unit 26 displays the curves k14 to k16 shown in FIG. 22 and the determination result shown in FIG. 23, allowing doctors and others at the hospital to determine that the curves k14 to k16 are different from each other.

[0302] As described above, curves k14 to k16 allow urine collected on the first day, urine collected on the second day, and urine collected on the third day from the same person to be distinguished from one another.

[0303] Therefore, when it is determined that the curves k14 to k16 are different from one another, for urine from the same person, the curve k14 is a curve for uniquely identifying urine collected on day 1, the curve k15 is a curve for uniquely identifying urine collected on day 2, and the curve k16 is a curve for uniquely identifying urine collected on day 3. The curve k14 represents the feature quantity based on the integral value for [urine collected on day 1], the curve k15 represents the feature quantity based on the integral value for [urine collected on day 2], and the curve k16 represents the feature quantity based on the integral value for [urine collected on day 3].

[0304] As a result, the curves k14 to k16, which are index curves, can be used to determine whether the product is genuine or fake. (5) Human saliva Fig. 24 is a diagram showing integral spectra of human saliva collected on different days. Referring to Fig. 24, each of curves k17 to k19 is a curve CUR created by the analysis device 2 using the above-mentioned method. Curves k17 to k19 represent integral spectra of the same human saliva, with curve k17 representing the integral spectrum of the same human saliva collected on the first day, curve k18 representing the integral spectrum of the same human saliva collected on the second day, and curve k19 representing the integral spectrum of the same human saliva collected on the third day.

[0305] The measurement conditions for the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k17 to k19 were the same as those shown in Table 6.

[0306] FIG. 25 is a diagram showing the results of determining whether or not the curves k17 to k19 shown in FIG. 24 are different from one another.

[0307] Whether or not the curves k17 to k19 are different from one another is determined by determining whether or not two curves among the curves k17 to k19 are different for all combinations of two curves among the curves k17 to k19.

[0308] There are three combinations of two curves among the curves k17 to k19: (k17, k18), (k17, k19), and (k18, k19).

[0309] Therefore, FIG. 25 shows whether the two curves differ for the three cases (k17, k18), (k17, k19), and (k18, k19).

[0310] Referring to FIG. 25, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k17 and the plurality of integral values ​​on the curve k18 DF_k17,k18 is σ DF_k17,k18 = 162.23 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k17 and the multiple integral values ​​on the curve k19 DF_k17,k19 is σ DF_k17,k19= 30.81 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k18 and the multiple integral values ​​on the curve k19 DF_k18,k19 is σ DF_k18,k19 =75.69(%).

[0311] As a result, the standard deviation of the difference σ DF_k17,k18 (=162.23(%)), standard deviation of the difference σ DF_k17,k19 (=30.81(%)), and the standard deviation of the difference σ DF_k18,k19 (=75.69(%)) is the threshold value σ th (=15%).

[0312] Therefore, the two curves k17 and k18 are different, the two curves k17 and k19 are different, and the two curves k18 and k19 are different (see "◯" in FIG. 25). Therefore, the curves k17 to k19 are mutually different curves.

[0313] Curves k17 to k19 also have two peaks in common. However, curves k17 to k19 have different peak positions in the range of classes 5 to 10, and different peak values ​​in the range of classes 5 to 10 and the range of classes 20 and above.

[0314] Therefore, it can also be determined that the curves k17 to k19 are different from one another by determining whether the peak positions or peak values ​​are the same.

[0315] The analysis system 10 is placed in a hospital, and the analysis device 2 of the analysis system 10 has a display unit 26. The display unit 26 displays the curves k17 to k19 shown in FIG. 24 and the judgment result shown in FIG. 25, allowing doctors and others at the hospital to determine that the curves k17 to k19 are different from each other.

[0316] As described above, curves k17 to k19 allow the same person's saliva collected on the first day, the same person's saliva collected on the second day, and the same person's saliva collected on the third day to be distinguished from one another.

[0317] Therefore, when it is determined that the curves k17 to k19 are different from one another, the curve k17 is a curve for uniquely identifying saliva collected on day 1 from the same person, the curve k18 is a curve for uniquely identifying saliva collected on day 2 from the same person, and the curve k19 is a curve for uniquely identifying saliva collected on day 3 from the same person. The curve k17 represents the feature quantity based on the integral value for the [saliva collected on day 1], the curve k18 represents the feature quantity based on the integral value for the [saliva collected on day 2], and the curve k19 represents the feature quantity based on the integral value for the [saliva collected on day 3].

[0318] As a result, the curves k17 to k19, which are index curves, can be used to determine whether the product is genuine or fake.

[0319] In the above (1) to (5), it has been explained that it is possible to determine that the multiple integrated value spectra created by the analysis device 2 for each of Calpis, wine, coffee, human urine, and human saliva are different from one another.

[0320] In addition, in (1) to (5) above, it was explained that it is also possible to determine whether multiple integral spectra differ from each other by determining whether the number of peaks, peak positions, and peak values ​​of the integral spectra are the same for each of Calpis, wine, coffee, human urine, and human saliva.

[0321] An example in which it can be determined that a plurality of integrated value spectra do not differ will now be described. Fig. 26 is a diagram showing two integrated value spectra for the same wine.

[0322] Curve k20 represents an integral spectrum created by the analysis device 2 based on the cyclic voltammogram CVG_a measured using sensor 11a, and curve k21 represents an integral spectrum created by the analysis device 2 based on the cyclic voltammogram CVG_b measured using sensor 11b instead of sensor 11a.

[0323] Each of the sensors 11a and 11b has the same configuration as the sensor 11 shown in Fig. 2. In addition, the working electrode, the reference electrode, and the counter electrode of the sensors 11a and 11b are made of the same material.

[0324] In measuring the cyclic voltammogram CVG in the sensor device 1, the cyclic voltammogram CVG_a was measured using the sensor 11a, and then the sensor 11b was attached to the measuring instrument 12 in place of the sensor 11a, and the cyclic voltammogram CVG_b was measured using the sensor 11b.

[0325] FIG. 27 is a diagram showing the result of determining whether the curves k20 and k21 shown in FIG. 26 are different from each other.

[0326] Referring to FIG. 27, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k20 and the plurality of integral values ​​on the curve k21 DF_k20,k21 is σ DF_k20,k21 =10.95(%).

[0327] As a result, the standard deviation of the difference σ DF_k20,k21 (=10.95(%)) is the threshold value σ th (=15%).

[0328] Therefore, the two curves k20 and k21 do not differ (see "x" in FIG. 27).

[0329] In this way, the analysis device 2 determines that the two curves k20 and k21 are not different, and therefore the generation of the integral spectrum by the analysis device 2 based on the cyclic voltammogram CVG is reproducible.

[0330] [Other indicators] FIG. 28 is a diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured while changing the potential scanning rate.

[0331] 28, curve k22 shows an integral spectrum created by analysis device 2 based on a cyclic voltammogram CVG_600mV / s measured at a potential scanning rate of 600mV / s, curve k23 shows an integral spectrum created by analysis device 2 based on a cyclic voltammogram CVG_500mV / s measured at a potential scanning rate of 500mV / s, and curve k24 shows an integral spectrum created by analysis device 2 based on a cyclic voltammogram CVG_300mV / s measured at a potential scanning rate of 300mV / s. The cyclic voltammograms CVG_600mV / s, CVG_500mV / s, and CVG_300mV / s are measured using the same analysis object.

[0332] Table 7 shows the measurement conditions for the cyclic voltammogram CVG_600 mV / s, cyclic voltammogram CVG_500 mV / s, and cyclic voltammogram CVG_300 mV / s.

[0333] [Table 7]

[0334] As shown in Table 7, the working electrode is made of diamond with a circular planar shape, and the counter electrode and reference electrode are made of rod-shaped gold. The potential scan range is -2.5 V to +2.5 V, the predetermined potential interval (integral value extraction potential) for calculating the integral value is 18.4 mV, and the potential scan rates are 600 mV / s, 500 mV / s, and 300 mV / s.

[0335] FIG. 29 is a diagram showing the result of determining whether the curves k22, k23, and k24 shown in FIG. 28 are different from one another.

[0336] Referring to FIG. 29, whether or not the curves k22 to k24 are different from one another is determined by determining whether or not any two curves among the curves k22 to k24 are different for all combinations of two curves among the curves k22 to k24.

[0337] There are three combinations of two curves among the curves k22 to k24: (k22, k23), (k22, k24), and (k23, k24).

[0338] Therefore, FIG. 29 shows whether or not the two curves differ for three combinations of two curves: ((k22, k23), (k22, k24), and (k23, k24).

[0339] Referring to FIG. 29, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k22 and the plurality of integral values ​​on the curve k23 DF_k22,k23 is σ DF_k22,k23 = 34.69 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k22 and the multiple integral values ​​on the curve k24 DF_k22,k24 is σ DF_k22,k24 = 51.23 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k23 and the multiple integral values ​​on the curve k24 DF_k23,k24 is σ DF_k23,k24 =30.26(%).

[0340] As a result, the standard deviation of the difference σ DF_k22,k23 (=34.69(%)), standard deviation of the difference σ DF_k22,k24 (=51.23(%)), and the standard deviation of the difference σ DF_k23,k24 (=30.26(%)) are all within the threshold σ th (=15%).

[0341] Therefore, the two curves k22 and k23 are different, the two curves k22 and k24 are different, and the two curves k23 and k24 are different (see "◯" in FIG. 29). Therefore, the curves k22 to k24 are mutually different curves.

[0342] Curves k22 to k24 also have three peaks in common. However, curves k22 to k24 have different peak positions in the range of 50 to 100 classes, and different peak positions in the range of 230 to 270 classes.

[0343] Furthermore, the curves k22 to k24 have mutually different peak values ​​in the range of 50 to 100 classes and the range of 230 to 270 classes.

[0344] Therefore, it can also be determined that the curves k22 to k24 are different from one another by determining whether the peak positions or peak values ​​are the same.

[0345] Fig. 30 is a first schematic diagram showing new index curves for identifying an analysis object. Referring to Fig. 30, curve k25 shows an integral spectrum in which the integral value in each class is the sum of the three integral values ​​in each class of the three curves k22 to k24 shown in Fig. 28.

[0346] A curve k26 represents an integral spectrum in which the sum of two integrals in each class of the two curves k22 and k23 shown in FIG. 28 is used as the integral in each class.

[0347] Furthermore, a curve k27 shows an integral spectrum in which the sum of two integrals in each class of the two curves k23 and k24 shown in FIG. 28 is used as the integral in each class.

[0348] FIG. 31 is a diagram showing the result of determining whether the curves k25, k26, and k27 shown in FIG. 30 are different from one another.

[0349] Whether or not the curves k25 to k27 are different from one another is determined by determining whether or not two curves among the curves k25 to k27 are different for all combinations of two curves among the curves k25 to k27.

[0350] There are three combinations of two curves among the curves k25 to k27: (k25, k26), (k25, k27), and (k26, k27).

[0351] Therefore, FIG. 31 shows whether the two curves differ for the three cases (k25, k26), (k25, k27), and (k26, k27).

[0352] Referring to FIG. 31, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k25 and the plurality of integral values ​​on the curve k26 DF_k25,k26 is σ DF_k25,k26 = 15.22 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k25 and the multiple integral values ​​on the curve k27 DF_k25,k27 is σ DF_k25,k27 = 27.76 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k26 and the multiple integral values ​​on the curve k27 DF_k26,k27 is σ DF_k26,k27 =30.92(%).

[0353] As a result, the standard deviation of the difference σ DF_k25,k26 (=15.22(%)), standard deviation of the difference σ DF_k25,k27 (=27.76(%)), and the standard deviation of the difference σ DF_k26,k27 (=30.92(%)) are all within the threshold σ th (=15%).

[0354] Therefore, the two curves k25 and k26 are different, the two curves k25 and k27 are different, and the two curves k26 and k27 are different (see "◯" in FIG. 31). Therefore, the curves k25 to k27 are mutually different curves.

[0355] In this case, curves k25, k26, and k27, which show the class dependency of the summation results obtained by adding the integral values ​​of at least two of curves k22, k23, and k24, which were created based on cyclic voltammograms CVG_600mV / s, CVG_500mV / s, and CVG_300mV / s measured by changing the potential scanning rate, can be used as new index curves.

[0356] When the curves k25, k26, and k27 are used as new index curves, the display unit 26 of the analysis device 2 displays the curves k25 to k27 shown in FIG. 30 and the determination results shown in FIG.

[0357] Fig. 32 is a second schematic diagram showing new index curves for identifying an analysis object. Referring to Fig. 32, curve k28 shows an integral spectrum in which the integral value in each class is the result of subtracting three integral values ​​in each class of the three curves k22 to k24 shown in Fig. 28.

[0358] A curve k29 shows an integral spectrum in which the integral value in each class is the result of subtracting two integral values ​​in each class of the two curves k22 and k23 shown in FIG.

[0359] Furthermore, a curve k30 shows an integral spectrum in which the integral value in each class is the result of subtracting two integral values ​​in each class of the two curves k23 and k24 shown in FIG.

[0360] FIG. 33 is a diagram showing the result of determining whether the curves k28, k29, and k30 shown in FIG. 32 are different from one another.

[0361] Whether or not the curves k28 to k30 are different from one another is determined by determining whether or not two of the curves k28 to k30 are different for all combinations of two of the curves k28 to k30.

[0362] There are three combinations of two curves among the curves k28 to k30: (k28, k29), (k28, k30), and (k29, k30).

[0363] Therefore, FIG. 33 shows whether or not the two curves differ for three combinations of two curves: (k28, k29), (k28, k30), and (k29, k30).

[0364] Referring to FIG. 33, the standard deviation σ of the difference between the plurality of integral values ​​on the curve k28 and the plurality of integral values ​​on the curve k29 DF_k28,k29 is σ DF_k28,k29 = 51.37 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k28 and the multiple integral values ​​on the curve k30 DF_k28,k30 is σ DF_k28,k30= 6415.85 (%), and the standard deviation σ of the difference between the multiple integral values ​​on the curve k29 and the multiple integral values ​​on the curve k30 DF_k29,k30 is σ DF_k29,k30 =87.68(%).

[0365] As a result, the standard deviation of the difference σ DF_k28,k29 (=51.37(%)), standard deviation of the difference σ DF_k28,k30 (=6415.85(%)), and the standard deviation of the difference σ DF_k29,k30 (=87.68(%)) is the threshold value σ th (=15%).

[0366] Therefore, the two curves k28 and k29 are different, the two curves k28 and k30 are different, and the two curves k29 and k30 are different (see "◯" in FIG. 33). Therefore, the curves k28 to k30 are mutually different curves.

[0367] In this case, curves k28, k29, and k30, which show the class dependency of the subtraction results obtained by subtracting the integral values ​​of at least two of curves k22, k23, and k24, which were created based on cyclic voltammograms CVG_600mV / s, CVG_500mV / s, and CVG_300mV / s measured by changing the potential scanning rate, can be used as new index curves.

[0368] In this way, a curve showing the class dependency of the sum or subtraction result of adding or subtracting the integral values ​​of at least two of the curves k22, k23, and k24 in each class can be used as a new index curve.

[0369] When subtracting the integral values ​​of two curves k22, k23, and k24 from the integral value of one of the remaining curves, for each class, the integral values ​​of curves k22 and k24 may be subtracted from the integral value of curve k23 (k23-k22-k24), or the integral values ​​of curves k22 and k23 may be subtracted from the integral value of curve k24 (k24-k22-k23).

[0370] Furthermore, when subtracting the integral value of one of curves k22, k23, and k24 from the integral value of another curve, for each class, the integral value of curve k22 may be subtracted from the integral value of curve 23 (k23-k22), the integral value of curve k22 may be subtracted from the integral value of curve 24 (k24-k22), the integral value of curve k24 may be subtracted from the integral value of curve 22 (k22-k24), or the integral value of curve k23 may be subtracted from the integral value of curve 24 (k24-k23).

[0371] When the curves k28, k29, and k30 are used as new index curves, the display unit 26 of the analysis device 2 displays the curves k28 to k30 shown in FIG. 32 and the determination results shown in FIG.

[0372] [Relationship between the number of integral values ​​and the standard deviation of the difference] (A) Calpis Figure 34 shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water when the number of integral values ​​is three.

[0373] 34, each of the curves k31 to k34 is a curve CUR created by the above-mentioned method by the analysis device 2. The curve k31 represents the integral spectrum of undiluted Calpis, the curve k32 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k33 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k34 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0374] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(= three integral values) for each of curves k31 to k34 were the same as those shown in Table 2 above, except that the specified potential range (= integral value extraction potential) of 192 mV was changed to 1536 mV.

[0375] As a result, in FIG. 34, each of classes 1 and 2 consists of a predetermined potential section (=integral value extraction potential) of 1536 mV, and class 3 consists of a predetermined potential section (=integral value extraction potential) of 1920 mV.

[0376] FIG. 35 is a diagram showing the results of determining whether or not the curves k31 to k34 shown in FIG. 34 are different from one another.

[0377] Whether or not the curves k31 to k34 are different from one another is determined by determining whether or not two curves among the curves k31 to k34 are different for all combinations of two curves among the curves k31 to k34.

[0378] There are six combinations of two curves among the curves k31 to k34: (k31, k32), (k31, k33), (k31, k34), (k32, k33), (k32, k34), and (k33, k34).

[0379] Therefore, FIG. 35 shows whether the two curves differ for six cases: (k31, k32), (k31, k33), (k31, k34), (k32, k33), (k32, k34), and (k33, k34).

[0380] 35, the standard deviation σ of the difference between the plurality of integral values ​​(=3 integral values) on the curve k31 and the plurality of integral values ​​(=3 integral values) on the curve k32 is DF_k31,k32 is 42.50(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k31 and the multiple integral values ​​(=3 integral values) on the curve k33 DF_k31,k33 is 71.86(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k31 and the multiple integral values ​​(=3 integral values) on the curve k34 DF_k31,k34 is 54.84(%).

[0381] In addition, the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k32 and the multiple integral values ​​(=3 integral values) on the curve k33 DF_k32,k33is 31.44(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k32 and the multiple integral values ​​(=3 integral values) on the curve k34 DF_k32,k34 is 15.15(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k33 and the multiple integral values ​​(=3 integral values) on the curve k34 DF_k33,k34 is 23.15(%).

[0382] As a result, the standard deviation of the difference σ DF_k31,k32 (=42.50(%)), standard deviation of the difference σ DF_k31,k33 (=71.86(%)), standard deviation of the difference σ DF_k31,k34 (=54.84(%)), standard deviation of the difference σ DF_k32,k33 (=31.44(%)), standard deviation of the difference σ DF_k32,k34 (=15.15(%)) and the standard deviation of the difference σ DF_k33,k34 (=23.15(%)) is the threshold value σ th (=15(%)) is greater than

[0383] Therefore, the two curves k31 and k32 are different, the two curves k31 and k33 are different, the two curves k31 and k34 are different, the two curves k32 and k33 are different, the two curves k32 and k34 are different, and the two curves k33 and k34 are different (see "◯" in FIG. 35). Therefore, the curves k31 to k34 are mutually different curves.

[0384] Figure 36 shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water when the number of integral values ​​is four.

[0385] 36, each of the curves k35 to k38 is a curve CUR created by the above-mentioned method by the analysis device 2. The curve k35 represents the integral spectrum of undiluted Calpis, the curve k36 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k37 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k38 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0386] The measurement conditions for the cyclic voltammograms that were used to calculate the multiple integral values ​​(=four integral values) for each of curves k35 to k38 were the same as those shown in Table 2 above, except that the specified potential range (=integral value extraction potential) of 192 mV was changed to 1152 mV.

[0387] As a result, in FIG. 36, each of classes 1, 2, and 3 consists of a predetermined potential section (=integral value extraction potential) of 1152 mV, and class 4 consists of a predetermined potential section (=integral value extraction potential) of 1536 mV.

[0388] FIG. 37 is a diagram showing the results of determining whether or not the curves k35 to k38 shown in FIG. 36 are different from one another.

[0389] Whether or not the curves k35 to k38 are different from one another is determined by determining whether or not two curves among the curves k35 to k38 are different for all combinations of two curves among the curves k35 to k38.

[0390] There are six combinations of two curves among the curves k35 to k38: (k35, k36), (k35, k37), (k35, k38), (k36, k37), (k36, k38), and (k37, k38).

[0391] Therefore, FIG. 37 shows whether the two curves differ for six cases: (k35, k36), (k35, k37), (k35, k38), (k36, k37), (k36, k38), and (k37, k38).

[0392] Referring to FIG. 37, the standard deviation σ of the difference between the plurality of integral values ​​(=4 integral values) on the curve k35 and the plurality of integral values ​​(=4 integral values) on the curve k36 DF_k35,k36 is 48.30(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k35 and the multiple integral values ​​(=4 integral values) on the curve k37 DF_k35,k37 is 56.45(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k35 and the multiple integral values ​​(=4 integral values) on the curve k38 DF_k35,k38 is 53.12(%).

[0393] In addition, the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k36 and the multiple integral values ​​(=4 integral values) on the curve k37 DF_k36,k37 is 12.30(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k36 and the multiple integral values ​​(=4 integral values) on the curve k38 DF_k36,k38 is 6.86(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k37 and the multiple integral values ​​(=4 integral values) on the curve k38 DF_k37,k38 is 11.70(%).

[0394] As a result, the standard deviation of the difference σ DF_k35,k36 (=48.30(%)), standard deviation of the difference σ DF_k35,k37 (=56.45(%)) and the standard deviation of the difference σ DF_k35,k38 (=53.12(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k36,k37 (=12.30(%)), standard deviation of the difference σ DF_k36,k38 (=6.86(%)) and the standard deviation of the difference σ DF_k37,k38 (=11.70(%)) is the threshold σ th (=15(%)).

[0395] Therefore, the two curves k35, k36 are different, the two curves k35, k37 are different, the two curves k35, k38 are different, the two curves k36, k37 are not different, the two curves k36, k38 are not different, and the two curves k37, k38 are not different.

[0396] Therefore, when the number of integral values ​​is four, the curves k35 to k38 are not mutually different curves.

[0397] It is possible to determine that the two curves k35 and k36, the two curves k35 and k37, and the two curves k35 and k38 are different.

[0398] Figure 38 shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water when the number of integral values ​​is five.

[0399] 38, each of the curves k43 to k46 is a curve CUR created by the above-mentioned method by the analysis device 2. The curve k43 represents the integral spectrum of undiluted Calpis, the curve k44 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k45 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k46 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0400] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=5 integral values) for each of curves k43 to k46 were the same as those shown in Table 2 above, except that the specified potential range (=integral value extraction potential) of 192 mV was changed to 960 mV.

[0401] As a result, in FIG. 38, each of classes 1, 2, 3, and 4 consists of a predetermined potential section (=integral value extraction potential) of 960 mV, and class 5 consists of a predetermined potential section (=integral value extraction potential) of 1152 mV.

[0402] FIG. 39 is a diagram showing the results of determining whether or not the curves k43 to k46 shown in FIG. 38 are different from one another.

[0403] Whether or not the curves k43 to k46 are different from one another is determined by determining whether or not two curves among the curves k43 to k46 are different for all combinations of two curves among the curves k43 to k46.

[0404] There are six combinations of two curves among the curves k43 to k46: (k43, k44), (k43, k45), (k43, k46), (k44, k45), (k44, k46), and (k45, k46).

[0405] Therefore, FIG. 39 shows whether the two curves differ for six cases: (k43, k44), (k43, k45), (k43, k46), (k44, k45), (k44, k46), and (k45, k46).

[0406] Referring to FIG. 39, the standard deviation σ of the difference between the plurality of integral values ​​(=5 integral values) on the curve k43 and the plurality of integral values ​​(=5 integral values) on the curve k44 DF_k43,k44 is 39.20(%), and the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k43 and the multiple integral values ​​(=5 integral values) on the curve k45 DF_k43,k45 is 44.75(%), and the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k43 and the multiple integral values ​​(=5 integral values) on the curve k46 DF_k43,k46 is 39.30(%).

[0407] In addition, the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k44 and the multiple integral values ​​(=5 integral values) on the curve k45 DF_k44,k45 is 13.02(%), and the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k44 and the multiple integral values ​​(=5 integral values) on the curve k46 DF_k44,k46is 10.04(%), and the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k45 and the multiple integral values ​​(=5 integral values) on the curve k46 DF_k45,k46 is 19.83(%).

[0408] As a result, the standard deviation of the difference σ DF_k43,k44 (=39.20(%)), standard deviation of the difference σ DF_k43,k45 (=44.75(%)), standard deviation of the difference σ DF_k43,k46 (=39.30(%)) and the standard deviation of the difference σ DF_k45,k46 (=19.83(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k44,k45 (=13.02(%)) and the standard deviation of the difference σ DF_k44,k46 (=10.04(%)) is the threshold σ th (=15(%)).

[0409] Therefore, the two curves k43, k44 are different, the two curves k43, k45 are different, the two curves k43, k46 are different, the two curves k45, k46 are different, the two curves k44, k45 are not different, and the two curves k44, k46 are not different.

[0410] Therefore, when the number of integral values ​​is five, the curves k43 to k46 are not mutually different curves.

[0411] Figure 40 shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water when the number of integral values ​​is 6.

[0412] 40, each of the curves k47 to k50 is a curve CUR created by the above-mentioned method by the analysis device 2. The curve k47 represents the integral spectrum of undiluted Calpis, the curve k48 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k49 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k50 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0413] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=6 integral values) for each of curves k47 to k50 were the same as those shown in Table 2 above, except that the specified potential interval (=integral value extraction potential) of 192 mV was changed to 768 mV.

[0414] As a result, in FIG. 40, each of classes 1, 2, 3, 4, and 5 consists of a predetermined potential section (=integral value extraction potential) of 768 mV, and class 6 consists of a predetermined potential section (=integral value extraction potential) of 1152 mV.

[0415] FIG. 41 is a diagram showing the results of determining whether or not the curves k47 to k50 shown in FIG. 40 are different from one another.

[0416] Whether or not the curves k47 to k50 are different from one another is determined by determining whether or not two curves among the curves k47 to k50 are different for all combinations of two curves among the curves k47 to k50.

[0417] There are six combinations of two curves among the curves k47 to k50: (k47, k48), (k47, k49), (k47, k50), (k48, k49), (k48, k50), and (k49, k50).

[0418] Therefore, FIG. 41 shows whether the two curves differ for six cases: (k47, k48), (k47, k49), (k47, k50), (k48, k49), (k48, k50), and (k49, k50).

[0419] Referring to FIG. 41, the standard deviation σ of the difference between the plurality of integral values ​​(=6 integral values) on the curve k47 and the plurality of integral values ​​(=6 integral values) on the curve k48 is DF_k47,k48 is 38.05(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k47 and the multiple integral values ​​(=6 integral values) on the curve k49 DF_k47,k49 is 45.94(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k47 and the multiple integral values ​​(=6 integral values) on the curve k50 DF_k47,k50 is 40.25(%).

[0420] In addition, the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k48 and the multiple integral values ​​(=6 integral values) on the curve k49 DF_k48,k49 is 16.90(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k48 and the multiple integral values ​​(=6 integral values) on the curve k50 DF_k48,k50 is 11.89(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k49 and the multiple integral values ​​(=6 integral values) on the curve k50 DF_k49,k50 is 18.04(%).

[0421] As a result, the standard deviation of the difference σ DF_k47,k48 (=38.05(%)), standard deviation of the difference σ DF_k47,k49 (=45.94(%)), standard deviation of the difference σ DF_k47,k50 (=40.25(%)), standard deviation of the difference σ DF_k48,k49 (=16.90(%)) and the standard deviation of the difference σ DF_k49,k50 (=18.04(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k48,k50 (=11.89(%)) is the threshold σ th (=15(%)).

[0422] Therefore, the two curves k47, k48 are different, the two curves k47, k49 are different, the two curves k47, k50 are different, the two curves k48, k49 are different, the two curves k49, k50 are different, and the two curves k48, k50 are not different.

[0423] Therefore, when the number of integral values ​​is six, the curves k47 to k50 are not mutually different curves.

[0424] Figure 42 shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water when the number of integral values ​​is 8.

[0425] 42, each of the curves k51 to k54 is a curve CUR created by the above-mentioned method by the analysis device 2. The curve k51 represents the integral spectrum of undiluted Calpis, the curve k52 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k53 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k54 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0426] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=8 integral values) for each of curves k51 to k54 were the same as those shown in Table 2 above, except that the specified potential range (=integral value extraction potential) of 192 mV was changed to 576 mV.

[0427] As a result, in FIG. 42, each of classes 1, 2, 3, 4, 5, 6, and 7 consists of a predetermined potential section of 576 mV (=integral value extraction potential), and class 8 consists of a predetermined potential section of 960 mV (=integral value extraction potential).

[0428] FIG. 43 is a diagram showing the results of determining whether or not the curves k51 to k54 shown in FIG. 42 are different from one another.

[0429] Whether or not the curves k51 to k54 are different from one another is determined by determining whether or not two curves among the curves k51 to k54 are different for all combinations of two curves among the curves k51 to k54.

[0430] There are six combinations of two curves among the curves k51 to k54: (k51, k52), (k51, k53), (k51, k54), (k52, k53), (k52, k54), and (k53, k54).

[0431] Therefore, FIG. 43 shows whether the two curves differ for six cases: (k51, k52), (k51, k53), (k51, k54), (k52, k53), (k52, k54), and (k53, k54).

[0432] Referring to FIG. 43, the standard deviation σ of the difference between the plurality of integral values ​​(=8 integral values) on the curve k51 and the plurality of integral values ​​(=8 integral values) on the curve k52 DF_k51,k52 is 87.13(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k51 and the multiple integral values ​​(=8 integral values) on the curve k53 DF_k51,k53 is 88.91(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k51 and the multiple integral values ​​(=8 integral values) on the curve k54 DF_k51,k54 is 87.77(%).

[0433] In addition, the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k52 and the multiple integral values ​​(=8 integral values) on the curve k53 DF_k52,k53 is 32.87%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k52 and the multiple integral values ​​(=8 integral values) on the curve k54 DF_k52,k54 is 24.08(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k53 and the multiple integral values ​​(=8 integral values) on the curve k54 DF_k53,k54 is 23.98(%).

[0434] As a result, the standard deviation of the difference σ DF_k51,k52 (=87.13(%)), standard deviation of the difference σ DF_k51,k53 (=88.91(%)), standard deviation of the difference σ DF_k51,k54 (=87.77(%)), standard deviation of the difference σ DF_k52,k53 (=32.87(%)), standard deviation of the difference σ DF_k52,k54 (=24.08(%)) and the standard deviation of the difference σ DF_k53,k54 (=23.98(%)) is the threshold value σ th (=15(%)) is greater than

[0435] Therefore, the two curves k51 and k52 are different, the two curves k51 and k53 are different, the two curves k51 and k54 are different, the two curves k52 and k53 are different, the two curves k52 and k54 are different, and the two curves k53 and k54 are different (see "◯" in FIG. 43). Therefore, the curves k51 to k54 are mutually different curves.

[0436] Figure 44 shows the integral spectra for undiluted Calpis, Calpis diluted 2-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold with tap water when the number of integral values ​​is 13.

[0437] 44, each of the curves k55 to k58 is a curve CUR created by the above-mentioned method by the analysis device 2. The curve k55 represents the integral spectrum of undiluted Calpis, the curve k56 represents the integral spectrum of Calpis diluted two-fold with tap water, the curve k57 represents the integral spectrum of Calpis diluted three-fold with tap water, and the curve k58 represents the integral spectrum of Calpis diluted four-fold with tap water.

[0438] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=13 integral values) for each of curves k55 to k58 were the same as those shown in Table 2 above, except that the specified potential range (=integral value extraction potential) of 192 mV was changed to 384 mV.

[0439] As a result, in FIG. 44, each of classes 1 to 13 is made up of a predetermined potential section (=integral value extraction potential) of 384 mV.

[0440] FIG. 45 is a diagram showing the results of determining whether or not the curves k55 to k58 shown in FIG. 44 are different from one another.

[0441] Whether or not the curves k55 to k58 are different from one another is determined by determining whether or not two curves among the curves k55 to k58 are different for all combinations of two curves among the curves k55 to k58.

[0442] There are six combinations of two curves among the curves k55 to k58: (k55, k56), (k55, k57), (k55, k58), (k56, k57), (k56, k58), and (k57, k58).

[0443] Therefore, FIG. 45 shows whether the two curves differ for six cases: (k55, k56), (k55, k57), (k55, k58), (k56, k57), (k56, k58), and (k57, k58).

[0444] Referring to FIG. 45, the standard deviation σ of the difference between the plurality of integral values ​​(=13 integral values) on the curve k55 and the plurality of integral values ​​(=13 integral values) on the curve k56 is DF_k55,k56 is 45.88(%), and the standard deviation σ of the difference between the multiple integral values ​​(=13 integral values) on the curve k55 and the multiple integral values ​​(=13 integral values) on the curve k57 DF_k55,k57 is 75.41(%), and the standard deviation σ of the difference between the multiple integral values ​​(=13 integral values) on the curve k55 and the multiple integral values ​​(=13 integral values) on the curve k58 DF_k55,k58 is 71.80(%).

[0445] In addition, the standard deviation σ of the difference between the multiple integral values ​​(=13 integral values) on the curve k56 and the multiple integral values ​​(=13 integral values) on the curve k57 DF_k56,k57is 55.94%), and the standard deviation σ of the difference between the multiple integral values ​​(=13 integral values) on curve k56 and the multiple integral values ​​(=13 integral values) on curve k58 DF_k56,k58 is 50.10(%), and the standard deviation σ of the difference between the multiple integral values ​​(=13 integral values) on the curve k57 and the multiple integral values ​​(=13 integral values) on the curve k58 DF_k57,k58 is 58.32(%).

[0446] As a result, the standard deviation of the difference σ DF_k55,k56 (=45.88(%)), standard deviation of the difference σ DF_k55,k57 (=75.41(%)), standard deviation of the difference σ DF_k55,k58 (=71.80(%)), standard deviation of the difference σ DF_k56,k57 (=55.94(%)), standard deviation of the difference σ DF_k56,k58 (=50.10(%)), and the standard deviation of the difference σ DF_k57,k58 (=58.32(%)) is the threshold value σ th (=15(%)) is greater than

[0447] Therefore, the two curves k55 and k56 are different, the two curves k55 and k57 are different, the two curves k55 and k58 are different, the two curves k56 and k57 are different, the two curves k56 and k58 are different, and the two curves k57 and k58 are different (see "◯" in FIG. 45). Therefore, the curves k55 to k58 are mutually different curves.

[0448] FIG. 14 shows the integral spectra of Calpis undiluted solution, Calpis diluted 2 times with tap water, Calpis diluted 3 times with tap water, and Calpis diluted 4 times with tap water when the number of integral values ​​(=number of classes) is 26, and FIG. 15 shows the standard deviation σ of the difference. DF_k1,k2 (=49.37(%)), standard deviation of the difference σ DF_k1,k3 (=69.61(%)), standard deviation of the difference σ DF_k1,k4 (=59.03(%)), standard deviation of the difference σ DF_k2,k3 (=44.49(%)), standard deviation of the difference σ DF_k2,k4 (=25.14(%)) and the standard deviation of the difference σDF_k3,k4 (=48.83(%)) are all within the threshold σ th (=15(%)).

[0449] Therefore, when the number of integral values ​​is 3, 8, 13, and 26, it is possible to obtain integral spectrum (= curve CUR) that uniquely identifies undiluted Calpis, Calpis diluted 2 times with tap water, Calpis diluted 3 times with tap water, and Calpis diluted 4 times with tap water.

[0450] In other words, when the number of integral values ​​is three, curve k31 is an integral spectrum for uniquely identifying undiluted Calpis, curve k32 is an integral spectrum for uniquely identifying Calpis diluted two-fold with tap water, curve k33 is an integral spectrum for uniquely identifying Calpis diluted three-fold with tap water, and curve k34 is an integral spectrum for uniquely identifying Calpis diluted four-fold with tap water.

[0451] Furthermore, when the number of integral values ​​is eight, curve k51 is an integral spectrum for uniquely identifying undiluted Calpis, curve k52 is an integral spectrum for uniquely identifying Calpis diluted two-fold with tap water, curve k53 is an integral spectrum for uniquely identifying Calpis diluted three-fold with tap water, and curve k54 is an integral spectrum for uniquely identifying Calpis diluted four-fold with tap water.

[0452] Furthermore, when the number of integral values ​​is 13, curve k55 is an integral spectrum for uniquely identifying undiluted Calpis, curve k56 is an integral spectrum for uniquely identifying Calpis diluted two-fold with tap water, curve k57 is an integral spectrum for uniquely identifying Calpis diluted three-fold with tap water, and curve k58 is an integral spectrum for uniquely identifying Calpis diluted four-fold with tap water.

[0453] Furthermore, when the number of integral values ​​is 26, as described above, curve k1 is an integral spectrum for uniquely identifying undiluted Calpis, curve k2 is an integral spectrum for uniquely identifying Calpis diluted two-fold with tap water, curve k3 is an integral spectrum for uniquely identifying Calpis diluted three-fold with tap water, and curve k4 is an integral spectrum for uniquely identifying Calpis diluted four-fold with tap water.

[0454] In this case, the integral spectrum (= index curve) for uniquely identifying the undiluted Calpis consists of curve k31 when the number of integral values ​​is 3, curve k51 when the number of integral values ​​is 8, curve k55 when the number of integral values ​​is 13, and curve k1 when the number of integral values ​​is 26.

[0455] In addition, the integral spectrum (= index curve) for uniquely identifying Calpis diluted 2 times with tap water consists of curve k32 when the number of integral values ​​is 3, curve k52 when the number of integral values ​​is 8, curve k56 when the number of integral values ​​is 13, and curve k2 when the number of integral values ​​is 26.

[0456] Furthermore, the integral spectrum (= index curve) for uniquely identifying Calpis diluted 3 times with tap water consists of curve k33 when the number of integrals is 3, curve k53 when the number of integrals is 8, curve k57 when the number of integrals is 13, and curve k3 when the number of integrals is 26.

[0457] Furthermore, the integral spectrum (= index curve) for uniquely identifying Calpis diluted 4 times with tap water consists of curve k34 when the number of integrals is 3, curve k54 when the number of integrals is 8, curve k58 when the number of integrals is 13, and curve k4 when the number of integrals is 26.

[0458] Therefore, in general, the integral spectrum (= index curve) for uniquely identifying undiluted Calpis is composed of multiple (= 4) index curves (= curves k31, k51, k55, k1) with different numbers of integral values, and the integral spectrum (= index curve) for uniquely identifying Calpis diluted 2 times with tap water is composed of multiple (= 4) index curves (= curves k32, k52, k56, k2) with different numbers of integral values. The integral spectrum (= index curve) for uniquely identifying Calpis diluted 3 times with tap water consists of multiple (= 4) index curves (= curves k33, k53, k57, k3) with different numbers of integral values, and the integral spectrum (= index curve) for uniquely identifying Calpis diluted 4 times with tap water consists of multiple (= 4) index curves (= curves k34, k54, k58, k4) with different numbers of integral values.

[0459] It was found that the minimum number of integral values ​​required to obtain an integral spectrum that can uniquely identify each of undiluted Calpis, Calpis diluted 2 times with tap water, Calpis diluted 3 times with tap water, and Calpis diluted 4 times with tap water is three.

[0460] (B) Red wine 1 FIG. 46 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is three.

[0461] 46, each of the curves k62 to k64 is a curve CUR created by the above-described method by the analysis device 2. The curve k62 represents the integral spectrum of a red wine (Chile 2020), the curve k63 represents the integral spectrum of a red wine (Australia 2010), and the curve k64 represents the integral spectrum of a red wine (France 2013).

[0462] The measurement conditions for the cyclic voltammograms that were used to calculate the multiple integral values ​​(= three integral values) for each of curves k62 to k64 were the same as those shown in Table 3 above, except that the specified potential range (= integral value extraction potential) of 17.6 mV was changed to 1654.4 mV.

[0463] As a result, in FIG. 46, each of classes 1 and 2 consists of a predetermined potential section (=integral value extraction potential) of 1654.4 mV, and class 3 consists of a predetermined potential section (=integral value extraction potential) of 1689.6 mV.

[0464] FIG. 47 is a diagram showing the results of determining whether or not the curves k62 to k64 shown in FIG. 46 are different from one another.

[0465] Whether or not the curves k62 to k64 are different from one another is determined by determining whether or not two of the curves k62 to k64 are different for all combinations of two of the curves k62 to k64.

[0466] There are three combinations of two curves among the curves k62 to k64: (k62, k63), (k62, k64), and (k63, k64).

[0467] Therefore, FIG. 47 shows whether the two curves differ for the three cases (k62, k63), (k62, k64), and (k63, k64).

[0468] Referring to FIG. 47, the standard deviation σ of the difference between the plurality of integral values ​​(=3 integral values) on the curve k62 and the plurality of integral values ​​(=3 integral values) on the curve k63 is DF_k62,k63 is 19.05(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k62 and the multiple integral values ​​(=3 integral values) on the curve k64 DF_k62,k64 is 71.82(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k63 and the multiple integral values ​​(=3 integral values) on the curve k64 DF_k63,k64 is 86.34(%).

[0469] As a result, the standard deviation of the difference σ DF_k62,k63 (=19.05(%)), standard deviation of the difference σ DF_k62,k64 (=71.82(%)) and the standard deviation of the difference σ DF_k63,k64 (=86.34(%)) is the threshold value σ th (=15(%)) is greater than

[0470] Therefore, the two curves k62 and k63 are different, the two curves k62 and k64 are different, and the two curves k63 and k64 are different (see "◯" in FIG. 47). Therefore, the curves k62 to k64 are mutually different curves.

[0471] FIG. 48 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is four.

[0472] 48, each of the curves k65 to k67 is a curve CUR created by the above-described method by the analysis device 2. The curve k65 represents the integral spectrum of a red wine (Chile 2020), the curve k66 represents the integral spectrum of a red wine (Australia 2010), and the curve k67 represents the integral spectrum of a red wine (France 2013).

[0473] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=four integral values) for each of curves k65 to k67 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 1249.6 mV.

[0474] As a result, in FIG. 48, each of classes 1 to 4 is made up of a predetermined potential section (=integral value extraction potential) of 1249.6 mV.

[0475] FIG. 49 is a diagram showing the results of determining whether or not the curves k65 to k67 shown in FIG. 48 are different from one another.

[0476] Whether or not the curves k65 to k67 are different from one another is determined by determining whether or not two curves among the curves k65 to k67 are different for all combinations of two curves among the curves k65 to k67.

[0477] There are three combinations of two curves among the curves k65 to k67: (k65, k66), (k65, k67), and (k66, k67).

[0478] Therefore, FIG. 49 shows whether the two curves differ for the three cases (k65, k66), (k65, k67), and (k66, k67).

[0479] Referring to FIG. 49, the standard deviation σ of the difference between the plurality of integral values ​​(=4 integral values) on the curve k65 and the plurality of integral values ​​(=4 integral values) on the curve k66 is DF_k65,k66 is 19.34(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k65 and the multiple integral values ​​(=4 integral values) on the curve k67 DF_k65,k67 is 80.96(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k66 and the multiple integral values ​​(=4 integral values) on the curve k67 DF_k66,k67 is 91.77(%).

[0480] As a result, the standard deviation of the difference σ DF_k65,k66 (=19.34(%)), standard deviation of the difference σ DF_k65,k67 (=80.96(%)) and the standard deviation of the difference σ DF_k66,k67 (=91.77(%)) are all within the threshold σ th (=15(%)) is greater than

[0481] Therefore, the two curves k65 and k66 are different, the two curves k65 and k67 are different, and the two curves k66 and k67 are different (see "◯" in FIG. 49). Therefore, the curves k65 to k67 are mutually different curves.

[0482] FIG. 50 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is six.

[0483] 50, each of the curves k68 to k70 is a curve CUR created by the above-described method by the analysis device 2. The curve k68 represents the integral spectrum of a red wine (Chile 2020), the curve k69 represents the integral spectrum of a red wine (Australia 2010), and the curve k70 represents the integral spectrum of a red wine (France 2013).

[0484] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=6 integral values) for each of curves k68 to k70 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 827.2 mV.

[0485] As a result, in FIG. 50, each of classes 1 to 5 consists of a predetermined potential section (=integral value extraction potential) of 827.2 mV, and class 6 consists of 862.4 mV.

[0486] FIG. 51 is a diagram showing the results of determining whether or not the curves k68 to k70 shown in FIG. 50 are different from one another.

[0487] Whether or not the curves k68 to k70 are different from one another is determined by determining whether or not two of the curves k68 to k70 are different for all combinations of two of the curves k68 to k70.

[0488] There are three combinations of two of the curves k68 to k70: (k68, k69), (k68, k70), and (k69, k70).

[0489] Therefore, FIG. 51 shows whether the two curves differ for the three cases (k68, k69), (k68, k70), and (k69, k70).

[0490] Referring to FIG. 51, the standard deviation σ of the difference between the plurality of integral values ​​(=6 integral values) on the curve k68 and the plurality of integral values ​​(=6 integral values) on the curve k69 is DF_k68,k69 is 27.33(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k68 and the multiple integral values ​​(=6 integral values) on the curve k70 DF_k68,k70 is 70.78(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k69 and the multiple integral values ​​(=6 integral values) on the curve k70 DF_k69,k70 is 80.57(%).

[0491] As a result, the standard deviation of the difference σ DF_k68,k69 (=27.33(%)), standard deviation of the difference σ DF_k68,k70 (=70.78(%)) and the standard deviation of the difference σ DF_k69,k70 (=80.57(%)) are all within the threshold σ th (=15(%)) is greater than

[0492] Therefore, the two curves k68 and k69 are different, the two curves k68 and k70 are different, and the two curves k69 and k70 are different (see "◯" in FIG. 51). Therefore, the curves k68 to k70 are mutually different curves.

[0493] FIG. 52 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is eight.

[0494] 52, each of the curves k71 to k73 is a curve CUR created by the above-described method by the analysis device 2. The curve k71 represents the integral spectrum of a red wine (Chile 2020), the curve k72 represents the integral spectrum of a red wine (Australia 2010), and the curve k73 represents the integral spectrum of a red wine (France 2013).

[0495] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=8 integral values) for each of curves k71 to k73 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 616 mV.

[0496] As a result, in FIG. 52, each of classes 1 to 7 consists of a predetermined potential section (=integral value extraction potential) of 616 mV, and class 8 consists of 686.4 mV.

[0497] FIG. 53 is a diagram showing the results of determining whether or not the curves k71 to k73 shown in FIG. 52 are different from one another.

[0498] Whether or not the curves k71 to k73 are different from one another is determined by determining whether or not two curves among the curves k71 to k73 are different for all combinations of two curves among the curves k71 to k73.

[0499] There are three combinations of two curves among the curves k71 to k73: (k71, k72), (k71, k73), and (k72, k73).

[0500] Therefore, FIG. 53 shows whether or not the two curves differ for the three cases (k71, k72), (k71, k73), and (k72, k73).

[0501] Referring to FIG. 53, the standard deviation σ of the difference between the plurality of integral values ​​(=8 integral values) on the curve k71 and the plurality of integral values ​​(=8 integral values) on the curve k72 DF_k71,k72is 34.73(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k71 and the multiple integral values ​​(=8 integral values) on the curve k73 DF_k71,k73 is 67.65(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k72 and the multiple integral values ​​(=8 integral values) on the curve k73 DF_k72,k73 is 77.44(%).

[0502] As a result, the standard deviation of the difference σ DF_k71,k72 (=34.73(%)), standard deviation of the difference σ DF_k71,k73 (=67.65(%)) and the standard deviation of the difference σ DF_k72,k73 (=77.44(%)) are all within the threshold σ th (=15(%)) is greater than

[0503] Therefore, the two curves k71 and k72 are different, the two curves k71 and k73 are different, and the two curves k72 and k73 are different (see "◯" in FIG. 53). Therefore, the curves k71 to k73 are mutually different curves.

[0504] FIG. 54 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 10.

[0505] 54, each of the curves k74 to k76 is a curve CUR created by the above-described method by the analysis device 2. The curve k74 represents the integral spectrum of a red wine (Chile 2020), the curve k75 represents the integral spectrum of a red wine (Australia 2010), and the curve k76 represents the integral spectrum of a red wine (France 2013).

[0506] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=10 integral values) for each of curves k74 to k76 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 492.8 mV.

[0507] As a result, in FIG. 54, each of classes 1 to 9 consists of a predetermined potential section (=integral value extraction potential) of 492.8 mV, and class 10 consists of 563.2 mV.

[0508] FIG. 55 is a diagram showing the results of determining whether or not the curves k74 to k76 shown in FIG. 54 are different from one another.

[0509] Whether or not the curves k74 to k76 are different from one another is determined by determining whether or not two of the curves k74 to k76 are different for all combinations of two of the curves k74 to k76.

[0510] There are three combinations of two curves among the curves k74 to k76: (k74, k75), (k74, k76), and (k75, k76).

[0511] Therefore, FIG. 55 shows whether the two curves differ for the three cases (k74, k75), (k74, k76), and (k75, k76).

[0512] Referring to FIG. 55, the standard deviation σ of the difference between the plurality of integral values ​​(=10 integral values) on the curve k74 and the plurality of integral values ​​(=10 integral values) on the curve k75 DF_k74,k75 is 45.00(%), and the standard deviation σ of the difference between the multiple integral values ​​(=10 integral values) on the curve k74 and the multiple integral values ​​(=10 integral values) on the curve k76 DF_k74,k76 is 77.58(%), and the standard deviation σ of the difference between the multiple integral values ​​(=10 integral values) on the curve k75 and the multiple integral values ​​(=10 integral values) on the curve k76 DF_k75,k76 is 89.41(%).

[0513] As a result, the standard deviation of the difference σ DF_k74,k75 (=45.00(%)), standard deviation of the difference σ DF_k74,k76 (=77.58(%)) and the standard deviation of the difference σ DF_k75,k76 (=89.41(%)) are all within the threshold σ th (=15(%)) is greater than

[0514] Therefore, the two curves k74 and k75 are different, the two curves k74 and k76 are different, and the two curves k75 and k76 are different (see "◯" in FIG. 55). Therefore, the curves k74 to k76 are mutually different curves.

[0515] FIG. 56 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 14.

[0516] 56, each of the curves k77 to k79 is a curve CUR created by the above-described method by the analysis device 2. The curve k77 represents the integral spectrum of a red wine (Chile 2020), the curve k78 represents the integral spectrum of a red wine (Australia 2010), and the curve k79 represents the integral spectrum of a red wine (France 2013).

[0517] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=14 integral values) for each of curves k77 to k79 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 352 mV.

[0518] As a result, in FIG. 56, each of classes 1 to 13 consists of a predetermined potential section (=integral value extraction potential) of 352 mV, and class 14 consists of 422.4 mV.

[0519] FIG. 57 is a diagram showing the results of determining whether or not the curves k77 to k79 shown in FIG. 56 are different from one another.

[0520] Whether or not the curves k77 to k79 are different from one another is determined by determining whether or not two curves among the curves k77 to k79 are different for all combinations of two curves among the curves k77 to k79.

[0521] There are three combinations of two curves among the curves k77 to k79: (k77, k78), (k77, k79), and (k78, k79).

[0522] Therefore, FIG. 57 shows whether the two curves differ for the three cases (k77, k78), (k77, k79), and (k78, k79).

[0523] Referring to FIG. 57, the standard deviation σ of the difference between the plurality of integral values ​​(=14 integral values) on the curve k77 and the plurality of integral values ​​(=14 integral values) on the curve k78 is DF_k77,k78 is 61.41(%), and the standard deviation σ of the difference between the multiple integral values ​​(=14 integral values) on the curve k77 and the multiple integral values ​​(=14 integral values) on the curve k79 DF_k77,k79 is 78.91(%), and the standard deviation σ of the difference between the multiple integral values ​​(=14 integral values) on the curve k78 and the multiple integral values ​​(=14 integral values) on the curve k79 DF_k78,k79 is 91.76(%).

[0524] As a result, the standard deviation of the difference σ DF_k77,k78 (=61.41(%)), standard deviation of the difference σ DF_k77,k79 (=78.91(%)) and the standard deviation of the difference σ DF_k78,k79 (=91.76(%)) are all within the threshold σ th (=15(%)) is greater than

[0525] Therefore, the two curves k77 and k78 are different, the two curves k77 and k79 are different, and the two curves k78 and k79 are different (see "◯" in FIG. 57). Therefore, the curves k77 to k79 are mutually different curves.

[0526] FIG. 58 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 28.

[0527] 58, each of the curves k80 to k82 is a curve CUR created by the above-described method by the analysis device 2. The curve k80 represents the integral spectrum of a red wine (Chile 2020), the curve k81 represents the integral spectrum of a red wine (Australia 2010), and the curve k82 represents the integral spectrum of a red wine (France 2013).

[0528] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=28 integral values) for each of curves k80 to k82 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 176 mV.

[0529] As a result, in FIG. 58, each of classes 1 to 27 consists of a predetermined potential section (=integral value extraction potential) of 176 mV, and class 28 consists of 246.4 mV.

[0530] FIG. 59 is a diagram showing the results of determining whether or not the curves k80 to k82 shown in FIG. 58 are different from one another.

[0531] Whether or not the curves k80 to k82 are different from one another is determined by determining whether or not two curves among the curves k80 to k82 are different for all combinations of two curves among the curves k80 to k82.

[0532] There are three combinations of two curves among the curves k80 to k82: (k80, k81), (k80, k82), and (k81, k82).

[0533] Therefore, FIG. 59 shows whether the two curves differ for the three cases (k80, k81), (k80, k82), and (k81, k82).

[0534] Referring to FIG. 59, the standard deviation σ of the difference between the plurality of integral values ​​(=28 integral values) on the curve k80 and the plurality of integral values ​​(=28 integral values) on the curve k81 is DF_k80,k81 is 58.15(%), and the standard deviation σ of the difference between the multiple integral values ​​(=28 integral values) on the curve k80 and the multiple integral values ​​(=28 integral values) on the curve k82 DF_k80,k82 is 71.16(%), and the standard deviation σ of the difference between the multiple integral values ​​(=28 integral values) on the curve k81 and the multiple integral values ​​(=28 integral values) on the curve k82 DF_k81,k82 is 89.81(%).

[0535] As a result, the standard deviation of the difference σ DF_k80,k81 (=58.15(%)), standard deviation of the difference σ DF_k80,k82 (=71.16(%)) and the standard deviation of the difference σ DF_k81,k82 (=89.81(%)) is the threshold value σ th (=15(%)) is greater than

[0536] Therefore, the two curves k80 and k81 are different, the two curves k80 and k82 are different, and the two curves k81 and k82 are different (see "◯" in FIG. 59). Therefore, the curves k80 to k82 are mutually different curves.

[0537] FIG. 60 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 57.

[0538] 60, each of the curves k83 to k85 is a curve CUR created by the above-described method by the analysis device 2. The curve k83 represents the integral spectrum of a red wine (Chile 2020), the curve k84 represents the integral spectrum of a red wine (Australia 2010), and the curve k85 represents the integral spectrum of a red wine (France 2013).

[0539] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=57 integral values) for each of curves k83 to k85 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 88 mV.

[0540] As a result, in FIG. 60, each of classes 1 to 56 is made up of a predetermined potential section (=integral value extraction potential) of 88 mV, and class 57 is made up of 70.4 mV.

[0541] FIG. 61 is a diagram showing the results of determining whether or not the curves k83 to k85 shown in FIG. 60 are different from one another.

[0542] Whether or not the curves k83 to k85 are different from one another is determined by determining whether or not two curves among the curves k83 to k85 are different for all combinations of two curves among the curves k83 to k85.

[0543] There are three combinations of two curves among the curves k83 to k85: (k83, k84), (k83, k85), and (k84, k85).

[0544] Therefore, FIG. 61 shows whether the two curves differ for the three cases (k83, k84), (k83, k85), and (k84, k85).

[0545] Referring to FIG. 61, the standard deviation σ of the difference between the plurality of integral values ​​(=57 integral values) on the curve k83 and the plurality of integral values ​​(=57 integral values) on the curve k84 DF_k83,k84is 67.36(%), and the standard deviation σ of the difference between the multiple integral values ​​(=57 integral values) on the curve k83 and the multiple integral values ​​(=57 integral values) on the curve k85 DF_k83,k85 is 76.13(%), and the standard deviation σ of the difference between the multiple integral values ​​(=57 integral values) on the curve k84 and the multiple integral values ​​(=57 integral values) on the curve k85 DF_k84,k85 is 95.05(%).

[0546] As a result, the standard deviation of the difference σ DF_k83,k84 (=67.36(%)), standard deviation of the difference σ DF_k83,k85 (=76.13(%)) and the standard deviation of the difference σ DF_k84,k85 (=95.05(%)) are all within the threshold σ th (=15(%)) is greater than

[0547] Therefore, the two curves k83 and k84 are different, the two curves k83 and k85 are different, and the two curves k84 and k85 are different (see "◯" in FIG. 61). Therefore, the curves k83 to k85 are mutually different curves.

[0548] FIG. 62 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 95.

[0549] 62, each of the curves k86 to k88 is a curve CUR created by the above-described method by the analysis device 2. The curve k86 represents the integral spectrum of a red wine (Chile 2020), the curve k87 represents the integral spectrum of a red wine (Australia 2010), and the curve k88 represents the integral spectrum of a red wine (France 2013).

[0550] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(= 95 integral values) for each of curves k86 to k88 were the same as those shown in Table 3 above, except that the specified potential range (= integral value extraction potential) of 17.6 mV was changed to 52.8 mV.

[0551] As a result, in FIG. 62, each of classes 1 to 94 is made up of a predetermined potential section (=integral value extraction potential) of 52.8 mV, and class 95 is made up of 35.2 mV.

[0552] FIG. 63 is a diagram showing the results of determining whether or not the curves k86 to k88 shown in FIG. 62 are different from one another.

[0553] Whether or not the curves k86 to k88 are different from one another is determined by determining whether or not two curves among the curves k86 to k88 are different for all combinations of two curves among the curves k86 to k88.

[0554] There are three combinations of two curves among the curves k86 to k88: (k86, k87), (k86, k88), and (k87, k88).

[0555] Therefore, FIG. 63 shows whether the two curves differ for the three cases (k86, k87), (k86, k88), and (k87, k88).

[0556] Referring to FIG. 63, the standard deviation σ of the difference between the plurality of integral values ​​(=95 integral values) on the curve k86 and the plurality of integral values ​​(=95 integral values) on the curve k87 DF_k86,k87 is 65.84(%), and the standard deviation σ of the difference between the multiple integral values ​​(=95 integral values) on the curve k86 and the multiple integral values ​​(=95 integral values) on the curve k88 DF_k86,k88 is 75.14(%), and the standard deviation σ of the difference between the multiple integral values ​​(=95 integral values) on the curve k87 and the multiple integral values ​​(=95 integral values) on the curve k88 DF_k87,k88 is 93.73(%).

[0557] As a result, the standard deviation of the difference σ DF_k86,k87 (=65.84(%)), standard deviation of the difference σ DF_k86,k88 (=75.14(%)) and the standard deviation of the difference σ DF_k87,k88 (=93.73(%)) are all within the threshold σ th (=15(%)) is greater than

[0558] Therefore, the two curves k86 and k87 are different, the two curves k86 and k88 are different, and the two curves k87 and k88 are different (see "◯" in FIG. 63). Therefore, the curves k86 to k88 are mutually different curves.

[0559] FIG. 64 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 142.

[0560] 64, each of the curves k89 to k91 is a curve CUR created by the above-described method by the analysis device 2. The curve k89 represents the integral spectrum of a red wine (Chile 2020), the curve k90 represents the integral spectrum of a red wine (Australia 2010), and the curve k91 represents the integral spectrum of a red wine (France 2013).

[0561] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=142 integral values) for each of curves k89 to k91 were the same as those shown in Table 3 above, except that the specified potential range (=integral value extraction potential) of 17.6 mV was changed to 35.2 mV.

[0562] As a result, in FIG. 64, each of classes 1 to 142 consists of a predetermined potential section (=integral value extraction potential) of 35.2 mV.

[0563] FIG. 65 is a diagram showing the results of determining whether or not the curves k89 to k91 shown in FIG. 64 are different from one another.

[0564] Whether or not the curves k89 to k91 are different from one another is determined by determining whether or not two of the curves k89 to k91 are different for all combinations of two of the curves k89 to k91.

[0565] There are three combinations of two curves among the curves k89 to k91: (k89, k90), (k89, k91), and (k90, k91).

[0566] Therefore, FIG. 65 shows whether the two curves differ for the three cases (k89, k90), (k89, k91), and (k90, k91).

[0567] Referring to FIG. 65, the standard deviation σ of the difference between the plurality of integral values ​​(=142 integral values) on the curve k89 and the plurality of integral values ​​(=142 integral values) on the curve k90 DF_k89,k90 is 66.26(%), and the standard deviation σ of the difference between the multiple integral values ​​(=142 integral values) on the curve k89 and the multiple integral values ​​(=142 integral values) on the curve k91 DF_k89,k91 is 75.30(%), and the standard deviation σ of the difference between the multiple integral values ​​(=142 integral values) on the curve k90 and the multiple integral values ​​(=142 integral values) on the curve k91 DF_k90,k91 is 93.45(%).

[0568] As a result, the standard deviation of the difference σ DF_k89,k90 (=66.26(%)), standard deviation of the difference σ DF_k89,k91 (=75.30(%)) and the standard deviation of the difference σ DF_k90,k91 (=93.45(%)) is the threshold value σ th (=15(%)) is greater than

[0569] Therefore, the two curves k89 and k90 are different, the two curves k89 and k91 are different, and the two curves k90 and k91 are different (see "◯" in FIG. 65). Therefore, the curves k89 to k91 are mutually different curves.

[0570] The above-mentioned Figure 16 shows the integral spectrum of red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals (=number of classes) is 284, and the above-mentioned Figure 17 shows the standard deviation of the difference σ DF_k5,k6 (=65.70(%)), standard deviation of the difference σ DF_k5,k7 (=74.94(%)) and the standard deviation of the difference σ DF_k6,k7 (=93.19(%)) are all within the threshold σ th (=15(%)).

[0571] Therefore, when the number of integrals is 3, 4, 6, 8, 10, 14, 28, 57, 95, 142, and 284, it is possible to obtain integral spectra (= curves CUR) that uniquely identify red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013), respectively.

[0572] In other words, when the number of integral values ​​is three, curve k62 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k63 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k64 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0573] Furthermore, when the number of integral values ​​is four, curve k65 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k66 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k67 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0574] Furthermore, when the number of integral values ​​is six, curve k68 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k69 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k70 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0575] Furthermore, when the number of integral values ​​is eight, curve k71 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k72 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k73 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0576] Furthermore, when the number of integral values ​​is 10, curve k74 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k75 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k76 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0577] Furthermore, when the number of integral values ​​is 14, curve k77 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k78 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k79 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0578] Furthermore, when the number of integral values ​​is 28, curve k80 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k81 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k82 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0579] Furthermore, when the number of integral values ​​is 57, curve k83 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k84 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k85 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0580] Furthermore, when the number of integral values ​​is 95, curve k86 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k87 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k88 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0581] Furthermore, when the number of integral values ​​is 142, curve k89 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k90 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k91 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0582] Furthermore, when the number of integral values ​​is 284, curve k5 is an integral value spectrum for uniquely identifying red wine (Chile 2020), curve k6 is an integral value spectrum for uniquely identifying red wine (Australia 2010), and curve k7 is an integral value spectrum for uniquely identifying red wine (France 2013).

[0583] Then, the integral spectrum (= index curve) for uniquely identifying red wine (Chile 2020) consists of curve k62 when the number of integrals is 3, curve k65 when the number of integrals is 4, curve k68 when the number of integrals is 6, curve k71 when the number of integrals is 8, curve k74 when the number of integrals is 10, curve k77 when the number of integrals is 14, curve k80 when the number of integrals is 28, curve k83 when the number of integrals is 57, curve k86 when the number of integrals is 95, curve k89 when the number of integrals is 142, and curve k5 when the number of integrals is 284.

[0584] In addition, the integral spectrum (= index curve) for uniquely identifying red wine (Australia 2010) consists of curve k63 when the number of integrals is 3, curve k66 when the number of integrals is 4, curve k69 when the number of integrals is 6, curve k72 when the number of integrals is 8, curve k75 when the number of integrals is 10, curve k78 when the number of integrals is 14, curve k81 when the number of integrals is 28, curve k84 when the number of integrals is 57, curve k87 when the number of integrals is 95, curve k90 when the number of integrals is 142, and curve k6 when the number of integrals is 284.

[0585] Furthermore, the integral spectrum (= index curve) for uniquely identifying red wine (France 2013) consists of curve k64 when the number of integrals is 3, curve k67 when the number of integrals is 4, curve k70 when the number of integrals is 6, curve k73 when the number of integrals is 8, curve k76 when the number of integrals is 10, curve k79 when the number of integrals is 14, curve k82 when the number of integrals is 28, curve k85 when the number of integrals is 57, curve k88 when the number of integrals is 95, curve k91 when the number of integrals is 142, and curve k7 when the number of integrals is 284.

[0586] Therefore, in general, the integral spectrum (= index curve) for uniquely identifying a red wine (Chile 2020) consists of multiple (= 11) index curves (= curves k62, k65, k68, k71, k74, k77, k80, k83, k86, k89, k5) with mutually different numbers of integrals, and the integral spectrum (= index curve) for uniquely identifying a red wine (Australia 2010) consists of multiple (= 11) index curves (= curves k62, k65, k68, k71, k74, k77, k80, k83, k86, k89, k5) with mutually different numbers of integrals. The integral spectrum (= index curve) for uniquely identifying red wine (France 2013) consists of multiple (= 11) index curves (= curves k63, k66, k69, k72, k75, k78, k81, k84, k87, k90, k6), and the integral spectrum (= index curve) for uniquely identifying red wine (France 2013) consists of multiple (= 11) index curves (= curves k64, k67, k70, k73, k76, k79, k82, k85, k88, k91, k7) with different numbers of integral values.

[0587] It was found that the minimum number of integrals required to obtain an integral spectrum that uniquely identifies each of red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) is three.

[0588] (C) Red Wine 2 FIG. 66 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is three.

[0589] 66, each of the curves k95 to k97 is a curve CUR created by the above-described method by the analysis device 2. The curve k95 represents the integral spectrum of a red wine (Italy 2015), the curve k96 represents the integral spectrum of a red wine (France 2016), and the curve k97 represents the integral spectrum of a red wine (Italy, year unknown).

[0590] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=3 integral values) for each of curves k95 to k97 were the same as those shown in Table 4 above, except that the predetermined potential range (=integral value extraction potential) of 34 mV was changed to 1054 mV.

[0591] As a result, in FIG. 66, each of classes 1 and 2 consists of a predetermined potential section (=integral value extraction potential) of 1054 mV, and class 3 consists of a predetermined potential section (=integral value extraction potential) of 1088 mV.

[0592] FIG. 67 is a diagram showing the results of determining whether or not the curves k95 to k97 shown in FIG. 66 are different from one another.

[0593] Whether or not the curves k95 to k97 are different from one another is determined by determining whether or not two curves among the curves k95 to k97 are different for all combinations of two curves among the curves k95 to k97.

[0594] There are three combinations of two curves among the curves k95 to k97: (k95, k96), (k95, k97), and (k96, k97).

[0595] Therefore, FIG. 67 shows whether the two curves differ for the three cases (k95, k96), (k95, k97), and (k96, k97).

[0596] Referring to FIG. 67, the standard deviation σ of the difference between the plurality of integral values ​​(=3 integral values) on the curve k95 and the plurality of integral values ​​(=3 integral values) on the curve k96 DF_k95,k96 is 7.62(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k95 and the multiple integral values ​​(=3 integral values) on the curve k97 DF_k95,k97 is 16.68(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k96 and the multiple integral values ​​(=3 integral values) on the curve k97 DF_k96,k97 is 10.33(%).

[0597] As a result, the standard deviation of the difference σ DF_k95,k97 (=16.68(%)) is the threshold σ th (=15(%)) and the standard deviation of the difference σ DF_k95,k96 (=7.62(%)) and the standard deviation of the difference σ DF_k96,k97 (=10.33(%)) is the threshold σ th (=15(%)).

[0598] Therefore, the two curves k95 and k97 are different, the two curves k95 and k96 are not different, and the two curves k96 and k97 are not different.

[0599] Therefore, the curves k95 to k97 are not mutually different curves.

[0600] FIG. 68 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is four.

[0601] 68, each of the curves k98 to k100 is a curve CUR created by the above-described method by the analysis device 2. The curve k98 represents the integral spectrum of a red wine (Italy 2015), the curve k99 represents the integral spectrum of a red wine (France 2016), and the curve k100 represents the integral spectrum of a red wine (Italy, year unknown).

[0602] The measurement conditions for the cyclic voltammograms that were used to calculate the multiple integral values ​​(=four integral values) for each of curves k98 to k100 were the same as those shown in Table 4 above, except that the predetermined potential interval (=integral value extraction potential) of 34 mV was changed to 782 mV.

[0603] As a result, in FIG. 68, each of classes 1 to 3 consists of a predetermined potential section of 782 mV (=integral value extraction potential), and class 4 consists of a predetermined potential section of 850 mV (=integral value extraction potential).

[0604] FIG. 69 is a diagram showing the results of determining whether or not the curves k98 to k100 shown in FIG. 68 are different from one another.

[0605] Whether or not the curves k98 to k100 are different from one another is determined by determining whether or not two curves among the curves k98 to k100 are different for all combinations of two curves among the curves k98 to k100.

[0606] There are three combinations of two curves among the curves k98 to k100: (k98, k99), (k98, k100), and (k99, k100).

[0607] Therefore, FIG. 69 shows whether the two curves differ for the three cases (k98, k99), (k98, k100), and (k99, k100).

[0608] Referring to FIG. 69, the standard deviation σ of the difference between the plurality of integral values ​​(=4 integral values) on the curve k98 and the plurality of integral values ​​(=4 integral values) on the curve k99 is DF_k98,k99 is 30.51(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k98 and the multiple integral values ​​(=4 integral values) on the curve k100 DF_k98,k100 is 35.29(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k99 and the multiple integral values ​​(=4 integral values) on the curve k100 DF_k99,k100 is 9.36(%).

[0609] As a result, the standard deviation of the difference σ DF_k98,k99 (=30.51(%)) and the standard deviation of the difference σ DF_k98,k100 (=35.29(%)) is the threshold σ th (=15(%)) and the standard deviation of the difference σ DF_k99,k100 (=9.36(%)) is the threshold σ th (=15(%)).

[0610] Therefore, the two curves k98 and k99 are different, the two curves k98 and k100 are different, and the two curves k99 and k100 are not different.

[0611] Therefore, the curves k98 to k100 are not mutually different curves.

[0612] FIG. 70 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is six.

[0613] 70, each of the curves k101 to k103 is a curve CUR created by the above-described method by the analysis device 2. The curve k101 represents the integral spectrum of a red wine (Italy 2015), the curve k102 represents the integral spectrum of a red wine (France 2016), and the curve k103 represents the integral spectrum of a red wine (Italy, year unknown).

[0614] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=6 integral values) for each of curves k101 to k103 were the same as those shown in Table 4 above, except that the specified potential range (=integral value extraction potential) of 34 mV was changed to 510 mV.

[0615] As a result, in FIG. 70, each of classes 1 to 5 consists of a predetermined potential section of 510 mV (=integral value extraction potential), and class 6 consists of a predetermined potential section of 646 mV (=integral value extraction potential).

[0616] FIG. 71 is a diagram showing the results of determining whether or not the curves k101 to k103 shown in FIG. 70 are different from one another.

[0617] Whether or not the curves k101 to k103 are different from one another is determined by determining whether or not two curves among the curves k101 to k103 are different for all combinations of two curves among the curves k101 to k103.

[0618] There are three combinations of two curves among the curves k101 to k103: (k101, k102), (k101, k103), and (k102, k103).

[0619] Therefore, FIG. 71 shows whether or not the two curves differ for the three cases (k101, k102), (k101, k103), and (k102, k103).

[0620] Referring to FIG. 71, the standard deviation σ of the difference between the plurality of integral values ​​(=6 integral values) on the curve k101 and the plurality of integral values ​​(=6 integral values) on the curve k102 is DF_k101,k102 is 16.42(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k101 and the multiple integral values ​​(=6 integral values) on the curve k103 DF_k101,k103 is 21.20(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k102 and the multiple integral values ​​(=6 integral values) on the curve k103 DF_k102,k103 is 13.85(%).

[0621] As a result, the standard deviation of the difference σ DF_k101,k102 (=16.42(%)) and the standard deviation of the difference σ DF_k101,k103 (=21.20(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k102,k103 (=13.85(%)) is the threshold value σ th (=15(%)).

[0622] Therefore, the two curves k101 and k102 are different, the two curves k101 and k103 are different, and the two curves k102 and k103 are not different.

[0623] Therefore, the curves k101 to k103 are not mutually different curves.

[0624] FIG. 72 is a diagram showing integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italy, year unknown) when the number of integrals is seven.

[0625] 72, each of the curves k104 to k106 is a curve CUR created by the above-described method by the analysis device 2. The curve k104 represents the integral spectrum of a red wine (Italy 2015), the curve k105 represents the integral spectrum of a red wine (France 2016), and the curve k106 represents the integral spectrum of a red wine (Italy, year unknown).

[0626] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=7 integral values) for each of curves k104 to k106 were the same as those shown in Table 4 above, except that the predetermined potential range (=integral value extraction potential) of 34 mV was changed to 476 mV.

[0627] As a result, in FIG. 72, each of classes 1 to 6 consists of a predetermined potential section of 476 mV (=integral value extraction potential), and class 7 consists of a predetermined potential section of 340 mV (=integral value extraction potential).

[0628] FIG. 73 is a diagram showing the results of determining whether or not the curves k104 to k106 shown in FIG. 72 are different from one another.

[0629] Whether or not the curves k104 to k106 are different from one another is determined by determining whether or not two curves among the curves k104 to k106 are different for all combinations of two curves among the curves k104 to k106.

[0630] There are three combinations of two curves among the curves k104 to k106: (k104, k105), (k104, k106), and (k105, k106).

[0631] Therefore, FIG. 73 shows whether the two curves differ for the three cases (k104, k105), (k104, k106), and (k105, k106).

[0632] Referring to FIG. 73, the standard deviation σ of the difference between the plurality of integral values ​​(=7 integral values) on the curve k104 and the plurality of integral values ​​(=7 integral values) on the curve k105 is DF_k104,k105 is 20.05(%), and the standard deviation σ of the difference between the multiple integral values ​​(=7 integral values) on the curve k104 and the multiple integral values ​​(=7 integral values) on the curve k106 DF_k104,k106 is 23.59(%), and the standard deviation σ of the difference between the multiple integral values ​​(=7 integral values) on the curve k105 and the multiple integral values ​​(=7 integral values) on the curve k106 DF_k105,k106 is 15.11(%).

[0633] As a result, the standard deviation of the difference σ DF_k104,k105 (=20.05(%)), standard deviation of the difference σ DF_k104,k106 (=23.59(%)) and the standard deviation of the difference σ DF_k105,k106 (=15.11(%)) are all within the threshold σ th (=15(%)) is greater than

[0634] Therefore, the two curves k104 and k105 are different, the two curves k104 and k106 are different, and the two curves k105 and k106 are different (see "◯" in FIG. 73). Therefore, the curves k104 to k106 are mutually different curves.

[0635] FIG. 74 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is eight.

[0636] 74, each of the curves k107 to k109 is a curve CUR created by the above-described method by the analysis device 2. The curve k107 represents the integral spectrum of a red wine (Italy 2015), the curve k108 represents the integral spectrum of a red wine (France 2016), and the curve k109 represents the integral spectrum of a red wine (Italy, year unknown).

[0637] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=8 integral values) for each of curves k107 to k109 were the same as those shown in Table 4 above, except that the predetermined potential range (=integral value extraction potential) of 34 mV was changed to 408 mV.

[0638] As a result, in FIG. 74, each of classes 1 to 7 consists of a predetermined potential section of 408 mV (=integral value extraction potential), and class 8 consists of a predetermined potential section of 340 mV (=integral value extraction potential).

[0639] FIG. 75 is a diagram showing the results of determining whether or not the curves k107 to k109 shown in FIG. 74 are different from one another.

[0640] Whether or not the curves k107 to k109 are different from one another is determined by determining whether or not two of the curves k107 to k109 are different for all combinations of two of the curves k107 to k109.

[0641] There are three combinations of two curves among the curves k107 to k109: (k107, k108), (k107, k109), and (k108, k109).

[0642] Therefore, FIG. 75 shows whether the two curves differ for the three cases (k107, k108), (k107, k109), and (k108, k109).

[0643] Referring to FIG. 75, the standard deviation σ of the difference between the plurality of integral values ​​(=8 integral values) on the curve k107 and the plurality of integral values ​​(=8 integral values) on the curve k108 is DF_k107,k108 is 62.94(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k107 and the multiple integral values ​​(=8 integral values) on the curve k109 DF_k107,k109 is 67.43(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k108 and the multiple integral values ​​(=8 integral values) on the curve k109 DF_k108,k109 is 38.08(%).

[0644] As a result, the standard deviation of the difference σ DF_k107,k108 (=62.94(%)), standard deviation of the difference σ DF_k107,k109 (=67.43(%)) and the standard deviation of the difference σ DF_k108,k109 (=38.08(%)) is the threshold value σ th (=15(%)) is greater than

[0645] Therefore, the two curves k107 and k108 are different, the two curves k107 and k109 are different, and the two curves k108 and k109 are different (see "◯" in FIG. 75). Therefore, the curves k107 to k109 are mutually different curves.

[0646] FIG. 76 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 11.

[0647] 76, each of the curves k110 to k112 is a curve CUR created by the above-described method by the analysis device 2. The curve k110 represents the integral spectrum of a red wine (Italy 2015), the curve k111 represents the integral spectrum of a red wine (France 2016), and the curve k112 represents the integral spectrum of a red wine (Italy, year unknown).

[0648] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=11 integral values) for each of curves k110 to k112 were the same as those shown in Table 4 above, except that the specified potential range (=integral value extraction potential) of 34 mV was changed to 306 mV.

[0649] As a result, in FIG. 76, each of classes 1 to 10 consists of a predetermined potential section of 306 mV (=integral value extraction potential), and class 11 consists of a predetermined potential section of 136 mV (=integral value extraction potential).

[0650] FIG. 77 is a diagram showing the results of determining whether or not the curves k110 to k112 shown in FIG. 76 are different from one another.

[0651] Whether or not the curves k110 to k112 are different from one another is determined by determining whether or not two curves among the curves k110 to k112 are different for all combinations of two curves among the curves k110 to k112.

[0652] There are three combinations of two curves among the curves k110 to k112: (k110, k111), (k110, k112), and (k111, k112).

[0653] Therefore, FIG. 77 shows whether or not the two curves differ for the three cases (k110, k111), (k110, k112), and (k111, k112).

[0654] Referring to FIG. 77, the standard deviation σ of the difference between the plurality of integral values ​​(=11 integral values) on the curve k110 and the plurality of integral values ​​(=11 integral values) on the curve k111 is DF_k110,k111 is 27.60(%), and the standard deviation σ of the difference between the multiple integral values ​​(=11 integral values) on the curve k110 and the multiple integral values ​​(=11 integral values) on the curve k112 DF_k110,k112 is 30.63(%), and the standard deviation σ of the difference between the multiple integral values ​​(=11 integral values) on the curve k111 and the multiple integral values ​​(=11 integral values) on the curve k112DF_k111,k112 is 16.41(%).

[0655] As a result, the standard deviation of the difference σ DF_k110,k111 (=27.60(%)), standard deviation of the difference σ DF_k110,k112 (=30.63(%)) and the standard deviation of the difference σ DF_k111,k112 (=16.41(%)) are all within the threshold σ th (=15(%)) is greater than

[0656] Therefore, the two curves k110 and k111 are different, the two curves k110 and k112 are different, and the two curves k111 and k112 are different (see "◯" in FIG. 77). Therefore, the curves k110 to k112 are mutually different curves.

[0657] FIG. 78 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 16.

[0658] 78, each of the curves k113 to k115 is a curve CUR created by the above-described method by the analysis device 2. The curve k113 represents the integral spectrum of a red wine (Italy 2015), the curve k114 represents the integral spectrum of a red wine (France 2016), and the curve k115 represents the integral spectrum of a red wine (Italy, year unknown).

[0659] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=16 integral values) for each of curves k113 to k115 were the same as those shown in Table 4 above, except that the specified potential range (=integral value extraction potential) of 34 mV was changed to 204 mV.

[0660] As a result, in FIG. 78, each of classes 1 to 15 consists of a predetermined potential section (=integral value extraction potential) of 204 mV, and class 16 consists of a predetermined potential section (=integral value extraction potential) of 272 mV.

[0661] FIG. 79 is a diagram showing the results of determining whether or not the curves k113 to k115 shown in FIG. 78 are different from one another.

[0662] Whether or not the curves k113 to k115 are different from one another is determined by determining whether or not two curves among the curves k113 to k115 are different for all combinations of two curves among the curves k113 to k115.

[0663] There are three combinations of two curves among the curves k113 to k115: (k113, k114), (k113, k115), and (k114, k115).

[0664] Therefore, FIG. 79 shows whether the two curves differ for the three cases (k113, k114), (k113, k115), and (k114, k115).

[0665] Referring to FIG. 79, the standard deviation σ of the difference between the plurality of integral values ​​(=16 integral values) on the curve k113 and the plurality of integral values ​​(=16 integral values) on the curve k114 is DF_k113,k114 is 25.16(%), and the standard deviation σ of the difference between the multiple integral values ​​(=16 integral values) on the curve k113 and the multiple integral values ​​(=16 integral values) on the curve k115 DF_k113,k115 is 29.10(%), and the standard deviation σ of the difference between the multiple integral values ​​(=16 integral values) on the curve k114 and the multiple integral values ​​(=16 integral values) on the curve k115 DF_k114,k115 is 16.01(%).

[0666] As a result, the standard deviation of the difference σ DF_k113,k114 (=25.16(%)), standard deviation of the difference σ DF_k113,k115 (=29.10(%)) and the standard deviation of the difference σ DF_k114,k115 (=16.01(%)) are all within the threshold σ th (=15(%)) is greater than

[0667] Therefore, the two curves k113 and k114 are different, the two curves k113 and k115 are different, and the two curves k114 and k115 are different (see "◯" in FIG. 79). Therefore, the curves k113 to k115 are mutually different curves.

[0668] FIG. 80 is a diagram showing integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals is 19.

[0669] 80, each of the curves k116 to k118 is a curve CUR created by the above-described method by the analysis device 2. The curve k116 represents the integral spectrum of a red wine (Italy 2015), the curve k117 represents the integral spectrum of a red wine (France 2016), and the curve k118 represents the integral spectrum of a red wine (Italy, year unknown).

[0670] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=19 integral values) for each of curves k116 to k118 were the same as those shown in Table 4 above, except that the specified potential range (=integral value extraction potential) of 34 mV was changed to 170 mV.

[0671] As a result, in FIG. 80, each of classes 1 to 18 consists of a predetermined potential section (=integral value extraction potential) of 170 mV, and class 19 consists of a predetermined potential section (=integral value extraction potential) of 136 mV.

[0672] FIG. 81 is a diagram showing the results of determining whether or not the curves k116 to k118 shown in FIG. 80 are different from one another.

[0673] Whether or not the curves k116 to k118 are different from one another is determined by determining whether or not two of the curves k116 to k118 are different for all combinations of two of the curves k116 to k118.

[0674] There are three combinations of two curves among the curves k116 to k118: (k116, k117), (k116, k118), and (k117, k118).

[0675] Therefore, FIG. 81 shows whether the two curves differ for the three cases (k116, k117), (k116, k118), and (k117, k118).

[0676] Referring to FIG. 81, the standard deviation σ of the difference between the plurality of integral values ​​(=19 integral values) on the curve k116 and the plurality of integral values ​​(=19 integral values) on the curve k117 is DF_k116,k117 is 28.90(%), and the standard deviation σ of the difference between the multiple integral values ​​(=19 integral values) on the curve k116 and the multiple integral values ​​(=19 integral values) on the curve k118 DF_k116,k118 is 33.05(%), and the standard deviation σ of the difference between the multiple integral values ​​(=19 integral values) on the curve k117 and the multiple integral values ​​(=19 integral values) on the curve k118 DF_k117,k118 is 17.31(%).

[0677] As a result, the standard deviation of the difference σ DF_k116,k117 (=28.90(%)), standard deviation of the difference σ DF_k116,k118 (=33.05(%)) and the standard deviation of the difference σ DF_k117,k118 (=17.31(%)) are all within the threshold σ th (=15(%)) is greater than

[0678] Therefore, the two curves k116 and k117 are different, the two curves k116 and k118 are different, and the two curves k117 and k118 are different (see "◯" in FIG. 81). Therefore, the curves k116 to k118 are mutually different curves.

[0679] FIG. 82 is a diagram showing integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italy, year unknown) when the number of integrals is 47.

[0680] 82, each of the curves k119 to k121 is a curve CUR created by the above-described method by the analysis device 2. The curve k119 represents the integral spectrum of a red wine (Italy 2015), the curve k120 represents the integral spectrum of a red wine (France 2016), and the curve k121 represents the integral spectrum of a red wine (Italy, year unknown).

[0681] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(= 47 integral values) for each of curves k119 to k121 were the same as those shown in Table 4 above, except that the specified potential range (= integral value extraction potential) of 34 mV was changed to 68 mV.

[0682] As a result, in FIG. 82, each of classes 1 to 47 consists of a predetermined potential section (=integral value extraction potential) of 68 mV.

[0683] FIG. 83 is a diagram showing the results of determining whether or not the curves k119 to k121 shown in FIG. 82 are different from one another.

[0684] Whether or not the curves k119 to k121 are different from one another is determined by determining whether or not two of the curves k119 to k121 are different for all combinations of two of the curves k119 to k121.

[0685] There are three combinations of two curves among the curves k119 to k121: (k119, k120), (k119, k121), and (k120, k121).

[0686] Therefore, FIG. 83 shows whether the two curves differ for the three cases (k119, k120), (k119, k121), and (k120, k121).

[0687] Referring to FIG. 83, the standard deviation σ of the difference between the plurality of integral values ​​(=47 integral values) on the curve k119 and the plurality of integral values ​​(=47 integral values) on the curve k120 DF_k119,k120is 37.61(%), and the standard deviation σ of the difference between the multiple integral values ​​(=47 integral values) on the curve k119 and the multiple integral values ​​(=47 integral values) on the curve k121 DF_k119,k121 is 39.71(%), and the standard deviation σ of the difference between the multiple integral values ​​(=47 integral values) on the curve k129 and the multiple integral values ​​(=47 integral values) on the curve k121 DF_k120,k121 is 17.49(%).

[0688] As a result, the standard deviation of the difference σ DF_k119,k120 (=37.61(%)), standard deviation of the difference σ DF_k119,k121 (=39.71(%)) and the standard deviation of the difference σ DF_k120,k121 (=17.49(%)) are all within the threshold σ th (=15(%)) is greater than

[0689] Therefore, the two curves k119 and k120 are different, the two curves k119 and k121 are different, and the two curves k120 and k121 are different (see "◯" in FIG. 83). Therefore, the curves k119 to k121 are mutually different curves.

[0690] The above-mentioned Figure 18 shows the integral spectrum of red wine (Italy 2015), red wine (France 2016), and red wine (Italy, unknown vintage) when the number of integrals (= number of classes) is 94, and the above-mentioned Figure 19 shows the standard deviation of the difference σ DF_k8,k9 (=57.13(%)), standard deviation of the difference σ DF_k8,k10 (=40.56(%)) and the standard deviation of the difference σ DF_k9,k10 (=17.85(%)) are all within the threshold σ th (=15(%)).

[0691] Therefore, when the number of integrals is 7, 8, 11, 16, 19, 47, and 94, it is possible to obtain integral spectra (= curves CUR) that can uniquely identify red wine (Italy 2015), red wine (France 2016), and red wine (Italy, unknown age), respectively.

[0692] In other words, when the number of integral values ​​is seven, curve k104 is an integral spectrum for uniquely identifying red wine (Italy 2015), curve k105 is an integral spectrum for uniquely identifying red wine (France 2016), and curve k106 is an integral spectrum for uniquely identifying red wine (Italy, unknown year).

[0693] Furthermore, when the number of integral values ​​is eight, curve k107 is an integral spectrum for uniquely identifying red wine (Italy 2015), curve k108 is an integral spectrum for uniquely identifying red wine (France 2016), and curve k109 is an integral spectrum for uniquely identifying red wine (Italy, unknown year).

[0694] Furthermore, when the number of integral values ​​is 11, curve k110 is an integral value spectrum for uniquely identifying red wine (Italy 2015), curve k111 is an integral value spectrum for uniquely identifying red wine (France 2016), and curve k112 is an integral value spectrum for uniquely identifying red wine (Italy, unknown year).

[0695] Furthermore, when the number of integral values ​​is 16, curve k113 is an integral value spectrum for uniquely identifying red wine (Italy 2015), curve k114 is an integral value spectrum for uniquely identifying red wine (France 2016), and curve k115 is an integral value spectrum for uniquely identifying red wine (Italy, unknown year).

[0696] Furthermore, when the number of integral values ​​is 19, curve k116 is an integral value spectrum for uniquely identifying red wine (Italy 2015), curve k117 is an integral value spectrum for uniquely identifying red wine (France 2016), and curve k118 is an integral value spectrum for uniquely identifying red wine (Italy, unknown year).

[0697] Furthermore, when the number of integral values ​​is 47, curve k119 is an integral value spectrum for uniquely identifying red wine (Italy 2015), curve k120 is an integral value spectrum for uniquely identifying red wine (France 2016), and curve k121 is an integral value spectrum for uniquely identifying red wine (Italy, unknown year).

[0698] Furthermore, when the number of integral values ​​is 94, curve k8 is an integral value spectrum for uniquely identifying red wine (Italy 2015), curve k9 is an integral value spectrum for uniquely identifying red wine (France 2016), and curve k10 is an integral value spectrum for uniquely identifying red wine (Italy, unknown year).

[0699] In this case, the integral spectrum (= index curve) for uniquely identifying red wine (Italy 2015) consists of curve k104 when the number of integrals is 7, curve k107 when the number of integrals is 8, curve k110 when the number of integrals is 11, curve k113 when the number of integrals is 16, curve k116 when the number of integrals is 19, curve k119 when the number of integrals is 47, and curve k8 when the number of integrals is 94.

[0700] In addition, the integral spectrum (= index curve) for uniquely identifying red wine (France 2016) consists of curve k105 when the number of integrals is 7, curve k108 when the number of integrals is 8, curve k111 when the number of integrals is 11, curve k114 when the number of integrals is 16, curve k117 when the number of integrals is 19, curve k120 when the number of integrals is 47, and curve k9 when the number of integrals is 94.

[0701] Furthermore, the integral spectrum (= index curve) for uniquely identifying red wine (Italian vintage unknown) consists of curve k106 when the number of integrals is 7, curve k109 when the number of integrals is 8, curve k112 when the number of integrals is 11, curve k115 when the number of integrals is 16, curve k118 when the number of integrals is 19, curve k121 when the number of integrals is 47, and curve k10 when the number of integrals is 94.

[0702] Therefore, in general, an integral spectrum (= index curve) for uniquely identifying a red wine (Italy 2015) consists of multiple (= seven) index curves (= curves k104, k107, k110, k113, k116, k119, k8) with different numbers of integrals, an integral spectrum (= index curve) for uniquely identifying a red wine (France 2016) consists of multiple (= seven) index curves (= curves k105, k108, k111, k114, k117, k120, k9) with different numbers of integrals, and an integral spectrum (= index curve) for uniquely identifying a red wine (Italy, unknown vintage) consists of multiple (= seven) index curves (= curves k106, k109, k112, k115, k118, k121, k10).

[0703] It was found that the minimum number of integrals required to obtain an integral spectrum that can uniquely identify each of the red wines (Italy 2015), red wine (France 2016), and red wine (Italy, unknown age) is seven.

[0704] In addition, when the number of integral values ​​is three, the standard deviation σ of the difference between the three integral values ​​on the curve k95 representing red wine (Italy 2015) and the three integral values ​​on the curve k97 representing red wine (Italy, year unknown) is k95,k97 is the threshold σ th(=15(%)) (see Figure 67), and the number of integrals is four, the standard deviation σ of the difference between the four integrals on the curve k98 representing red wine (Italy 2015) and the four integrals on the curve k100 representing red wine (Italy, year unknown) k98,k100 is the threshold σ th (=15(%)) (see Figure 69), and the number of integrals is six, the standard deviation σ of the difference between the six integrals on the curve k101 representing red wine (Italy 2015) and the six integrals on the curve k103 representing red wine (Italy, year unknown) k101,k103 is the threshold σ th The result is 21.20(%), which is larger than the (=15(%)) (see Figure 71).

[0705] In addition, when the number of integral values ​​is seven, the standard deviation σ of the difference between the seven integral values ​​on the curve k104 representing red wine (Italy 2015) and the seven integral values ​​on the curve k106 representing red wine (Italy, year unknown) is k104,k106 is the threshold σ th (=15(%)) (see Figure 73), and when the number of integrals is eight, the standard deviation σ of the difference between the eight integrals on the curve k107 representing red wine (Italy 2015) and the eight integrals on the curve k109 representing red wine (Italy, year unknown) k107,k109 is the threshold σ th (=15(%)) (see Figure 75), and the number of integrals is 11, the standard deviation σ of the difference between the 11 integrals on the curve k110 representing red wine (Italy 2015) and the 11 integrals on the curve k112 representing red wine (Italy, year unknown) k110,k112 is the threshold σ th (=15(%)) (see Figure 77), and the number of integrals is 16, the standard deviation σ of the difference between the 16 integrals on the curve k113 representing red wine (Italy 2015) and the 16 integrals on the curve k115 representing red wine (Italy, year unknown) k113,k115 is the threshold σ th(=15(%)) is 29.10(%) (see Figure 79).

[0706] Furthermore, when the number of integrals is 19, the standard deviation σ of the difference between the 19 integrals on the curve k116 representing red wine (Italy 2015) and the 19 integrals on the curve k118 representing red wine (Italy, year unknown) is k116,k118 is the threshold σ th (=15(%)) (see Figure 81), and the number of integrals is 47, the standard deviation σ of the difference between the 47 integrals on the curve k119 representing red wine (Italy 2015) and the 47 integrals on the curve k121 representing red wine (Italy, year unknown) k119,k121 is the threshold σ th (=15(%)) (see Figure 83), and the number of integrals is 94, the standard deviation σ of the difference between the 94 integrals on the curve k8 representing red wine (Italy 2015) and the 94 integrals on the curve k10 representing red wine (Italy, year unknown) k8,k10 is the threshold σ th The result is 40.56(%), which is larger than the mean (=15(%)) (see FIG. 19).

[0707] As a result, the standard deviation of the difference between the integrals of the curve representing red wine (Italy 2015) and the integrals of the curve representing red wine (Italy, unknown vintage) was 3, 4, 6, 7, 8, 11, 16, 19, 47, and 94, and the threshold value σ th (=15(%)) is greater than

[0708] Therefore, each of the above-mentioned curves k95, k98, k101, k104, k107, k110, k113, k116, k119, and k8 is an integral spectrum for uniquely identifying red wine (Italy 2015), and each of the above-mentioned curves k97, k100, k103, k106, k109, k112, k115, k118, k121, and k10 is an integral spectrum for uniquely identifying red wine (Italy, year unknown).

[0709] It was found that the minimum number of integrals in the integral spectrum required to uniquely identify a red wine (Italy 2015) and a red wine (Italy, unknown age) is three.

[0710] (D) Coffee FIG. 84 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is three.

[0711] 84, each of the curves k125 to k127 is a curve CUR created by the above-described method by the analysis device 2. The curve k125 represents the integral spectrum of coffee (WONDA), the curve k126 represents the integral spectrum of coffee (Craft Boss), and the curve k127 represents the integral spectrum of coffee (Gold Brew).

[0712] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(= three integral values) for each of curves k125 to k127 were the same as those shown in Table 5 above, except that the specified potential range (= integral value extraction potential) of 18.4 mV was changed to 1656 mV.

[0713] As a result, in FIG. 84, each of classes 1 and 2 consists of a predetermined potential section (=integral value extraction potential) of 1656 mV, and class 3 consists of a predetermined potential section (=integral value extraction potential) of 1674.4 mV.

[0714] FIG. 85 is a diagram showing the results of determining whether or not the curves k125 to k127 shown in FIG. 84 are different from one another.

[0715] Whether or not the curves k125 to k127 are different from one another is determined by determining whether or not two curves among the curves k125 to k127 are different for all combinations of two curves among the curves k125 to k127.

[0716] There are three combinations of two curves among the curves k125 to k127: (k125, k126), (k125, k127), and (k126, k127).

[0717] Therefore, FIG. 85 shows whether the two curves differ for the three cases (k125, k126), (k125, k127), and (k126, k127).

[0718] Referring to FIG. 85, the standard deviation σ of the difference between the plurality of integral values ​​(=3 integral values) on the curve k125 and the plurality of integral values ​​(=3 integral values) on the curve k126 is DF_k125,k126 is 10.36(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k125 and the multiple integral values ​​(=3 integral values) on the curve k127 DF_k125,k127 is 21.07(%), and the standard deviation σ of the difference between the multiple integral values ​​(=3 integral values) on the curve k126 and the multiple integral values ​​(=3 integral values) on the curve k127 DF_k126,k127 is 10.73(%).

[0719] As a result, the standard deviation of the difference σ DF_k125,k126 (=10.36(%)) is the threshold value σ th (=15(%)), and the standard deviation of the difference σ DF_k126,k127 (=10.73(%)) is the threshold σ th (=15(%)), and the standard deviation of the difference σ DF_k125,k127 (=21.07(%)) is the threshold value σ th (=15(%)) is greater than

[0720] Therefore, the two curves k125 and k127 are different, the two curves k125 and k126 are not different, and the two curves k126 and k127 are not different. Therefore, when the number of integral values ​​is three, the curves k125 to k127 are not different from each other.

[0721] FIG. 86 shows the integral spectrum for WONDA, Craft Boss, and Gold Brew when the number of integrals is four.

[0722] 86, each of the curves k128 to k130 is a curve CUR created by the above-described method by the analysis device 2. The curve k128 represents the integral spectrum of coffee (WONDA), the curve k129 represents the integral spectrum of coffee (Craft Boss), and the curve k130 represents the integral spectrum of coffee (Gold Brew).

[0723] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=four integral values) for each of curves k128 to k130 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 1251.2 mV.

[0724] As a result, in FIG. 86, each of classes 1 to 3 consists of a predetermined potential section (=integral value extraction potential) of 1251.2 mV, and class 4 consists of a predetermined potential section (=integral value extraction potential) of 1232.8 mV.

[0725] FIG. 87 is a diagram showing the results of determining whether or not the curves k128 to k130 shown in FIG. 86 are different from one another.

[0726] Whether or not the curves k128 to k130 are different from one another is determined by determining whether or not two of the curves k128 to k130 are different for all combinations of two of the curves k128 to k130.

[0727] There are three combinations of two curves among the curves k128 to k130: (k128, k129), (k128, k130), and (k129, k130).

[0728] Therefore, FIG. 87 shows whether the two curves differ for the three cases (k128, k129), (k128, k130), and (k129, k130).

[0729] Referring to FIG. 87, the standard deviation σ of the difference between the plurality of integral values ​​(=4 integral values) on the curve k128 and the plurality of integral values ​​(=4 integral values) on the curve k129 is DF_k128,k129 is 11.64(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k128 and the multiple integral values ​​(=4 integral values) on the curve k130 DF_k128,k130 is 39.98(%), and the standard deviation σ of the difference between the multiple integral values ​​(=4 integral values) on the curve k129 and the multiple integral values ​​(=4 integral values) on the curve k130 DF_k129,k130 is 24.26(%).

[0730] As a result, the standard deviation of the difference σ DF_k128,k129 (=11.64(%)) is the threshold σ th (=15(%)), and the standard deviation of the difference σ DF_k128,k130 (=39.98(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k129,k130 (=24.26(%)) is the threshold value σ th (=15(%)) is greater than

[0731] Therefore, the two curves k128 and k130 are different, the two curves k129 and k130 are different, and the two curves k128 and k129 are not different. Therefore, when the number of integral values ​​is four, the curves k128 to k130 are not mutually different curves.

[0732] FIG. 88 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is five.

[0733] 88, each of the curves k131 to k133 is a curve CUR created by the above-described method by the analysis device 2. The curve k131 represents the integral spectrum of coffee (WONDA), the curve k132 represents the integral spectrum of coffee (Craft Boss), and the curve k133 represents the integral spectrum of coffee (Gold Brew).

[0734] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=5 integral values) for each of curves k131 to k133 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 993.6 mV.

[0735] As a result, in FIG. 88, each of classes 1 to 4 consists of a predetermined potential section (=integral value extraction potential) of 993.6 mV, and class 5 consists of a predetermined potential section (=integral value extraction potential) of 1012 mV.

[0736] FIG. 89 is a diagram showing the results of determining whether or not the curves k131 to k133 shown in FIG. 88 are different from one another.

[0737] Whether or not the curves k131 to k133 are different from one another is determined by determining whether or not two curves among the curves k131 to k133 are different for all combinations of two curves among the curves k131 to k133.

[0738] There are three combinations of two curves among the curves k131 to k133: (k131, k132), (k131, k133), and (k132, k133).

[0739] Therefore, FIG. 89 shows whether the two curves differ for the three cases (k131, k132), (k131, k133), and (k132, k133).

[0740] Referring to FIG. 89, the standard deviation σ of the difference between the plurality of integral values ​​(=5 integral values) on the curve k131 and the plurality of integral values ​​(=5 integral values) on the curve k132 DF_k131,k132 is 13.44(%), and the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k131 and the multiple integral values ​​(=5 integral values) on the curve k133 DF_k131,k133 is 30.78(%), and the standard deviation σ of the difference between the multiple integral values ​​(=5 integral values) on the curve k132 and the multiple integral values ​​(=5 integral values) on the curve k133 DF_k132,k133 is 19.88(%).

[0741] As a result, the standard deviation of the difference σ DF_k131,k132 (=13.44(%)) is the threshold value σ th (=15(%)), and the standard deviation of the difference σ DF_k131,k133 (=30.78(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k132,k133 (=19.88(%)) is the threshold σ th (=15(%)) is greater than

[0742] Therefore, the two curves k131 and k133 are different, the two curves k132 and k133 are different, and the two curves k131 and k132 are not different. Therefore, when the number of integral values ​​is five, the curves k131 to k133 are not different from each other.

[0743] FIG. 90 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is six.

[0744] 90, each of the curves k134 to k136 is a curve CUR created by the above-described method by the analysis device 2. The curve k134 represents the integral spectrum of coffee (WONDA), the curve k135 represents the integral spectrum of coffee (Craft Boss), and the curve k136 represents the integral spectrum of coffee (Gold Brew).

[0745] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=6 integral values) for each of curves k134 to k136 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 828 mV.

[0746] As a result, in FIG. 90, each of classes 1 to 5 consists of a predetermined potential section (=integral value extraction potential) of 828 mV, and class 6 consists of a predetermined potential section (=integral value extraction potential) of 846.4 mV.

[0747] FIG. 91 is a diagram showing the result of determining whether or not the curves k134 to k136 shown in FIG. 90 are different from one another.

[0748] Whether or not the curves k134 to k136 are different from one another is determined by determining whether or not two of the curves k134 to k136 are different for all combinations of two of the curves k134 to k136.

[0749] There are three combinations of two curves among the curves k134 to k136: (k134, k135), (k134, k136), and (k135, k136).

[0750] Therefore, FIG. 91 shows whether the two curves differ for the three cases (k134, k135), (k134, k136), and (k135, k136).

[0751] Referring to FIG. 91, the standard deviation σ of the difference between the plurality of integral values ​​(=6 integral values) on the curve k134 and the plurality of integral values ​​(=6 integral values) on the curve k135 is DF_k134,k135 is 15.26(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k134 and the multiple integral values ​​(=6 integral values) on the curve k136 DF_k134,k136 is 41.83(%), and the standard deviation σ of the difference between the multiple integral values ​​(=6 integral values) on the curve k135 and the multiple integral values ​​(=6 integral values) on the curve k136DF_k135,k136 is 31.27(%).

[0752] As a result, the standard deviation of the difference σ DF_k134,k135 (=15.26(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k134,k136 (=41.83(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k135,k136 (=31.27(%)) is the threshold σ th (=15(%)) is greater than

[0753] Therefore, the two curves k134 and k135 are different, the two curves k134 and k136 are different, and the two curves k135 and k136 are different (see "◯" in FIG. 91). Therefore, the curves k134 to k136 are mutually different curves.

[0754] FIG. 92 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is seven.

[0755] 92, each of the curves k137 to k139 is a curve CUR generated by the above-described method by the analysis device 2. The curve k137 represents the integral spectrum of coffee (WONDA), the curve k138 represents the integral spectrum of coffee (Craft Boss), and the curve k139 represents the integral spectrum of coffee (Gold Brew).

[0756] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=7 integral values) for each of curves k137 to k139 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 717.6 mV.

[0757] As a result, in FIG. 92, each of classes 1 to 6 consists of a predetermined potential section (=integral value extraction potential) of 717.6 mV, and class 7 consists of a predetermined potential section (=integral value extraction potential) of 680.8 mV.

[0758] FIG. 93 is a diagram showing the results of determining whether or not the curves k137 to k139 shown in FIG. 92 are different from one another.

[0759] Whether or not the curves k137 to k139 are different from one another is determined by determining whether or not two of the curves k137 to k139 are different for all combinations of two of the curves k137 to k139.

[0760] There are three combinations of two curves among the curves k137 to k139: (k137, k138), (k137, k139), and (k138, k139).

[0761] Therefore, FIG. 93 shows whether the two curves differ for the three cases (k137, k138), (k137, k139), and (k138, k139).

[0762] Referring to FIG. 93, the standard deviation σ of the difference between the plurality of integral values ​​(=7 integral values) on the curve k137 and the plurality of integral values ​​(=7 integral values) on the curve k138 DF_k137,k138 is 16.64(%), and the standard deviation σ of the difference between the multiple integral values ​​(=7 integral values) on the curve k137 and the multiple integral values ​​(=7 integral values) on the curve k139 DF_k137,k139 is 45.35(%), and the standard deviation σ of the difference between the multiple integral values ​​(=7 integral values) on the curve k138 and the multiple integral values ​​(=7 integral values) on the curve k139 DF_k138,k139 is 33.46(%).

[0763] As a result, the standard deviation of the difference σ DF_k137,k138 (=16.64(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k137,k139 (=45.35(%)) is the threshold value σth (=15(%)) and the standard deviation of the difference σ DF_k138,k139 (=33.46(%)) is the threshold value σ th (=15(%)) is greater than

[0764] Therefore, the two curves k137 and k138 are different, the two curves k137 and k139 are different, and the two curves k138 and k139 are different (see "◯" in FIG. 93). Therefore, the curves k137 to k139 are mutually different curves.

[0765] FIG. 94 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is eight.

[0766] 94, each of the curves k140 to k142 is a curve CUR created by the above-described method by the analysis device 2. The curve k140 represents the integral spectrum of coffee (WONDA), the curve k141 represents the integral spectrum of coffee (Craft Boss), and the curve k142 represents the integral spectrum of coffee (Gold Brew).

[0767] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=8 integral values) for each of curves k140 to k142 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 625.6 mV.

[0768] As a result, in FIG. 94, each of classes 1 to 7 consists of a predetermined potential section (=integral value extraction potential) of 625.6 mV, and class 8 consists of a predetermined potential section (=integral value extraction potential) of 607.2 mV.

[0769] FIG. 95 is a diagram showing the results of determining whether or not the curves k140 to k142 shown in FIG. 94 are different from one another.

[0770] Whether or not the curves k140 to k142 are different from one another is determined by determining whether or not two curves among the curves k140 to k142 are different for all combinations of two curves among the curves k140 to k142.

[0771] There are three combinations of two curves among the curves k140 to k142: (k140, k141), (k140, k142), and (k141, k142).

[0772] Therefore, FIG. 95 shows whether or not the two curves differ for the three cases (k140, k141), (k140, k142), and (k141, k142).

[0773] Referring to FIG. 95, the standard deviation σ of the difference between the plurality of integral values ​​(=8 integral values) on the curve k140 and the plurality of integral values ​​(=8 integral values) on the curve k141 is DF_k140,k141 is 18.56(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k140 and the multiple integral values ​​(=8 integral values) on the curve k142 DF_k140,k142 is 59.01(%), and the standard deviation σ of the difference between the multiple integral values ​​(=8 integral values) on the curve k141 and the multiple integral values ​​(=8 integral values) on the curve k142 DF_k141,k142 is 48.44(%).

[0774] As a result, the standard deviation of the difference σ DF_k140,k141 (=18.56(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k140,k142 (=59.01(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k141,k142 (=48.44(%)) is the threshold value σ th (=15(%)) is greater than

[0775] Therefore, the two curves k140 and k141 are different, the two curves k140 and k142 are different, and the two curves k141 and k142 are different (see "◯" in FIG. 95). Therefore, the curves k140 to k142 are mutually different curves.

[0776] FIG. 96 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is nine.

[0777] 96, each of the curves k143 to k145 is a curve CUR created by the above-described method by the analysis device 2. The curve k143 represents the integral spectrum of coffee (WONDA), the curve k144 represents the integral spectrum of coffee (Craft Boss), and the curve k145 represents the integral spectrum of coffee (Gold Brew).

[0778] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(= 9 integral values) for each of curves k143 to k145 were the same as those shown in Table 5 above, except that the specified potential range (= integral value extraction potential) of 18.4 mV was changed to 552 mV.

[0779] As a result, in FIG. 96, each of classes 1 to 8 consists of a predetermined potential section (=integral value extraction potential) of 552 mV, and class 9 consists of a predetermined potential section (=integral value extraction potential) of 570.4 mV.

[0780] FIG. 97 is a diagram showing the results of determining whether or not the curves k143 to k145 shown in FIG. 96 are different from one another.

[0781] Whether or not the curves k143 to k145 are different from one another is determined by determining whether or not two curves among the curves k143 to k145 are different for all combinations of two curves among the curves k143 to k145.

[0782] There are three combinations of two curves among the curves k143 to k145: (k143, k144), (k143, k145), and (k144, k145).

[0783] Therefore, FIG. 97 shows whether the two curves differ for the three cases (k143, k144), (k143, k145), and (k144, k145).

[0784] Referring to FIG. 97, the standard deviation σ of the difference between the plurality of integral values ​​(=9 integral values) on the curve k143 and the plurality of integral values ​​(=9 integral values) on the curve k144 is DF_k143,k144 is 21.65(%), and the standard deviation σ of the difference between the multiple integral values ​​(=9 integral values) on the curve k143 and the multiple integral values ​​(=9 integral values) on the curve k145 DF_k143,k145 is 70.36(%), and the standard deviation σ of the difference between the multiple integral values ​​(=9 integral values) on the curve k144 and the multiple integral values ​​(=9 integral values) on the curve k145 DF_k144,k145 is 65.19(%).

[0785] As a result, the standard deviation of the difference σ DF_k143,k144 (=21.65(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k143,k145 (=70.36(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k144,k145 (=65.19(%)) is the threshold value σ th (=15(%)) is greater than

[0786] Therefore, the two curves k143 and k144 are different, the two curves k143 and k145 are different, and the two curves k144 and k145 are different (see "◯" in FIG. 97). Therefore, the curves k143 to k145 are mutually different curves.

[0787] FIG. 98 shows the integral spectrum for WONDA, Craft Boss, and Gold Brew when the number of integrals is 10.

[0788] 98, each of the curves k146 to k148 is a curve CUR created by the above-described method by the analysis device 2. The curve k146 represents the integral spectrum of coffee (WONDA), the curve k147 represents the integral spectrum of coffee (Craft Boss), and the curve k148 represents the integral spectrum of coffee (Gold Brew).

[0789] The measurement conditions of the cyclic voltammograms that were used to calculate the multiple integral values ​​(=10 integral values) for each of curves k146 to k148 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 496.8 mV.

[0790] As a result, in FIG. 98, each of classes 1 to 9 consists of a predetermined potential section (=integral value extraction potential) of 496.8 mV, and class 10 consists of a predetermined potential section (=integral value extraction potential) of 515.2 mV.

[0791] FIG. 99 is a diagram showing the results of determining whether or not the curves k146 to k148 shown in FIG. 98 are different from one another.

[0792] Whether or not the curves k146 to k148 are different from one another is determined by determining whether or not two of the curves k146 to k148 are different for all combinations of two of the curves k146 to k148.

[0793] There are three combinations of two curves among the curves k146 to k148: (k146, k147), (k146, k148), and (k147, k148).

[0794] Therefore, FIG. 99 shows whether the two curves differ for the three cases (k146, k147), (k146, k148), and (k147, k148).

[0795] Referring to FIG. 99, the standard deviation σ of the difference between the plurality of integral values ​​(=10 integral values) on the curve k146 and the plurality of integral values ​​(=10 integral values) on the curve k147 DF_k146,k147 is 25.21(%), and the standard deviation σ of the difference between the multiple integral values ​​(=10 integral values) on the curve k146 and the multiple integral values ​​(=10 integral values) on the curve k148 DF_k146,k148 is 84.29(%), and the standard deviation σ of the difference between the multiple integral values ​​(=10 integral values) on the curve k147 and the multiple integral values ​​(=10 integral values) on the curve k148 DF_k147,k148 is 78.52(%).

[0796] As a result, the standard deviation of the difference σ DF_k146,k147 (=25.21(%)) is the threshold σ th (=15(%)) and the standard deviation of the difference σ DF_k146,k148 (=84.29(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k147,k148 (=78.52(%)) is the threshold value σ th (=15(%)) is greater than

[0797] Therefore, the two curves k146 and k147 are different, the two curves k146 and k148 are different, and the two curves k147 and k148 are different (see "◯" in FIG. 99). Therefore, the curves k146 to k148 are mutually different curves.

[0798] FIG. 100 shows the integral spectrum for WONDA, Craft Boss, and Gold Brew when the number of integrals is 15.

[0799] 100, each of curves k149 to k151 is a curve CUR created by the above-described method by analysis device 2. Curve k149 represents the integral spectrum of coffee (WONDA), curve k150 represents the integral spectrum of coffee (Craft Boss), and curve k151 represents the integral spectrum of coffee (Gold Brew).

[0800] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=15 integral values) for each of curves k149 to k151 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 349.6 mV.

[0801] As a result, in FIG. 100, each of classes 1 to 14 consists of a predetermined potential section (=integral value extraction potential) of 349.6 mV, and class 15 consists of a predetermined potential section (=integral value extraction potential) of 92 mV.

[0802] FIG. 101 is a diagram showing the result of determining whether or not the curves k149 to k151 shown in FIG. 100 are different from one another.

[0803] Whether or not the curves k149 to k151 are different from one another is determined by determining whether or not two of the curves k149 to k151 are different for all combinations of two of the curves k149 to k151.

[0804] There are three combinations of two curves among the curves k149 to k151: (k149, k150), (k149, k151), and (k150, k151).

[0805] Therefore, FIG. 101 shows whether the two curves differ for the three cases (k149, k150), (k149, k151), and (k150, k151).

[0806] Referring to FIG. 101, the standard deviation σ of the difference between the plurality of integral values ​​(=15 integral values) on the curve k149 and the plurality of integral values ​​(=15 integral values) on the curve k150 DF_k149,k150 is 54.79(%), and the standard deviation σ of the difference between the multiple integral values ​​(=15 integral values) on the curve k149 and the multiple integral values ​​(=15 integral values) on the curve k151 DF_k149,k151is 67.29(%), and the standard deviation σ of the difference between the multiple integral values ​​(=15 integral values) on the curve k150 and the multiple integral values ​​(=15 integral values) on the curve k151 DF_k150,k151 is 48.38(%).

[0807] As a result, the standard deviation of the difference σ DF_k149,k150 (=54.79(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k149,k151 (=67.29(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k150,k151 (=48.38(%)) is the threshold σ th (=15(%)) is greater than

[0808] Therefore, the two curves k149 and k150 are different, the two curves k149 and k151 are different, and the two curves k150 and k151 are different (see "◯" in FIG. 101). Therefore, the curves k149 to k151 are mutually different curves.

[0809] FIG. 102 shows the integral spectrum for WONDA, Craft Boss, and Gold Brew when the number of integrals is 20.

[0810] 102, each of the curves k152 to k154 is a curve CUR generated by the above-described method by the analysis device 2. The curve k152 represents the integral spectrum of coffee (WONDA), the curve k153 represents the integral spectrum of coffee (Craft Boss), and the curve k154 represents the integral spectrum of coffee (Gold Brew).

[0811] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=20 integral values) for each of curves k152 to k154 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 257.6 mV.

[0812] As a result, in FIG. 102, each of classes 1 to 19 consists of a predetermined potential section (=integral value extraction potential) of 257.6 mV, and class 20 consists of a predetermined potential section (=integral value extraction potential) of 92 mV.

[0813] FIG. 103 is a diagram showing the results of determining whether or not the curves k152 to k154 shown in FIG. 102 are different from one another.

[0814] Whether or not the curves k152 to k154 differ from one another is determined by determining whether or not two curves among the curves k152 to k154 differ from one another for all combinations of two curves among the curves k152 to k154.

[0815] There are three combinations of two curves among the curves k152 to k154: (k152, k153), (k152, k154), and (k153, k154).

[0816] Therefore, FIG. 103 shows whether the two curves differ for the three cases (k152, k153), (k152, k154), and (k153, k154).

[0817] Referring to FIG. 103, the standard deviation σ of the difference between the plurality of integral values ​​(=20 integral values) on the curve k152 and the plurality of integral values ​​(=20 integral values) on the curve k153 DF_k152,k153 is 34.35(%), and the standard deviation σ of the difference between the multiple integral values ​​(=20 integral values) on the curve k152 and the multiple integral values ​​(=20 integral values) on the curve k154 DF_k152,k154 is 77.77(%), and the standard deviation σ of the difference between the multiple integral values ​​(=20 integral values) on the curve k153 and the multiple integral values ​​(=20 integral values) on the curve k154 DF_k153,k154 is 64.33(%).

[0818] As a result, the standard deviation of the difference σ DF_k152,k153 (=34.35(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k152,k154(=77.77(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k153,k154 (=64.33(%)) is the threshold value σ th (=15(%)) is greater than

[0819] Therefore, the two curves k152 and k153 are different, the two curves k152 and k154 are different, and the two curves k153 and k154 are different (see "◯" in FIG. 103). Therefore, the curves k152 to k154 are mutually different curves.

[0820] FIG. 104 is a diagram showing the integral spectrum for WONDA, Craft Boss, and Gold Brew when the number of integrals is 39.

[0821] 104, each of the curves k155 to k157 is a curve CUR created by the above-described method by the analysis device 2. The curve k155 represents the integral spectrum of coffee (WONDA), the curve k156 represents the integral spectrum of coffee (Craft Boss), and the curve k157 represents the integral spectrum of coffee (Gold Brew).

[0822] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=39 integral values) for each of curves k155 to k157 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 128.8 mV.

[0823] As a result, in FIG. 104, each of classes 1 to 38 consists of a predetermined potential section (=integral value extraction potential) of 128.8 mV, and class 39 consists of a predetermined potential section (=integral value extraction potential) of 92 mV.

[0824] FIG. 105 is a diagram showing the results of determining whether or not the curves k155 to k157 shown in FIG. 104 are different from one another.

[0825] Whether or not the curves k155 to k157 are different from one another is determined by determining whether or not two curves among the curves k155 to k157 are different for all combinations of two curves among the curves k155 to k157.

[0826] There are three combinations of two curves among the curves k155 to k157: (k155, k156), (k155, k157), and (k156, k157).

[0827] Therefore, FIG. 105 shows whether the two curves differ for the three cases (k155, k156), (k155, k157), and (k156, k157).

[0828] Referring to FIG. 105, the standard deviation σ of the difference between the plurality of integral values ​​(=39 integral values) on the curve k155 and the plurality of integral values ​​(=39 integral values) on the curve k156 DF_k155,k156 is 40.61(%), and the standard deviation σ of the difference between the multiple integral values ​​(=39 integral values) on the curve k155 and the multiple integral values ​​(=39 integral values) on the curve k157 DF_k155,k157 is 67.02(%), and the standard deviation σ of the difference between the multiple integral values ​​(=39 integral values) on the curve k156 and the multiple integral values ​​(=39 integral values) on the curve k157 DF_k156,k157 is 50.55(%).

[0829] As a result, the standard deviation of the difference σ DF_k155,k156 (=40.61(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k155,k157 (=67.02(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k156,k157 (=50.55(%)) is the threshold σ th (=15(%)) is greater than

[0830] Therefore, the two curves k155 and k156 are different, the two curves k155 and k157 are different, and the two curves k156 and k157 are different (see "◯" in FIG. 105). Therefore, the curves k155 to k157 are mutually different curves.

[0831] FIG. 106 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 54.

[0832] 106, each of the curves k158 to k160 is a curve CUR created by the above-described method by the analysis device 2. The curve k158 represents the integral spectrum of coffee (WONDA), the curve k159 represents the integral spectrum of coffee (Craft Boss), and the curve k160 represents the integral spectrum of coffee (Gold Brew).

[0833] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=54 integral values) for each of curves k158 to k160 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 92 mV.

[0834] As a result, in FIG. 106, each of classes 1 to 53 consists of a predetermined potential section of 92 mV (=integral value extraction potential), and class 54 consists of a predetermined potential section of 110.4 mV (=integral value extraction potential).

[0835] FIG. 107 is a diagram showing the results of determining whether or not the curves k158 to k160 shown in FIG. 106 are different from one another.

[0836] Whether or not the curves k158 to k160 are different from one another is determined by determining whether or not two of the curves k158 to k160 are different for all combinations of two of the curves k158 to k160.

[0837] There are three combinations of two curves among the curves k158 to k160: (k158, k159), (k158, k160), and (k159, k160).

[0838] Therefore, FIG. 107 shows whether the two curves differ for the three cases (k158, k159), (k158, k160), and (k159, k160).

[0839] Referring to FIG. 107, the standard deviation σ of the difference between the plurality of integral values ​​(=54 integral values) on the curve k158 and the plurality of integral values ​​(=54 integral values) on the curve k159 is DF_k158,k159 is 37.13(%), and the standard deviation σ of the difference between the multiple integral values ​​(=54 integral values) on the curve k158 and the multiple integral values ​​(=54 integral values) on the curve k160 DF_k158,k160 is 64.89(%), and the standard deviation σ of the difference between the multiple integral values ​​(=54 integral values) on the curve k159 and the multiple integral values ​​(=54 integral values) on the curve k160 DF_k159,k160 is 52.12(%).

[0840] As a result, the standard deviation of the difference σ DF_k158,k159 (=37.13(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k158,k160 (=64.89(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k159,k160 (=52.12(%)) is the threshold σ th (=15(%)) is greater than

[0841] Therefore, the two curves k158 and k159 are different, the two curves k158 and k160 are different, and the two curves k159 and k160 are different (see "◯" in FIG. 107). Therefore, the curves k158 to k160 are mutually different curves.

[0842] FIG. 108 is a diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 135.

[0843] 108, each of the curves k161 to k163 is a curve CUR created by the above-described method by the analysis device 2. The curve k161 represents the integral spectrum of coffee (WONDA), the curve k162 represents the integral spectrum of coffee (Craft Boss), and the curve k163 represents the integral spectrum of coffee (Gold Brew).

[0844] The measurement conditions for the cyclic voltammograms used to calculate the multiple integral values ​​(=135 integral values) for each of curves k161 to k163 were the same as those shown in Table 5 above, except that the specified potential range (=integral value extraction potential) of 18.4 mV was changed to 36.8 mV.

[0845] As a result, in FIG. 108, each of classes 1 to 134 consists of a predetermined potential section (=integral value extraction potential) of 36.8 mV, and class 135 consists of a predetermined potential section (=integral value extraction potential) of 55.2 mV.

[0846] FIG. 109 is a diagram showing the results of determining whether or not the curves k161 to k163 shown in FIG. 108 are different from one another.

[0847] Whether or not the curves k161 to k163 are different from one another is determined by determining whether or not two curves among the curves k161 to k163 are different for all combinations of two curves among the curves k161 to k163.

[0848] There are three combinations of two curves among the curves k161 to k163: (k161, k162), (k161, k163), and (k162, k163).

[0849] Therefore, FIG. 109 shows whether the two curves differ for the three cases (k161, k162), (k161, k163), and (k162, k163).

[0850] Referring to FIG. 109, the standard deviation σ of the difference between the plurality of integral values ​​(=135 integral values) on the curve k161 and the plurality of integral values ​​(=135 integral values) on the curve k162 DF_k161,k162 is 37.87(%), and the standard deviation σ of the difference between the multiple integral values ​​(=135 integral values) on the curve k161 and the multiple integral values ​​(=135 integral values) on the curve k163 DF_k161,k163 is 66.31(%), and the standard deviation σ of the difference between the multiple integral values ​​(=135 integral values) on the curve k162 and the multiple integral values ​​(=135 integral values) on the curve k163 DF_k162,k163 is 50.33(%).

[0851] As a result, the standard deviation of the difference σ DF_k161,k162 (=37.87(%)) is the threshold σ th (=15(%)) and the standard deviation of the difference σ DF_k161,k163 (=66.31(%)) is the threshold value σ th (=15(%)) and the standard deviation of the difference σ DF_k162,k163 (=50.33(%)) is the threshold σ th (=15(%)) is greater than

[0852] Therefore, the two curves k161 and k162 are different, the two curves k161 and k163 are different, and the two curves k162 and k163 are different (see "◯" in FIG. 109). Therefore, the curves k161 to k163 are mutually different curves.

[0853] FIG. 20 above shows the integral spectra of coffee (WONDA), coffee (Craft Boss), and coffee (Gold Brew) when the number of integrals (=number of classes) is 271, and FIG. 21 above shows the standard deviation of the difference σ DF_k11,k12 (=37.00(%)), standard deviation of the difference σ DF_k11,k13 (=66.20(%)) and the standard deviation of the difference σ DF_k12,k13 (=50.55(%)) are all within the threshold σ th (=15(%)).

[0854] Therefore, it was found that when the number of integral values ​​is 6, 7, 8, 9, 10, 15, 20, 39, 54, 135, and 271, it is possible to obtain integral spectrum (= curve CUR) that uniquely identifies coffee (WONDA), coffee (Craft Boss), and coffee (Gold Brew), respectively.

[0855] That is, when the number of integral values ​​is six, curve k134 is an integral spectrum for uniquely identifying coffee (WONDA), curve k135 is an integral spectrum for uniquely identifying coffee (Craft Boss), and curve k136 is an integral spectrum for uniquely identifying coffee (Gold Brew).

[0856] Furthermore, when the number of integral values ​​is seven, curve k137 is an integral spectrum for uniquely identifying coffee (WONDA), curve k138 is an integral spectrum for uniquely identifying coffee (Craft Boss), and curve k139 is an integral spectrum for uniquely identifying coffee (Gold Brew).

[0857] Furthermore, when the number of integral values ​​is eight, curve k140 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k141 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k142 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0858] Furthermore, when the number of integral values ​​is nine, curve k143 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k144 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k145 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0859] Furthermore, when the number of integral values ​​is 10, curve k146 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k147 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k148 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0860] Furthermore, when the number of integral values ​​is 15, curve k149 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k150 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k151 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0861] Furthermore, when the number of integral values ​​is 20, curve k152 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k153 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k154 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0862] Furthermore, when the number of integral values ​​is 39, curve k155 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k156 is an i...

Claims

1. a sensor which is used to calculate an integral value in a predetermined potential interval of a current-potential characteristic of a cyclic voltammogram of an analysis object made of a liquid based on the current-potential characteristic of the cyclic voltammogram for all of the predetermined potential intervals to calculate a plurality of integral values ​​in the plurality of predetermined potential intervals, and which is used to create a curve showing the dependency of the integral value on the predetermined potential interval based on the plurality of integral values ​​calculated in the plurality of predetermined potential intervals, and which is detachable from a measuring device which measures the cyclic voltammogram, and which is discarded each time measurement of the cyclic voltammogram is performed; A substrate having a flat plate shape; a first wiring arranged on one surface of the substrate along a first direction; a second wiring arranged on one surface of the substrate along the first direction at a predetermined interval from the first wiring in a second direction perpendicular to the first direction; a third wiring arranged on one surface of the substrate along the first direction at a predetermined interval from the second wiring in the second direction; a working electrode that is disposed on one surface of the substrate, electrically connected to one end of the first wiring, and that exchanges electrons with the object to be analyzed; a reference electrode that is disposed on one surface of the substrate, electrically connected to one end of the second wiring, and serves as a reference for determining the potential of the working electrode; a counter electrode that is disposed on one surface of the substrate, electrically connected to one end of the third wiring, and returns to a system a current value equal to a current value generated in the working electrode; the first wiring, the second wiring, and the third wiring are electrically connected to the measuring instrument when the other end of the substrate in the first direction is inserted into a recess of the measuring instrument; The sensor, wherein the working electrode has a circular or rectangular planar shape.

2. An analysis device that creates an index curve, which is a curve that serves as an index for identifying an analysis target made of a liquid, based on a current-potential characteristic of a cyclic voltammogram of the analysis target measured by a cyclic voltammetry method, comprising: a calculation unit that executes a calculation process of calculating an integral value in a predetermined potential section of the current-potential characteristic based on the current-potential characteristic for all the predetermined potential sections to calculate a plurality of the integral values ​​in the plurality of the predetermined potential sections; a creation unit that creates, as the index curve, a curve indicating the dependency of the integral value on the predetermined potential interval based on the plurality of integral values ​​in the plurality of predetermined potential intervals calculated by the arithmetic unit.

3. The analysis device according to claim 2 , wherein the arithmetic unit, in the arithmetic processing, calculates an area of ​​the cyclic voltammogram in one of the predetermined potential sections for each of the plurality of predetermined potential sections to calculate the plurality of integral values.

4. 4. The analytical device according to claim 3, wherein the arithmetic unit, in the arithmetic processing, performs a subtraction process to subtract a current value of a reduction wave from a current value of an oxidation wave of the cyclic voltammogram at one unit potential in the one predetermined potential section to calculate an intensity of the cyclic voltammogram at the one unit potential for all unit potentials in the one predetermined potential section to calculate a plurality of intensities in the one predetermined potential section, and calculates a sum of the calculated intensities as the area of ​​the cyclic voltammogram in the one predetermined potential section.

5. 5. The analytical device according to claim 4, wherein the calculation unit, in the calculation process, calculates an intensity of the cyclic voltammogram at the one unit potential in the one predetermined potential section by subtracting a current value of the reduction wave from a current value of the oxidation wave of the cyclic voltammogram at the one unit potential.

6. 6. The analytical device according to claim 5, wherein the calculation unit, in the calculation process, calculates an intensity consisting of a negative value as the intensity of the cyclic voltammogram at the one unit potential when the current value of the reduction wave at the one unit potential is greater than the current value of the oxidation wave.

7. a judgment unit that judges whether or not the P integral values ​​are different from each other when the calculation unit calculates the integral values ​​in the predetermined potential sections for each of P (P is an integer equal to or greater than 2) analysis objects in the calculation process, 3. The analysis device according to claim 2, wherein the creation unit creates the P curves based on the P integral values ​​when the judgment unit judges that the P integral values ​​are different from each other.

8. Z (Z is a combination of the two [several integral values] when extracting two [several integral values] from the P [several integral values]) Ｐ C ２ The two integral values ​​of the Z set are extracted by dividing the two integral values ​​of the Z set by the two integral values ​​of the n set ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] (where n represents the total number of the predetermined potential sections, and i ≠ j), The judgment unit is configured to calculate the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] for all combinations of the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ8. The analysis device according to claim 7, wherein when it is determined that the P integral values ​​are different, it is determined that the P integral values ​​are different from each other.

9. The [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ] are n classes Cls １ ~Cls ｎ and the [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] are the n classes Cls １ ~Cls ｎ It is associated with The judgment unit is configured to calculate the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] based on one class Cls ｋ (k is any value from 1 to n) ｋ＿ｉ and the integral ITG ｋ＿ｊ Difference DF ｋ The calculation of the n classes Cls １ ~Cls ｎ Execute for all n differential DFs １ ~D.F. ｎ Calculate the n calculated differences DF １ ~D.F. ｎ When it is determined that the standard deviation of is greater than the threshold value, the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ 9. The analysis device according to claim 8, wherein the analysis device determines that the two are different.

10. The P analytes have P mutually different names, When two analysis objects having different names among the P analysis objects are defined as a first analysis object and a second analysis object, and two analysis objects included in the first analysis object and different in type are defined as a third analysis object and a fourth analysis object, 8. The analysis device according to claim 7, wherein the judgment unit judges that the [plurality of integral values] for the first analysis object and the [plurality of integral values] for the second analysis object differ from each other when a first standard deviation, which is a standard deviation of a difference between the [plurality of integral values] for the first analysis object and the [plurality of integral values] for the second analysis object, is greater than a first threshold value, and judges that the [plurality of integral values] for the third analysis object and the [plurality of integral values] for the fourth analysis object differ from each other when a second standard deviation, which is a standard deviation of a difference between the [plurality of integral values] for the third analysis object and the [plurality of integral values] for the fourth analysis object, is greater than a second threshold value that is smaller than the first threshold value.

11. The analysis device according to claim 2 , further comprising a display unit for displaying the curve created by the creation unit.

12. a receiving unit that receives measurement data of a cyclic voltammogram of an object to be analyzed, which is made of a liquid and is measured using a cyclic voltammetry method, from a sensor device via wired or wireless communication, and receives a curve created based on the measurement data, which shows the dependency of a plurality of integral values ​​in a plurality of predetermined potential intervals of the cyclic voltammogram on the predetermined potential intervals, as an "index curve, which is a curve that serves as an index for identifying the object to be analyzed" from an analysis device via a network; a transmitting unit that transmits analysis data including a current-potential characteristic of the measurement data received by the receiving unit to the analyzing device via a network; a display unit for displaying the curve received by the receiving unit as the index curve.

13. A sensor device comprising the sensor according to claim 1 and a measuring instrument that measures a cyclic voltammogram of an analysis object made of a liquid using the sensor; An analysis system comprising the analysis device according to any one of claims 2 to 11.

14. A sensor device comprising the sensor according to claim 1 and a measuring instrument that measures a cyclic voltammogram of an analysis object made of a liquid using the sensor; An analysis device according to any one of claims 2 to 11, An analysis system comprising the terminal device according to claim 12.

15. A program for causing a computer to execute the creation of an index curve, which is a curve that serves as an index for identifying an analyte, based on a current-potential characteristic of a cyclic voltammogram of the analyte made of a liquid measured by a cyclic voltammetry method, comprising: a first step of executing a calculation process in which an arithmetic unit calculates an integral value of the current-potential characteristic in a predetermined potential section based on the current-potential characteristic for all the predetermined potential sections to calculate a plurality of the integral values ​​in a plurality of the predetermined potential sections; a second step in which a creation unit creates, as the index curve, a curve indicating the dependency of the integral value on the predetermined potential interval based on the multiple integral values ​​in the multiple predetermined potential intervals calculated in the arithmetic processing of the first step.

16. 16. The program for causing a computer to execute the program according to claim 15, wherein the arithmetic unit, in the arithmetic processing of the first step, calculates an area of ​​the cyclic voltammogram in one of the predetermined potential sections for all of the plurality of predetermined potential sections to calculate the plurality of integral values.

17. 17. The program for causing a computer to execute the program according to claim 16, wherein the arithmetic unit, in the arithmetic processing of the first step, performs a subtraction process to subtract a current value of a reduction wave from a current value of an oxidation wave of the cyclic voltammogram at one unit potential in the one predetermined potential section to calculate an intensity of the cyclic voltammogram at the one unit potential for all unit potentials in the one predetermined potential section to calculate a plurality of intensities in the one predetermined potential section, and calculates a sum of the calculated intensities as the area of ​​the cyclic voltammogram in the one predetermined potential section.

18. 18. The program for causing a computer to execute the program according to claim 17, wherein the calculation unit, in the calculation processing of the first step, calculates an intensity of the cyclic voltammogram at the one unit potential in the one predetermined potential section by subtracting a current value of the reduction wave from a current value of the oxidation wave of the cyclic voltammogram at the one unit potential.

19. 20. The program for causing a computer to execute the program according to claim 18, wherein the arithmetic unit calculates an intensity consisting of a negative value as the intensity of the cyclic voltammogram at the one unit potential when the current value of the reduction wave at the one unit potential is greater than the current value of the oxidation wave at the one unit potential in the arithmetic processing of the first step.

20. a determination unit causes the computer to further execute a third step of determining whether or not the P number of [the plurality of integral values] differ from each other when the calculation unit calculates the plurality of integral values ​​in the plurality of predetermined potential sections for each of the P number of analysis objects (P is an integer equal to or greater than 2) in the calculation process of the first step, 16. The program for causing a computer to execute the program according to claim 15, wherein the creation unit creates the P curves based on the P integral values ​​in the second step when the judgment unit judges that the P integral values ​​are different from each other in the third step.

21. Z (Z is the number of combinations of the two [several integral values] when extracting two [several integral values] from the P [several integral values]) Ｐ C ２ The two [several integral values] of the Z set when extracting the two [several integral values] of the Z set are converted into two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] (where n represents the total number of the predetermined potential sections, and i ≠ j), In the third step, the judgment unit judges whether the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] for all combinations of the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ 21. The program for causing a computer to execute the program according to claim 20, wherein when it is determined that the P number of [the plurality of integral values] differ, it is determined that the P number of [the plurality of integral values] differ from each other.

22. The [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ] are n classes Cls １ ~Cls ｎ and the [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] are the n classes Cls １ ~Cls ｎ It is associated with In the third step, the judgment unit １＿ｉ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] based on one class Cls ｋ (k is any value from 1 to n) ｋ＿ｉ and the integral ITG ｋ＿ｊ Difference DF ｋ The calculation of the n classes Cls １ ~Cls ｎ Execute for all n differential DFs １ ~D.F. ｎ Calculate the n calculated differences DF １ ~D.F. ｎ When it is determined that the standard deviation of is greater than the threshold value, the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ 22. The program executed by a computer according to claim 21, wherein the program determines that the two are different.

23. The P analytes have P mutually different names, When two analysis objects having different names among the P analysis objects are defined as a first analysis object and a second analysis object, and two analysis objects included in the first analysis object and different in type are defined as a third analysis object and a fourth analysis object, 21. The program for causing a computer to execute the program of claim 20, wherein in the third step, the judgment unit judges that the [plurality of integral values] for the first analysis object and the [plurality of integral values] for the second analysis object differ from each other when a first standard deviation that is a standard deviation of a difference between the [plurality of integral values] for the first analysis object and the [plurality of integral values] for the second analysis object is greater than a first threshold value, and judges that the [plurality of integral values] for the third analysis object and the [plurality of integral values] for the fourth analysis object differ from each other when a second standard deviation that is a standard deviation of a difference between the [plurality of integral values] for the third analysis object and the [plurality of integral values] for the fourth analysis object is greater than a second threshold value that is smaller than the first threshold value.

24. 16. The program for causing a computer to execute the program according to claim 15, further comprising a fourth step of causing a display unit to display the curve created by the creation unit.

Images