Sensor, analysis device, terminal device, analysis system using these, and program to be executed by computer

Abstract

Provided is a sensor 11 that is used for measuring a cyclic voltammogram of an object to be analyzed consisting of a liquid, that is detachable from a cyclic voltammogram measurement instrument, and that is discarded every time the measurement of the cyclic voltammogram is executed. The sensor comprises: a substrate 111 having a flat plate shape; a working electrode 112; a counter electrode 113; a reference electrode 114; and wirings 115-117. The wirings 115-117 are arranged on one surface of the substrate 111. The working electrode 112 is disposed on the one surface of the substrate 111 and is electrically connected to one end of the wiring 115. The counter electrode 113 is disposed on the one surface of the substrate 111 and is electrically connected to one end of the wiring 116. The reference electrode 114 is disposed on the 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 square.

Description

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

[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.

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

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

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

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

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

[0007] However, 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.

[0014] (Configuration 1) According to an embodiment of the present invention, a 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 arranged on one side of the substrate, is electrically connected to one end of the first wire, and is an electrode that 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 to return 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 another 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 arithmetic unit calculates the area of ​​the cyclic voltammogram in one predetermined potential interval for all of the plurality of predetermined potential intervals in the arithmetic processing, 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 calculated multiple 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 the calculation process, 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 configuration 7, when extracting two [plural integral values] from P [plural integral values], Z (Z is the number of combinations of two [plural integral values]) Ｐ C ２ When extracting two [plurality of integral values] of Z sets, two [plurality of integral values] of Z sets are converted into two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] (n represents the total number of predetermined potential sections, and i≠j), the judgment unit calculates Z sets of two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] included in the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] for all combinations of [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] are determined to be different, it is determined that the P [plurality of integral values] are different from each other.

[0022] (Configuration 9) In configuration 8, [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ] are n classes Cls １ ~Cls ｎ and n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] are n classes Cls １ ~Cls ｎ The decision unit is associated with 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 one of 1 to n) ｋ＿ｉ and the integral value ITG ｋ＿ｊ Difference DF ｋ Calculating n classes Cls １ ~Cls ｎ Execute for all of the n differential DFs １ ~DF ｎ Calculate the n calculated differences DF １ ~DF 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 ] is determined to be different.

[0023] (Configuration 10) In Configuration 7, when the P analysis targets have P names that are different from one another, two analysis targets with different names among the P analysis targets are defined as the first and second analysis targets, and two analysis targets that are included in the first analysis target and are different in type from one another are defined as the third and fourth analysis targets, The judgment unit judges that the [multiple integral values] for the first analysis object and the [multiple integral values] for the second analysis object are different 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 are different 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) According to another 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 execute the creation of an index curve, which is a curve that serves as an index when identifying an analysis object consisting of a liquid measured using cyclic voltammetry, based on the current-potential characteristics of a cyclic voltammogram of the analysis object, the program causing a computer to execute the following steps: a first step in which an arithmetic unit executes an arithmetic 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 a plurality of predetermined potential intervals; and a second step in which a creation unit creates, as an index curve, a curve that shows 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 in the arithmetic process of the first step.

[0029] (Configuration 16) In configuration 15, in the first step of the calculation process, the calculation unit calculates the area of ​​the cyclic voltammogram in one predetermined potential section for all of the plurality of predetermined potential sections, 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 subtraction 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 one unit potential in the calculation process of the first step, 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, when the calculation unit calculates multiple integral values ​​in multiple predetermined potential intervals for each of P (P is an integer greater than or equal to 2) analysis objects in the calculation processing of the first step, the judgment unit further causes the computer to execute a third step of determining whether the P [multiple integral values] differ from each other, and when the judgment unit determines in the third step that the P [multiple integral values] differ from each other, the creation unit creates P curves based on the P [multiple integral values] in the second step.

[0034] (Configuration 21) In configuration 20, when extracting two [plurality of integral values] from P [plurality of integral values], Z (Z is the number of combinations of two [plurality of integral values]) Ｐ C ２When extracting two [plurality of integral values] of Z sets, two [plurality of integral values] of Z sets are converted into two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] (n represents the total number of predetermined potential sections, and i≠j), the judgment unit, in the third step, calculates Z sets of two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] included in the two [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] for all combinations of [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ], [n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] are determined to be different, it is determined that the P [plurality of integral values] are different from each other.

[0035] (Configuration 22) In configuration 21, [n integral values ​​ITG １＿ｉ ~ITG ｎ＿ｉ ] are n classes Cls １ ~Cls ｎ and n integral values ​​ITG １＿ｊ ~ITG ｎ＿ｊ ] are n classes Cls １ ~Cls ｎ In the third step, the decision unit determines whether 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 one of 1 to n) ｋ＿ｉ and the integral value ITG ｋ＿ｊ Difference DF ｋ Calculating n classes Cls １ ~Cls ｎExecute for all of the n differential DFs １ ~DF ｎ Calculate the n calculated differences DF １ ~DF 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 ] is determined to be different.

[0036] (Configuration 23) In Configuration 20, when the P analysis targets have P names that are different from one another, and two analysis targets with different names among the P analysis targets are defined as the first and second analysis targets, and two analysis targets that are included in the first analysis target and are different in type from one another are defined as the third and fourth analysis targets, 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 are different 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 are different 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.

[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.

[0041] 14 is a schematic diagram of an analysis system according to a first embodiment of the present invention. It is a schematic diagram of the sensor device 1 shown in FIG. 1. It is a perspective view of the measuring instrument 12 shown in FIG. 2. It is a schematic diagram of the measuring instrument 12 shown in FIG. 2. It is a schematic diagram showing a timing chart of the potential supplied to the sensor 11 shown in FIG. 2. It is a schematic diagram of measurement data MRS. It is a schematic diagram of the analysis device 2 shown in FIG. 1. It is a diagram for explaining a method of creating a curve CUR showing the relationship between a plurality of classes Cls and a plurality of integral values ​​ITG. It is a first conceptual diagram for explaining a method for calculating an integral value. It is a second conceptual diagram for explaining a method for calculating an integral value. It is a diagram showing an example of calculated data. It is a diagram showing a part of a cyclic voltammogram. It is a diagram showing a cyclic voltammogram of Calpis. It is a diagram showing integral value spectra for undiluted Calpis, Calpis diluted two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water. It is a diagram showing the results of determining whether the curves k1 to k4 shown in FIG. 14 are different from each other. 20 is a diagram showing integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013). It shows the results of determining whether or not curves k5 to k7 shown in FIG. 16 are different from one another. It shows integral spectra for red wine (Italy 2015), red wine (France 2016), and red wine (Italy, age unknown). It shows the results of determining whether or not curves k8 to k10 shown in FIG. 18 are different from one another. It shows integral spectra for WONDA, Craft Boss, and Gold Brew. It shows the results of determining whether or not curves k11 to k13 shown in FIG. 20 are different from one another. It shows integral spectra for human urine collected on different days. It shows the results of determining whether or not curves k14 to k16 shown in FIG. 22 are different from one another. It shows integral spectra for human saliva collected on different days. It shows the results of determining whether or not curves k17 to k19 shown in FIG. 24 are different from one another. It shows two integral spectra for the same wine. 27 is a diagram showing the results of determining whether or not the curves k20 and k21 shown in Fig. 26 are different from each other. Fig. 28 is a diagram showing an integrated value spectrum created based on a cyclic voltammogram CVG measured while changing the potential scanning rate.30. A diagram showing the results of determining whether or not the curves k22, k23, and k24 shown in FIG. 28 are different from one another. This is a first schematic diagram showing a new index curve when identifying an analyte. This is a diagram showing the results of determining whether or not the curves k25, k26, and k27 shown in FIG. 30 are different from one another. This is a second schematic diagram showing a new index curve when identifying an analyte. This is a diagram showing the results of determining whether or not the curves k28, k29, and k30 shown in FIG. 32 are different from one another. This is a diagram showing integral value 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. This 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. 36 shows 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 integrals is four. This figure shows the results of determining whether or not the curves k35 to k38 shown in FIG. 36 are different from one another. This figure shows 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 integrals is five. This figure shows the results of determining whether or not the curves k43 to k46 shown in FIG. 38 are different from one another. This figure shows 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 integrals is six. This figure shows the results of determining whether or not the curves k47 to k50 shown in FIG. 40 are different from one another. 42. This figure shows the integral spectra of 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. This figure shows the results of determining whether or not the curves k51 to k54 shown in FIG. 42 are different from one another, when the number of integral values ​​is 13. This figure shows the integral spectra of 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. This figure shows the results of determining whether or not the curves k55 to k58 shown in FIG. 44 are different from one another.47 shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is three. This figure shows the results of determining whether or not curves k62 to k64 shown in FIG. 46 are mutually different. This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is four. This figure shows the results of determining whether or not curves k65 to k67 shown in FIG. 48 are mutually different. This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is six. This figure shows the results of determining whether or not curves k68 to k70 shown in FIG. 50 are mutually different. 52 . This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is eight. This figure shows the results of determining whether or not curves k71 to k73 shown in FIG. 52 are mutually different. This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is ten. This figure shows the results of determining whether or not curves k74 to k76 shown in FIG. 54 are mutually different. This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is fourteen. This figure shows the results of determining whether or not curves k77 to k79 shown in FIG. 56 are mutually different. 58. 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. A diagram showing the results of determining whether curves k80 to k82 shown in FIG. 58 are different from one another. 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 57.60 , which shows the results of determining whether or not the curves k83 to k85 shown in FIG. 60 are different from one another. This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 95. This figure shows the results of determining whether or not the curves k86 to k88 shown in FIG. 62 are different from one another. This figure shows integral spectra for red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) when the number of integrals is 142. This figure shows the results of determining whether or not the curves k89 to k91 shown in FIG. 64 are different from one another. 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. This figure shows the results of determining whether or not the curves k95 to k97 shown in FIG. 66 are different from one another. 7 shows integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italian vintage unknown) when the number of integrals is four. This figure shows the results of determining whether or not the curves k98 to k100 shown in FIG. 68 are mutually different. This figure shows integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italian vintage unknown) when the number of integrals is six. This figure shows the results of determining whether or not the curves k101 to k103 shown in FIG. 70 are mutually different. This figure shows integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italian vintage unknown) when the number of integrals is seven. This figure shows the results of determining whether or not the curves k104 to k106 shown in FIG. 72 are mutually different. This figure shows integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italian vintage unknown) when the number of integrals is eight. FIG. 75 is a diagram showing the result of determining whether or not the curves k107 to k109 shown in FIG. 74 are different from one another.76. This figure 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 11. This figure shows the results of determining whether or not the curves k110 to k112 shown in FIG. 76 are different from one another. This figure 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 16. This figure shows the results of determining whether or not the curves k113 to k115 shown in FIG. 78 are different from one another. This figure 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 19. This figure shows the results of determining whether or not the curves k116 to k118 shown in FIG. 80 are different from one another. 82. This figure shows integral spectra for a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italian vintage unknown) when the number of integrals is 47. This figure shows the results of determining whether or not the curves k119 to k121 shown in FIG. 82 are different from one another. This figure shows integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integrals is three. This figure shows the results of determining whether or not the curves k125 to k127 shown in FIG. 84 are different from one another. This figure shows integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integrals is four. This figure shows the results of determining whether or not the curves k128 to k130 shown in FIG. 86 are different from one another. This figure shows integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integrals is five. This figure shows the results of determining whether or not the curves k131 to k133 shown in FIG. 88 are different from one another. 91 is a diagram showing integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 6. It 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. It is a diagram showing integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 7.92. A diagram showing the results of determining whether or not the curves k137 to k139 shown in FIG. 92 are different from one another. A diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is eight. A diagram showing the results of determining whether or not the curves k140 to k142 shown in FIG. 94 are different from one another. A diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is nine. A diagram showing the results of determining whether or not the curves k143 to k145 shown in FIG. 96 are different from one another. A diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is ten. A diagram showing the results of determining whether or not the curves k146 to k148 shown in FIG. 98 are different from one another. A diagram showing integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is fifteen. A diagram showing the results of determining whether or not the curves k149 to k151 shown in FIG. 100 are different from one another. 114. A diagram showing integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 20. A diagram showing the results of determining whether or not curves k152 to k154 shown in FIG. 102 are different from one another. A diagram showing integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 39. A diagram showing the results of determining whether or not curves k155 to k157 shown in FIG. 104 are different from one another. A diagram showing integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 54. A diagram showing the results of determining whether or not curves k158 to k160 shown in FIG. 114 are different from one another. A diagram showing integral spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 135. A diagram showing the results of determining whether or not curves k161 to k163 shown in FIG. 108 are different from one another. A diagram showing integral spectra for human urine collected on different days when the number of integral values ​​is 3. 111 is a diagram showing the result of determining whether or not the curves k167 to k169 shown in FIG. 110 are different from one another.114 shows integral spectra of human urine collected on different days when the number of integrals is four. 115 shows the results of determining whether or not the curves k170 to k172 shown in FIG. 112 are different from one another. 116 shows integral spectra of human urine collected on different days when the number of integrals is five. 117 shows the results of determining whether or not the curves k173 to k175 shown in FIG. 114 are different from one another. 117 shows integral spectra of human urine collected on different days when the number of integrals is seven. 118 shows the results of determining whether or not the curves k176 to k178 shown in FIG. 116 are different from one another. 118 shows integral spectra of human urine collected on different days when the number of integrals is nine. 119 shows the results of determining whether or not the curves k179 to k181 shown in FIG. 118 are different from one another. 120 shows integral spectra of human urine collected on different days when the number of integrals is thirteen. 121 shows the results of determining whether or not the curves k182 to k184 shown in FIG. 120 are different from one another. 124 shows integral spectra of human saliva collected on different days when the number of integral values ​​is three. 125 shows a diagram illustrating the results of determining whether or not the curves k188 to k190 shown in FIG. 122 are different from one another. 126 shows integral spectra of human saliva collected on different days when the number of integral values ​​is four. 127 shows a diagram illustrating the results of determining whether or not the curves k191 to k193 shown in FIG. 124 are different from one another. 127 shows integral spectra of human saliva collected on different days when the number of integral values ​​is five. 128 shows a diagram illustrating the results of determining whether or not the curves k194 to k196 shown in FIG. 126 are different from one another. 128 shows integral spectra of human saliva collected on different days when the number of integral values ​​is seven. 129 shows a diagram illustrating the results of determining whether or not the curves k197 to k199 shown in FIG. 128 are different from one another. 129 shows integral spectra of human saliva collected on different days when the number of integral values ​​is nine. 130 shows a diagram illustrating the results of determining whether or not the curves k200 to k202 shown in FIG. 130 are different from one another. FIG. 10 is a diagram showing integral spectra of human saliva collected on different days when the number of integrals is 13.A diagram showing the determination result of whether the curves k203 to k205 shown in FIG. 132 are different from each other. For the case where the number of integral values is 3, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k208 and k209 shown in FIG. 134 are different from each other. For the case where the number of integral values is 4, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k210 and k211 shown in FIG. 136 are different from each other. For the case where the number of integral values is 6, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k212 and k213 shown in FIG. 138 are different from each other. For the case where the number of integral values is 8, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k214 and k215 shown in FIG. 140 are different from each other. For the case where the number of integral values is 10, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k216 and k217 shown in FIG. 142 are different from each other. For the case where the number of integral values is 14, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k218 and k219 shown in FIG. 144 are different from each other. For the case where the number of integral values is 28, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k220 and k221 shown in FIG. 146 are different from each other. For the case where the number of integral values is 41, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k222 and k223 shown in FIG. 148 are different from each other. For the case where the number of integral values is 95, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k224 and k225 shown in FIG. 150 are different from each other. For the case where the number of integral values is 142, it is a diagram showing two integral value spectra for the same wine. A diagram showing the determination result of whether the curves k226 and k227 shown in FIG. 152 are different from each other.154. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is three. 155. A diagram showing the results of determining whether or not the curves k231 to k233 shown in FIG. 154 are different from one another. 156. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is four. 157. A diagram showing the results of determining whether or not the curves k234 to k236 shown in FIG. 156 are different from one another. 158. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is five. 159. A diagram showing the results of determining whether or not the curves k237 to k239 shown in FIG. 158 are different from one another. 160. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is six. 161. A diagram showing the results of determining whether or not the curves k240 to k242 shown in FIG. 160 are different from one another. 164 shows an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is seven. 165 shows a diagram illustrating the results of determining whether or not the curves k243 to k245 shown in FIG. 162 are different from one another. 166 shows an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is eight. 167 shows a diagram illustrating the results of determining whether or not the curves k246 to k248 shown in FIG. 164 are different from one another. 168 shows an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is nine. 169 shows a diagram illustrating the results of determining whether or not the curves k249 to k251 shown in FIG. 166 are different from one another. 169 shows an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is ten. 169 shows a diagram illustrating the results of determining whether or not the curves k252 to k254 shown in FIG. 168 are different from one another.170. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is 15. A diagram showing the results of determining whether or not the curves k255 to k257 shown in FIG. 170 are different from one another. A diagram showing the results of determining whether or not the curves k258 to k260 shown in FIG. 172 are different from one another. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is 39. A diagram showing the results of determining whether or not the curves k261 to k263 shown in FIG. 174 are different from one another. A diagram showing an integral spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integrals is 54. 176. A diagram showing the results of determining whether or not the curves k264 to k266 shown in FIG. 176 are different from one another. A diagram showing an integral value spectrum created based on a cyclic voltammogram CVG measured by changing the potential scanning rate when the number of integral values ​​is 135. A diagram showing the results of determining whether or not the curves k267 to k269 shown in FIG. 178 are different from one another. A flowchart for explaining the operation of the analysis system 10 shown in FIG. 1. A flowchart for explaining the detailed operation of step S9 in FIG. 180. A flowchart for explaining the detailed operation of step S12 in FIG. 180. A flowchart for explaining the detailed operation of step S13 in FIG. 180. A flowchart for explaining the detailed operation of step S14 in FIG. 180. A flowchart for explaining the detailed operation of step S144 in FIG. 184. Another flowchart for explaining the detailed operation of step S144 in FIG. Ｐ C ２ A set of two curves CUR ｉ , C.U.R. ｊ 180 is a conceptual diagram of a determination result showing whether or not the analysis data ALY_D in step S11 of FIG. ｕｎｉ From index data IDXｕｎｉ 180. The P pieces of analysis data ALY_D in step S16 of FIG. １ ~ ALY_D Ｐ P index data IDX １ ~IDX Ｐ 195. It is a conceptual diagram showing an update to. Another schematic diagram of analysis device 2 shown in FIG. 1. It is a schematic diagram of an analysis system according to embodiment 2. It is a schematic diagram of analysis device 2B shown in FIG. 191. It is a schematic diagram of terminal device 3 shown in FIG. 191. It is a first flowchart for explaining the operation of analysis system 10A shown in FIG. 191. It is a second flowchart for explaining the operation of analysis system 10A shown in FIG. 191. It is a flowchart for explaining detailed operation of step S24 of FIG. 195. It is a flowchart for explaining detailed operation of step S34 of FIG. It is another schematic diagram of analysis device 2B shown in FIG. 191. It is another schematic diagram of terminal device 3 shown in FIG.

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

[0043] 1 is a schematic diagram of 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 cyclic voltammogram measurement data of an analysis object consisting of a liquid such as soft drinks, wine, coffee, human urine, and human saliva using a cyclic voltammetry method, and transmits the measured cyclic voltammogram CVG measurement data to the analysis device 2 via wireless or wired communication.

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

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

[0048] When transmitting the measurement data of the cyclic voltammogram CVG to the 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, an integral value of the current-potential characteristic (I-V) included in the measurement data based on the measurement data of the cyclic voltammogram CVG, using a method described below, thereby calculating 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, which 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] The wirings 115 to 117 are arranged along the x-axis direction (first direction) on the upper surface of the substrate 111. The wiring 116 is arranged along the x-axis direction (first direction) at a predetermined interval (e.g., 2 to 3 mm) from the wiring 115 in the y-axis direction (second direction orthogonal to the first direction). The wiring 117 is arranged along the x-axis direction (first direction) at a predetermined interval (e.g., 2 to 3 mm) from the wiring 116 in the y-axis direction (second direction orthogonal to the first direction).

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

[0056] The substrate 111 is made of, for example, a printed 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 a single crystal diamond or a polycrystalline diamond, but polycrystalline diamond is preferred. In this case, it is more preferable that the dangling bonds on the outermost surface of the polycrystalline diamond are terminated with hydrogen.

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

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

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

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

[0062] 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 device 12 has a recess 121A into which a portion of the other end of sensor 11 is inserted.

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

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

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

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

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

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

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

[0071] Here, users of the sensor device 1 are, for example, staff at 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 current-potential characteristics (IV) that mutually correspond 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, during the period from time t3 to time t4, the supply unit 121 supplies the potential V in the range of 0 V to −2.5 V to the working electrode 112 while changing the potential V at a predetermined scanning speed.

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

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

[0079] 6 is a schematic diagram of the measurement data MRS. Referring to FIG. 6, the measurement data MRS includes the name of the analysis object, the type of the analysis object, and current-potential characteristics (IV). The current-potential characteristics (IV) are configured such that a potential V corresponds to a 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 mutually associated. １ ~V ｄ The current value I is I １ ~I ｄ The current value I １ ~I ｄ are the potential V １ ~V ｄ can be associated with

[0083] When the scanning range of the potential V is −2.5 V to +2.5 V, the potential V １ , V ２ , V ３ , V ４ , ..., V ｄ－２ , V ｄ－１ , V ｄ are respectively 0 V, 1 mV, 2 mV, 3 mV, ..., 2499 mV, 2500 mV, 2499 mV, ..., 2 mV, 1 mV, 0 mV, -1 mV, -2 mV, ..., -2499 mV, -2500 mV, -2499 mV, ..., -2 mV, -1 mV, and 0 V. That is, the potential V １ , V ２ , V ３ , V ４ , ..., V ｄ－２ , V ｄ－１ , V ｄ is made up 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. The measurement unit 122 then 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 transmission unit 123 receives the measurement data MRS from the measurement unit 122 and transmits the received measurement data MRS to the analysis device 2 via wired 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 of 2 or more) analysis targets by cyclic voltammetry, the measurement unit 122 of the sensor device 1 creates each of the P pieces of measurement data MRS_1 to MRS_P by the above-mentioned method, and the transmission unit 123 of the sensor device 1 transmits the P pieces of measurement data MRS_1 to MRS_P created by the measurement unit 122 to the 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.

[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 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 and identification information ID for identifying the measurement data MRS_uni ｕｎｉ Issue the following.

[0094] Then, the control unit 22 determines the name of the object to be analyzed, ALY_Na ｕｎｉ and the type of the object to be analyzed ALY_Kd ｕｎｉ and the current-potential characteristic (IV) showing the correspondence relationship between the current value I and the potential V. ｕｎｉ 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 object to be analyzed ALY_Na ｕｎｉ , type of analyte ALY_Kd ｕｎｉ and current-potential characteristics (IV) ｕｎｉ Analysis data ALY_D in which the above are associated with each other ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / (I-V) 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 ｕｎｉ is 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 references the timer and calculates the time t １ ~t Ｐ and P pieces of identification information ID for identifying each of the P pieces of measurement data MRS_1 to MRS_P. １ ~ID Ｐ Issue the following.

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

[0099] Then, the control unit 22 detects the time t ｐ , identification information ID ｐ , name of the object to be analyzed ALY_Na ｐ , type of analyte ALY_Kd ｐ and current-potential characteristics (IV) ｐ (p is any one of 1 to P) ｐ = [t ｐ / ID ｐ / ALY_Na ｐ / ALY_Kd ｐ / (I-V) ｐ ] is performed for all p=1 to P, and P pieces of analysis data ALY_D １ = [t １ / ID １ / ALY_Na １ / ALY_Kd １ / (I-V) １ ]~ALY_D Ｐ = [t Ｐ / ID Ｐ / ALY_Na Ｐ / ALY_Kd Ｐ / (I-V) Ｐ ] to create a

[0100] Then, the control unit 22 generates P pieces of analysis data ALY_D １ ~ ALY_D Ｐ are stored in the database 28, and P pieces of analysis data ALY_D １ ~ 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 result ALY_RLS associated with i ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] from the creation unit 25, the analysis result ALY_RLS ｕｎｉ From identification information ID ｕｎｉ , calculation data CAL ｕｎｉ and curve CUR ｕｎｉ and the detected identification information ID ｕｎｉ Analysis data ALY_D having the same identification information as 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 curve CUR ｕｎｉ is stored as analysis data ALY_D ｕｎｉ The index data IDX ｕｎｉ 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_D １ ~ ALY_D Ｐ to the arithmetic unit 23, and then P pieces of identification information ID １ ~ID Ｐ and P calculation data CAL １ ~CALＰ and P curves CUR １ ~CUR Ｐ P analysis results ALY_RLS １ = [ID １ / CAL １ / CUR １ ]~ALY_RLS Ｐ = [ID Ｐ / CAL Ｐ / CUR Ｐ ] 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 curve CUR ｐ and the detected identification information ID ｐ Analysis data ALY_D having the same identification information as 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 curve CUR ｐ is stored as analysis data ALY_D ｐ The index data IDX ｐ 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 for updating P pieces of analysis data ALY_D １ ~ ALY_D Ｐ , and P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Each of the P index data IDX １ ~ Index data IDX Ｐ and the updated P index data IDX１ ~ Index data IDX Ｐ Each of the P pieces of analysis data ALY_D １ ~ 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 A request RQT to display ｕｎｉ When the name of the object to be analyzed, ALY_Na, is received from the reception unit 27, ｕｎｉ Based on the above, the name of the analyte 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 output to the display unit 26.

[0107] The control unit 22 also calculates the P names ALY_Na of the P analysis objects. １ ~ALY_Na Ｐ q (q is an integer satisfying 1≦q≦P) names ALY_Na １ ~ALY_Na ｑ and q names ALY_Na １ ~ALY_Na ｑ q curves CUR respectively associated with １ ~CUR ｑ A request RQT to display ｑ When the receiving unit 27 receives the q names ALY_Na １ ~ALY_Na ｑ Based on the above, q names ALY_Na of q analytes are １ ~ALY_Na ｑ and q names ALY_Na of q analytes. １ ~ALY_Na ｑ q curves CUR respectively associated with １ ~CUR ｑ and q names ALY_Na of the q analysis objects are detected from the database 28. １ ~ALY_Na ｑand q curves CUR １ ~CUR ｑ and output to the display unit 26.

[0108] In this case, the request RQT ｑ In the above, q names ALY_Na of q analytes are １ ~ALY_Na ｑ Instead of q types ALY_Kd of q analytes １ ~ALY_Kd ｑ may also be used.

[0109] The calculation unit 23 calculates the analysis data ALY_D ｕｎｉ = [t ｕｎｉ / ID ｕｎｉ / ALY_Na ｕｎｉ / ALY_Kd ｕｎｉ / (I-V) 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 / (I-V) ｕｎｉ ] Current-potential characteristics (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 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 defined as “class (N ＳＥＣ -1)" and N ＳＥＣ The th predetermined potential section V_ITV is defined as "Class N ＳＥＣ "

[0112] Then, the calculation unit 23 calculates N ＳＥＣ Individual class and N ＳＥＣCalculation data CAL corresponding to the integral values ｕｎｉ The created calculation data CAL ｕｎｉ and identification information ID ｕｎｉ The calculation result CAL_RLS in which ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ Then, the calculation unit 23 creates the calculation result CAL_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ ] and a signal S_u indicating that there is one operation data item to the creation unit 25.

[0113] The calculation unit 23 also calculates P pieces of analysis data ALY_D １ ~ ALY_D Ｐ 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 / (I-V) ｕｎｉ ] from the control unit 22, the analysis data ALY_D ｐ = [t ｐ / ID ｐ / ALY_Na ｐ / ALY_Kd ｐ / (I-V) ｐ ] Current-potential characteristics (IV) ｐ (p is any one of 1 to P) ＳＥＣ Individual class and N ＳＥＣ Calculation data CAL corresponding to the integral values ｐ The creation of P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Calculation data CAL is performed for all P data. １ ~CAL Ｐ Create a.

[0115] Then, the calculation unit 23 calculates P pieces of identification information ID １ ~ID Ｐ and P calculation data CAL １ ~CAL ＰP calculation results CAL_RLS １ = [ID １ / CAL １ ]~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 the calculation result CAL_RLS is created, ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ ] to the creation unit 25 only, and P calculation results CAL_RLS １ = [ID １ / CAL １ ]~CAL_RLS Ｐ = [ID Ｐ / CAL Ｐ ] (i.e., a plurality of calculation results) are created, P calculation results CAL_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) 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 The 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 P pieces of calculation data CAL are associated with the P integral values. １ ~CAL Ｐ The same is true for each of the above.

[0118] The decision unit 24 calculates P calculation results CAL_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) from the calculation unit 23. Then, the decision unit 24 calculates the P calculation results CAL_RLS １ ~CAL_RLS ＰP pieces of calculation data CAL included in １ ~CAL Ｐ Detect.

[0119] Then, the decision unit 24 calculates the P pieces of calculation data CAL １ ~CAL Ｐ Based on Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ (i≠j), where Ｐ C ２ is P pieces of calculation data CAL １ ~CAL Ｐ Two different calculation data CAL ｉ , CAL ｊ Two different calculation data CAL when extracting (i≠j) ｉ , CAL ｊ This is the number of combinations where (i≠j).

[0120] Then, the decision unit 24 calculates the two calculation data CAL ｉ , CAL ｊ Based on (i≠j), the calculation data CAL ｉ The multiple integral values ​​and calculation data CAL included in ｊ Calculate the difference between the multiple integral values ​​included in and calculate the standard deviation of the calculated difference. Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ This is carried out for all (i≠j).

[0121] The determination unit 24 calculates the calculation data CAL by the following method. ｉ The 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 A plurality of integral values ​​ITG included in １＿ｉ ~ITG ｎ＿ｉ and calculation data CAL ｊ A plurality of integral values ​​ITG included in １＿ｊ ~ITG ｎ＿ｊBased on this, one class Cls is determined by the following formula: ｋ The integral value ITG ｋ＿ｉ and the integral value ITG ｋ＿ｊ Difference DF ｋ Calculate the following.

[0123]

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

[0125] Then, the decision unit 24 determines one class Cls by the formula (1). ｋ The difference DF of the integral value in ｋ The calculation of n classes Cls １ ~Cls ｎ Execute for all of the n differential DFs １ ~DF ｎ Calculate.

[0126] Then, the decision unit 24 calculates n differences DF １ ~DF ｎ Standard deviation σ ＤＦ Calculate.

[0127] Then, the determination 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, the 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 calculates the two calculation data CAL by the above-mentioned method. ｉ , CAL ｊ (i≠j) is determined to be different or not. Ｐ C ２ Set of two calculation data CAL ｉ , CALｊ Execute for all (i≠j) Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ Two calculation data CAL of each pair (i≠j) ｉ , CAL ｊ It is determined whether (i≠j) is different.

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

[0130] The determination unit 24 determines, by the above-described method, Ｐ C ２ Set of two calculation data CAL ｉ , CAL ｊ Two calculation data CAL of 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]

[0132] P calculation data CAL １ ~CAL Ｐ are not different from each other, １ ~CAL Ｐ This corresponds to determining that the P calculation data CAL １ ~CAL Ｐ are different from each other, the P pieces of calculation data CAL １ ~CAL ＰThis 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] FIG. 8(a) shows one calculation data CAL ｕｎｉ 8(b) shows the curve CUR representing 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 object to be analyzed ALY-Na ｕｎｉ and the type of analyte ALY-Kd ｕｎｉ The class Cls includes n classes Cls, and an integral value ITG. １ ~Cls ｎ The integral value ITG is made up of n integral values ​​ITG １ ~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 V ＰＴＳ When n = (| - V Ｓ１ |+|+V Ｓ２ |) / V ＰＴＳ is.

[0137] n integral values ​​ITG １ ~ITG ｎ are n classes Cls １ ~Cls ｎ can be associated with

[0138] The creation unit 25 creates one calculation data CAL ｕｎｉand a signal S_u indicating that there is one piece of calculation data from the calculation unit 23, the calculation unit 23 determines that there is one piece of calculation data CAL calculated by the calculation unit 23 based on the signal S_u.

[0139] The creating unit 25 then creates a set of classes (Cls １ , integral value ITG １ ) to the calculation data CAL ｕｎｉ Then, a set of (class Cls ２ , integral value ITG ２ ) to the calculation data CAL ｕｎｉ Then, similarly, a set of (class Cls ｎ－１ , integral value ITG ｎ－１ ) to the calculation data CAL ｕｎｉ Detect from a set of (class Cls ｎ , integral value ITG ｎ ) to the calculation data CAL ｕｎｉ Detect from.

[0140] Then, the creation unit 25 creates a set of classes (Cls) on a graph whose horizontal axis represents the classes and whose vertical axis represents the integral values. １ , integral value ITG １ ), one set of (class Cls ２ , integral value ITG ２ ), one set of (class Cls ３ , integral value ITG ３ ), ..., a set of (Cls ｎ－２ , integral value ITG ｎ―２ ), one set of (class Cls ｎ－１ , integral value ITG ｎ―１ ) and a set of (classes Cls ｎ , integral value ITG ｎ ) is plotted.

[0141] Thereafter, 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, which is a smooth curve, by connecting the n plotted points. ｕｎｉ can be created.

[0142] After plotting the 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 create a

[0143] Then, the creating 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_RLS １ ~CAL_RLS Ｐ (i.e., a plurality of calculation results) from the calculation unit 23 and P calculation data CAL １ ~CAL Ｐ The determination unit 24 receives a determination result (determination result shown in Table 1) indicating whether or not the two inputs are different.

[0145] Then, the creating unit 25 creates one calculation data CAL by the method described with reference to FIG. ｐ (p is any one of 1 to P) ｐ P pieces of calculation data CAL are created. １ ~CAL Ｐ , and P curves CUR are obtained. １ ~CUR Ｐ (i.e., multiple curves CUR) are created.

[0146] In this case, P pieces of calculation data CAL １ ~CAL Ｐ (i.e., a plurality of pieces of calculation data) are the calculation data CAL shown in FIG. ｕｎｉ It has the same configuration as

[0147] In addition, the curve CUR ｕｎｉ is [n integral values ​​ITG １ ~ITG ｎ ] of [n classes Cls １ ~Cls ｎ ] dependency. １ ~Cls ｎ Each of these curves consists of a predetermined potential section. ｕｎｉis a curve that represents the dependency of the integral value on a predetermined potential section. Similarly, P curves CUR １ ~CUR Ｐ Each of the curves represents the dependency of the integral value on a predetermined potential interval.

[0148] The creation unit 25 creates one curve CUR ｕｎｉ When the calculation result CAL_RLS is created, ｕｎｉ Curve CUR ｕｎｉ Add the analysis result ALY_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] is created, and the created analysis result ALY_RLS ｕｎｉ = [ID ｕｎｉ / CAL ｕｎｉ / CUR ｕｎｉ ] to the control unit 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 CUR １ ~CUR Ｐ When P calculation results CAL_RLS are created, １ ~CAL_RLS Ｐ Each of them has P curves CUR. １ ~CUR Ｐ Add P analysis results ALY_RLS １ = [ID １ / CAL １ / CUR １ ]~ALY_RLS Ｐ = [ID Ｐ / CAL Ｐ / CUR Ｐ ] and P analysis results ALY_RLS １ ~ ALY_RLS Ｐ and outputting the judgment results (the judgment results shown in FIG. 187 to be described later) to the control unit 22, and １ ~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 ｕｎｉWhen the curve CUR is received from the creation unit 25, ｕｎｉ Display.

[0151] On the other hand, the display unit 26 displays P curves CUR １ ~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 CUR are １ ~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, a Japanese restaurant, a 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 CUR displayed on the display unit 26. １ ~CUR Ｐ and the determination result (determination result shown in FIG. 187 to be described later) is referred to, and P curves CUR are １ ~CUR Ｐ are different from each other, or P curves CUR １ ~CUR Ｐ Which curves are different from each other, and P curves CUR １ ~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 ALY_Na is received from the control unit 22, ｕｎｉ and curve CUR ｕｎｉ Display.

[0154] Furthermore, the display unit 26 displays q (q is an integer satisfying 1≦q≦P) names ALY_Na of the analysis object. １ ~ALY_Na ｑ and q curves CUR １ ~CUR ｑ When the received q names ALY_Na are received from the control unit 22, １ ~ALY_Na ｑ and q curves CUR １ ~CUR ｑDisplay.

[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 A request RQT to display 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_Na of 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)). １ ~ALY_Na Ｐ q names ALY_Na １ ~ALY_Na ｑ and q names ALY_Na １ ~ALY_Na ｑ q curves CUR associated with １ ~CUR ｑ A request RQT to display ｑ Then, the reception unit 27 receives the request RQT ｑ to the control unit 22.

[0157] The database 28 contains the analysis data ALY_D ｕｎｉ or P pieces of analysis data ALY_D １ ~ ALY_D Ｐ or the analytical data ALY_D ｕｎｉ Instead of index data IDX ｕｎｉ or P pieces of analysis data ALY_D １ ~ ALY_D Ｐ Instead of P index data IDX １ ~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 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].

[0164] FIG. 10 shows the cyclic voltammogram CVG in a predetermined potential section [V １ ~V ２ ] is an enlarged view of the portion indicated by the arrow.

[0165] 10, the calculation unit 23 calculates the voltage in the predetermined potential section [V １ ~V ２ ], when calculating the integral value ITG of the potential V １ The current value Iox_1 of the oxidation wave and the current value Ird_1 of the reduction wave are detected, and the difference between the current value Iox_1 and the current value Ird_1 (Iox_1-Ird_1) is calculated to obtain the potential V １The intensity (Iox_1-Ird_1) of the cyclic voltammogram CVG is calculated.

[0166] Thereafter, the arithmetic unit 23 calculates the potential V １ The potential V is obtained by adding a unit potential (= 1 mV) to １ The oxidation wave current value Iox_2 and the reduction wave current value Ird_2 at +1 are detected, and the difference between the current value Iox_2 and the current value Ird_2 (Iox_2-Ird_2) is calculated to obtain the potential V １ The intensity (Iox_2-Ird_2) of the cyclic voltammogram CVG at +1 is calculated.

[0167] Furthermore, the arithmetic unit 23 calculates the potential V １ +1 plus unit potential (=1 mV) V １ The oxidation wave current value Iox_3 and the reduction wave current value Ird_3 at +2 are detected, and the difference between the current value Iox_3 and the current value Ird_3 (Iox_3-Ird_3) is calculated to obtain the potential V １ The intensity (Iox_3-Ird_3) of the cyclic voltammogram CVG at +2 is calculated.

[0168] Similarly, the calculation unit 23 calculates the potential V ２ The potential V is calculated by adding a unit potential (= 1 mV) to -1. ２ The current value Iox_N of the oxidation wave and the current value Ird_N of the reduction wave are detected, and the difference between the current value Iox_N and the current value Ird_N (Iox_N-Ird_N) is calculated to obtain the potential V ２ The intensity (Iox_N-Ird_N) of the cyclic voltammogram CVG is calculated.

[0169] Then, the calculation unit 23 calculates the voltage across the predetermined potential section [V １ ~V ２ ] is calculated.

[0170]

[0171] That is, the calculation unit 23 calculates the voltage in the predetermined potential section [V １ ~V ２], multiple intensities ((Iox_1-Ird_1), (Iox_2-Ird_2), ..., (Iox_N-Ird_N)) of the cyclic voltammogram CVG for each unit potential (=1 mV) are calculated, and the multiple calculated intensities ((Iox_1-Ird_1), (Iox_2-Ird_2), ..., (Iox_N-Ird_N)) are added to obtain a predetermined potential interval [V １ ~V ２ ], the integral value ITG of the cyclic voltammogram CVG is calculated.

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

[0173] Then, by the formula (2), the predetermined potential section [V １ ~V ２ Calculating the integral ITG in [ ] corresponds to calculating the sum of the calculated intensities as the area of ​​a cyclic voltammogram in one predetermined potential section.

[0174] In addition, the potential V １ Calculating the difference (Iox_1-Ird_1) at the potential V １ +1, calculating the difference (Iox_2-Ird_2), the potential V １ +2, and calculating the difference (Iox_3-Ird_3) at the potential V ２Each calculation of the difference (Iox_N-Ird_N) corresponds to subtracting the current value of the reduction wave from the current value of the oxidation wave of the cyclic voltammogram at one unit potential in one predetermined potential interval to calculate the intensity of the cyclic voltammogram at one unit potential.

[0175] The calculation unit 23 calculates the voltage in the predetermined potential range [V １ ~V ２ ] by a method of calculating an integral value ITG of a cyclic voltammogram CVG in a plurality of predetermined potential intervals [0 to 100 mV], [101 to 200 mV], [201 to 300 mV], ..., [2301 to 2400 mV], [2401 to 2500 mV], [0 to -100 mV], [-101 to -200 mV], ..., [-2301 to -2400 mV], [-2401 to -2500 mV] １ , ITG ２ , ITG ３ , ..., ITG ２４ , ITG ２５ , ITG ２６ , ITG ２７ , ..., ITG ４９ , ITG ５０ Calculate.

[0176] The above-described methods shown in FIGS. 9 and 10 are used to calculate the multiple integral values ​​ITG １ ~ITG ５０ By calculating the above, it is possible to correct the characteristic variations between sensors used to measure the cyclic voltammogram CVG, and to clearly show the difference in signal intensity between solutions of the object to be analyzed.

[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 specified potential section represents the total number of electrons in the oxidation reaction and reduction reaction between the electrode (working electrode) and the object to be analyzed in one specified potential section.

[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 this embodiment of the present invention, the calculation unit 23 calculates the voltage in the predetermined potential section [V １ ~V ２ ] may be calculated.

[0181]

[0182] In the formula (3), the formula (3A) is １ ~V ２ ], and Equation (3B) represents the sum of the oxidation currents in a predetermined potential section [V １ ~V ２ In the formulas (3A) and (3B), N represents the sum of the reduction currents in a predetermined potential section [V １ ~V ２ ], where n=1 to N.

[0183] As a result, equation (3C) is １ ~V ２ ] represents the integral ITG.

[0184] The calculation unit 23 calculates the voltage in the predetermined potential section [V １ ~V ２ ] the sum of the oxidation currents I ＩＴＧ ｏｘ is calculated, and the predetermined potential section [V １ ~V ２ ] the sum of the reduction currents I 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 obtain the predetermined potential section [V １ ~V ２ ] the integral ITG(V １ -V ２ ) is calculated.

[0186] The integral value ITG (V １ -V ２ ) is the integral value ITG(V １ -V ２ ) matches.

[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 ​​ITG １ , ITG ２ , ITG ３ , ..., ITG ２４ , ITG ２５ , ITG ２６ , ITG ２７ , ..., ITG ４９ , ITG ５０ When the calculation is performed, a plurality of predetermined potential sections [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] are divided into a plurality of classes Cls １ ~Cls ５０ Let us assume that there are multiple classes Cls １ ~Cls ５０ Each of the plurality of integral values ​​ITG １ , ITG ２ , ITG ３ , ..., ITG ２４ , ITG ２５ , ITG ２６ , ITG ２７ , ..., ITG ４９ , ITG ５０ are associated with the calculation data CAL １ Create a.

[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 a case where region REG is located in a positive current region, Fig. 12(b) shows a case where, in region REG, the reduction wave is located in a positive current region and the oxidation wave is located in a negative current region, and Fig. 12(c) shows a case where region REG is located in a 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 FIG. 12A, when calculating the integral value of a predetermined potential section in the region REG, the current value I ｏｘ＿１＿ＲＥＧ , I ｏｘ＿２＿ＲＥＧ , ..., I ｏｘ＿Ｎ’＿ＲＥＧ are the current values ​​I of the reduction wave, respectively. ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿ＲＥＧ Since it is smaller than the predetermined potential section in the region REG, the integral value ITG of the predetermined potential section becomes a negative value.

[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 are negative current values, and the reduction wave current value I ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿ＲＥＧ are positive current values, the integral ITG of the predetermined potential section in the region REG becomes a negative value.

[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’＿ＲＥＧare negative current values, and the reduction wave current value I ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿Ｒ Each of these currents is a negative current value, and the oxidation current value I ｏｘ＿１＿ＲＥＧ , I ｏｘ＿２＿ＲＥＧ , ..., I ｏｘ＿Ｎ’＿ＲＥＧ Absolute value of |I ｏｘ＿１＿ＲＥＧ |, |I ｏｘ＿２＿ＲＥＧ |, ..., |I ｏｘ＿Ｎ’＿ＲＥＧ | are the current values ​​of the reduction waves, I ｒｄ＿１＿ＲＥＧ , I ｒｄ＿２＿ＲＥＧ , ..., I ｒｄ＿Ｎ’＿Ｒ Absolute value of |I ｒｄ＿１＿ＲＥＧ |, |I ｒｄ＿２＿ＲＥＧ |, ..., |I ｒｄ＿Ｎ’＿Ｒ |, the integral ITG of the predetermined potential section in the region REG becomes a negative value.

[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 by a method different from the above-described method.

[0195] For example, the arithmetic unit 23 calculates a regression curve RC1 (the curve indicated by the solid line) showing the oxidation wave of the cyclic voltammogram CVG in FIG. 9 and a regression curve RC2 (the curve indicated by the dashed line) showing the reduction wave, using the potential V as an explanatory variable and the current I as a response variable, and calculates a regression curve RC2 (the curve indicated by the dashed line) showing the reduction wave of the cyclic voltammogram CVG in a predetermined potential interval [V １ -V ２ ], an integral value ITG_RC1 of the regression curve RC1 and an integral value ITG_RC2 of the regression curve RC2 are calculated, and the integral value ITG_RC2 is subtracted from the integral value ITG_RC1 to obtain a predetermined potential section [V １ -V ２ ] may be calculated for all predetermined potential intervals to calculate a plurality of integral values ​​in a plurality of predetermined potential intervals.

[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] EXAMPLES The curve CUR created 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 2-fold with tap water, and Figure 13(c) shows the cyclic voltammogram CVG_Cal_3 of Calpis diluted 5-fold with tap water.

[0199] Referring to FIG. 13, the standard deviation σ of the plurality of current values ​​in the cyclic voltammogram CVG_Cal_1 ＣＶＧ＿Ｃａｌ＿１ is 163.81, and the standard deviation σ of the multiple current values ​​in the cyclic voltammogram CVG_Cal_2 is ＣＶＧ＿Ｃａｌ＿２ is 160.75, and the standard deviation σ of the multiple current values ​​in the cyclic voltammogram CVG_Cal_3 is ＣＶＧ＿Ｃａｌ＿３ is 152.89.

[0200] And the standard deviation σ ＣＶＧ＿Ｃａｌ＿１ ~σ ＣＶＧ＿Ｃａｌ＿３ Based on this, the index IDX is calculated by the following formula: １ ~IDX ３ Calculate.

[0201]

[0202] In each of the formulas (4A) to (4C), σCVG_AVERAGE is the three standard deviations σ of the three cyclic voltammograms CVG_Cal_1 to CVG_Cal_3 to be compared. ＣＶＧ＿Ｃａｌ＿１ ~σ ＣＶＧ＿Ｃａｌ＿３ , and σCVG_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 = 152.89, and the index IDX １ ~IDX ３ When calculated, IDX １ =2.93(%), IDX ２ = 1.01 (%) and IDX ３ = -3.93 (%).

[0204] As a result, the index IDX １ ~IDX ３ Each of these is a threshold σ ｔｈ (=15%).

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

[0206] As a result, it is difficult to distinguish between "undiluted Calpis," "Calpis diluted two-fold with tap water," and "Calpis diluted five-fold 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-described 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 of the cyclic voltammograms that were used to calculate the multiple integral values ​​for each of the curves k1 to k4.

[0210]

[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 the two curves are different for six cases: (k1, k2), (k1, k3), (k1, k4), (k2, k3), (k2, k4), and (k3, k4).

[0216] 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] Furthermore, 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 within 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-fold with tap water], the curve k3 is a curve for uniquely identifying [Calpis diluted 3-fold with tap water], and the curve k4 is a curve for uniquely identifying [Calpis diluted 4-fold 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-fold with tap water], the curve k3 represents the feature quantity based on the integral value for [Calpis diluted 3-fold with tap water], and the curve k4 represents the feature quantity based on the integral value for [Calpis diluted 4-fold with tap water].

[0226] As a result, the authenticity of a product can be determined by the index curves k1 to k4. (2) Wine Figure 16 shows the integral spectra of red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013).

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

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

[0229]

[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 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 any 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] 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 σＤＦ＿ｋ５，ｋ７ (=74.94(%)), and the standard deviation of the difference σ ＤＦ＿ｋ６，ｋ７ (=93.19(%)) are all within the threshold value 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] Furthermore, 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, curves k5 to k7 allow red wine (Chile 2020), red wine (Australia 2010), and 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 the red wine (Chile 2020), the curve k6 is a curve for uniquely identifying the red wine (Australia 2010), and the curve k7 is a curve for uniquely identifying the red wine (France 2013). The curve k5 represents the feature quantity based on the integral value for the red wine (Chile 2020), the curve k6 represents the feature quantity based on the integral value for the red wine (Australia 2010), and the curve k7 represents the feature quantity based on the integral value for the 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 shows the integral spectra of red wine (Italy 2015), red wine (France 2016), and red wine (Italy, unknown year).

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

[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 described above, 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 having a working electrode made of gold 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 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 are different for three cases: (k8, k9), (k8, k10), and (k9, k10).

[0255] 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(%)) is the threshold value 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] Curves k8 to k10 have three peaks in common, but the positions of the peaks of curves k8 to k10 in the range of classes 0 to 20 are different from one another.

[0259] Therefore, it can 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 can distinguish red wine (Italy 2015), red wine (France 2016), and red wine (Italy, unknown year) from each other.

[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, the curves k8 to k10, which are index curves, can be used to determine whether the item is genuine or fake.

[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 (dependence on a predetermined potential interval), included negative integral values.

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

[0266] In this way, it is possible to determine whether or not the wine being 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 the wine being 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, so the quality of the wine as the object to be 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 Figure 20 shows the integrated value spectra for WONDA, Craft Boss, and Gold Brew.

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

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

[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 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 three cases: (k11, k12), (k11, k13), and (k12, k13).

[0278] Referring to Figure 21, the standard deviation σDF_k11,k12 of the difference between the multiple integral values ​​on curve k11 and the multiple integral values ​​on curve k12 is σDF_k11,k12 = 37.00 (%), the standard deviation σDF_k11,k13 of the difference between the multiple integral values ​​on curve k11 and the multiple integral values ​​on curve k13 is σDF_k11,k13 = 66.20 (%), and the standard deviation σDF_k12,k13 of the difference between the multiple integral values ​​on curve k12 and the multiple integral values ​​on curve k13 is σDF_k12,k13 = 50.55 (%).

[0279] As a result, all of the standard deviation of the differences σDF_k11, k12 (=37.00(%)), the standard deviation of the differences σDF_k11, k13 (=66.20(%)), and the standard deviation of the differences σDF_k12, k13 (=50.55(%)) are within the threshold value σｔｈ (=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 on curve k13 are different from the positions of the two peaks on 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, authenticity determination can be performed by determining whether the product is genuine or fake using the index curves k11 to k13. (4) Human Urine Figure 22 is a diagram showing integral spectra of human urine collected on different days. Referring to Figure 22, each of curves k14 to k16 is a curve CUR created by the analysis device 2 using the method described above. 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 curves k14 to k16.

[0289]

[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 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 or not the two curves differ for three cases: (k14, k15), (k14, k16), and (k15, k16).

[0295] Referring to Figure 23, the standard deviation σDF_k14,k15 of the difference between the multiple integral values ​​on curve k14 and the multiple integral values ​​on curve k15 is σDF_k14,k15 = 38.45 (%), the standard deviation σDF_k14,k16 of the difference between the multiple integral values ​​on curve k14 and the multiple integral values ​​on curve k16 is σDF_k14,k16 = 26.04 (%), and the standard deviation σDF_k15,k16 of the difference between the multiple integral values ​​on curve k15 and the multiple integral values ​​on curve k16 is σDF_k15,k16 = 22.93 (%).

[0296] As a result, all of the standard deviation of the differences σDF_k14, k15 (=38.45(%)), the standard deviation of the differences σDF_k14, k16 (=26.04(%)), and the standard deviation of the differences σDF_k15, k16 (=22.93(%)) are within the threshold value 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, but the 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 peak values ​​that are different from each other 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 judgment results 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 curves k14 to k16 are different from one another, for urine from the same person, curve k14 is a curve for uniquely identifying urine collected on day 1, curve k15 is a curve for uniquely identifying urine collected on day 2, and curve k16 is a curve for uniquely identifying urine collected on day 3. Curve k14 represents the feature quantity based on the integral value for [urine collected on day 1], curve k15 represents the feature quantity based on the integral value for [urine collected on day 2], and curve k16 represents the feature quantity based on the integral value for [urine collected on day 3].

[0304] As a result, authenticity determination can be performed by determining whether the product is genuine or fake using the index curves k14 to k16. (5) Human Saliva Figure 24 is a diagram showing integral spectra of human saliva collected on different days. Referring to Figure 24, each of curves k17 to k19 is a curve CUR created by the analysis device 2 using the method described above. 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 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 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 any 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 or not the two curves differ for three cases: (k17, k18), (k17, k19), and (k18, k19).

[0310] Referring to Figure 25, the standard deviation σDF_k17,k18 of the differences between the multiple integral values ​​on curve k17 and the multiple integral values ​​on curve k18 is σDF_k17,k18 = 162.23 (%), the standard deviation σDF_k17,k19 of the differences between the multiple integral values ​​on curve k17 and the multiple integral values ​​on curve k19 is σDF_k17,k19 = 30.81 (%), and the standard deviation σDF_k18,k19 of the differences between the multiple integral values ​​on curve k18 and the multiple integral values ​​on curve k19 is σDF_k18,k19 = 75.69 (%).

[0311] As a result, all of the standard deviation of the differences σDF_k17, k18 (=162.23(%)), the standard deviation of the differences σDF_k17, k19 (=30.81(%)), and the standard deviation of the differences σDF_k18, k19 (=75.69(%)) are within 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 results 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 can be used to distinguish between saliva collected from the same person on the first day, saliva collected from the same person on the second day, and saliva collected from the same person on the third day.

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

[0318] As a result, it is possible to determine whether the product is genuine or fake by using the curves k17 to k19, which are index curves.

[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 one another 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 integral spectra do not differ will now be described. Fig. 26 shows two integral 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 σDF_k20,k21 of the differences between the plurality of integral values ​​on the curve k20 and the plurality of integral values ​​on the curve k21 is σDF_k20,k21=10.95(%).

[0327] As a result, the standard deviation σDF_k20, k21 (=10.95(%)) of the difference is 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 creation of an integrated value spectrum by the analysis device 2 based on the cyclic voltammogram CVG is reproducible.

[0330] [Other Indices] 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 indicates an integrated spectrum generated by the analysis device 2 based on a cyclic voltammogram CVG_600 mV / s measured at a potential scanning rate of 600 mV / s, curve k23 indicates an integrated spectrum generated by the analysis device 2 based on a cyclic voltammogram CVG_500 mV / s measured at a potential scanning rate of 500 mV / s, and curve k24 indicates an integrated spectrum generated by the analysis device 2 based on a cyclic voltammogram CVG_300 mV / s measured at a potential scanning rate of 300 mV / s. The same analysis object was used to measure the cyclic voltammograms CVG_600 mV / s, CVG_500 mV / s, and CVG_300 mV / s.

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

[0333]

[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 Figure 29, whether curves k22 to k24 are different from each other is determined by determining whether two curves among curves k22 to k24 are different for all combinations of two curves among 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 are different for three combinations of two curves ((k22, k23), (k22, k24), and (k23, k24).

[0339] Referring to Figure 29, the standard deviation σDF_k22,k23 of the difference between the multiple integral values ​​on curve k22 and the multiple integral values ​​on curve k23 is σDF_k22,k23 = 34.69 (%), the standard deviation σDF_k22,k24 of the difference between the multiple integral values ​​on curve k22 and the multiple integral values ​​on curve k24 is σDF_k22,k24 = 51.23 (%), and the standard deviation σDF_k23,k24 of the difference between the multiple integral values ​​on curve k23 and the multiple integral values ​​on curve k24 is σDF_k23,k24 = 30.26 (%).

[0340] As a result, all of the standard deviation of the differences σDF_k22,k23 (=34.69(%)), the standard deviation of the differences σDF_k22,k24 (=51.23(%)), and the standard deviation of the differences σDF_k23,k24 (=30.26(%)) are within the threshold value 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, but the 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 peak values ​​that are different from each other in the range of 50 to 100 and the range of 230 to 270.

[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] 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 for each class is the sum of the three integral values ​​for 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 any 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 or not the two curves differ for three cases: (k25, k26), (k25, k27), and (k26, k27).

[0352] Referring to Figure 31, the standard deviation σDF_k25,k26 of the difference between the multiple integral values ​​on curve k25 and the multiple integral values ​​on curve k26 is σDF_k25,k26 = 15.22 (%), the standard deviation σDF_k25,k27 of the difference between the multiple integral values ​​on curve k25 and the multiple integral values ​​on curve k27 is σDF_k25,k27 = 27.76 (%), and the standard deviation σDF_k26,k27 of the difference between the multiple integral values ​​on curve k26 and the multiple integral values ​​on curve k27 is σDF_k26,k27 = 30.92 (%).

[0353] As a result, all of the standard deviation of the differences σDF_k25,k26 (=15.22(%)), the standard deviation of the differences σDF_k25,k27 (=27.76(%)), and the standard deviation of the differences σDF_k26,k27 (=30.92(%)) are within the threshold value 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 result obtained by adding the integral values ​​of at least two of curves k22, k23, and k24, which were created based on cyclic voltammograms CVG_600 mV / s, CVG_500 mV / s, and CVG_300 mV / 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] 32 is a second schematic diagram showing new index curves for identifying an analyte. Referring to FIG. 32, curve k28 shows an integral spectrum in which the integral value in each class is the result of subtracting the three integral values ​​in each class of the three curves k22 to k24 shown in FIG.

[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 curves among the curves k28 to k30 are different for all combinations of two curves among 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 are different for three combinations of two curves: (k28, k29), (k28, k30), and (k29, k30).

[0364] Referring to Figure 33, the standard deviation σDF_k28,k29 of the differences between the multiple integral values ​​on curve k28 and the multiple integral values ​​on curve k29 is σDF_k28,k29 = 51.37 (%), the standard deviation σDF_k28,k30 of the differences between the multiple integral values ​​on curve k28 and the multiple integral values ​​on curve k30 is σDF_k28,k30 = 6415.85 (%), and the standard deviation σDF_k29,k30 of the differences between the multiple integral values ​​on curve k29 and the multiple integral values ​​on curve k30 is σDF_k29,k30 = 87.68 (%).

[0365] As a result, the standard deviation of the differences σDF_k28,k29 (=51.37(%)), the standard deviation of the differences σDF_k28,k30 (=6415.85(%)), and the standard deviation of the differences σDF_k29,k30 (=87.68(%)) all fall within the threshold 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_600 mV / s, CVG_500 mV / s, and CVG_300 mV / 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 obtained by 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 integrals of two of the curves k22, k23, and k24 from the integral of one of the remaining two curves, for each class, the integrals of the curves k22 and k24 may be subtracted from the integral of the curve k23 (k23-k22-k24), or the integrals of the curves k22 and k23 may be subtracted from the integral of the 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 differences] (A) Calpis Figure 34 shows the integral value 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 3.

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

[0374] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 of 1536 mV (=integral value extraction potential), and class 3 consists of a predetermined potential section of 1920 mV (=integral value extraction potential).

[0376] FIG. 35 is a diagram showing the results of determining whether 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] Referring to Figure 35, the standard deviation σDF_k31,k32 of the difference between the multiple integral values ​​(= three integral values) on curve 31 and the multiple integral values ​​(= three integral values) on curve k32 is 42.50 (%), the standard deviation σDF_k31,k33 of the difference between the multiple integral values ​​(= three integral values) on curve 31 and the multiple integral values ​​(= three integral values) on curve k33 is 71.86 (%), and the standard deviation σDF_k31,k34 of the difference between the multiple integral values ​​(= three integral values) on curve 31 and the multiple integral values ​​(= three integral values) on curve k34 is 54.84 (%).

[0381] Furthermore, the standard deviation σDF_k32,k33 of the difference between the multiple integral values ​​(=3 integral values) on curve 32 and the multiple integral values ​​(=3 integral values) on curve k33 is 31.44(%), the standard deviation σDF_k32,k34 of the difference between the multiple integral values ​​(=3 integral values) on curve 32 and the multiple integral values ​​(=3 integral values) on curve k34 is 15.15(%), and the standard deviation σDF_k33,k34 of the difference between the multiple integral values ​​(=3 integral values) on curve 33 and the multiple integral values ​​(=3 integral values) on curve k34 is 23.15(%).

[0382] As a result, all of the standard deviation of differences σDF_k31, k32 (= 42.50(%)), the standard deviation of differences σDF_k31, k33 (= 71.86(%)), the standard deviation of differences σDF_k31, k34 (= 54.84(%)), the standard deviation of differences σDF_k32, k33 (= 31.44(%)), the standard deviation of differences σDF_k32, k34 (= 15.15(%)), and the standard deviation of differences σDF_k33, k34 (= 23.15(%)) were within the threshold value σ ｔｈ (=15(%)) is greater.

[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 two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water when the number of integral values ​​is four.

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

[0386] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 of 1152 mV (=integral value extraction potential), and class 4 consists of a predetermined potential section of 1536 mV (=integral value extraction potential).

[0388] FIG. 37 is a diagram showing the results of determining whether 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 Figure 37, the standard deviation σDF_k35,k36 of the difference between the multiple integral values ​​(=4 integral values) on curve k35 and the multiple integral values ​​(=4 integral values) on curve k36 is 48.30 (%), the standard deviation σDF_k35,k37 of the difference between the multiple integral values ​​(=4 integral values) on curve k35 and the multiple integral values ​​(=4 integral values) on curve k37 is 56.45 (%), and the standard deviation σDF_k35,k38 of the difference between the multiple integral values ​​(=4 integral values) on curve k35 and the multiple integral values ​​(=4 integral values) on curve k38 is 53.12 (%).

[0393] Furthermore, the standard deviation σDF_k36,k37 of the difference between the multiple integral values ​​(=4 integral values) on curve k36 and the multiple integral values ​​(=4 integral values) on curve k37 is 12.30(%), the standard deviation σDF_k36,k38 of the difference between the multiple integral values ​​(=4 integral values) on curve k36 and the multiple integral values ​​(=4 integral values) on curve k38 is 6.86(%), and the standard deviation σDF_k37,k38 of the difference between the multiple integral values ​​(=4 integral values) on curve k37 and the multiple integral values ​​(=4 integral values) on curve k38 is 11.70(%).

[0394] As a result, the standard deviation of the difference σDF_k35, k36 (=48.30(%)), the standard deviation of the difference σDF_k35, k37 (=56.45(%)), and the standard deviation of the difference σDF_k35, k38 (=53.12(%)) are all within the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k36, k37 (=12.30(%)), the standard deviation of the differences σDF_k36, k38 (=6.86(%)), and the standard deviation of the differences σDF_k37, k38 (=11.70(%)) are larger than the threshold value σ ｔｈ (=15(%)).

[0395] Therefore, the two curves k35 and k36 are different, the two curves k35 and k37 are different, the two curves k35 and k38 are different, the two curves k36 and k37 are not different, the two curves k36 and k38 are not different, and the two curves k37 and 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 can be determined 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 two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water when the number of integral values ​​is five.

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

[0400] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=five integral values) for each of curves k43 to k46 were the same as those shown in Table 2 above, except that the predetermined potential interval (=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 of 960 mV (=integral value extraction potential), and class 5 consists of a predetermined potential section of 1152 mV (=integral value extraction potential).

[0402] FIG. 39 is a diagram showing the results of determining whether 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 any 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 Figure 39, the standard deviation σDF_k43,k44 of the difference between the multiple integral values ​​(= 5 integral values) on curve k43 and the multiple integral values ​​(= 5 integral values) on curve k44 is 39.20 (%), the standard deviation σDF_k43,k45 of the difference between the multiple integral values ​​(= 5 integral values) on curve k43 and the multiple integral values ​​(= 5 integral values) on curve k45 is 44.75 (%), and the standard deviation σDF_k43,k46 of the difference between the multiple integral values ​​(= 5 integral values) on curve k43 and the multiple integral values ​​(= 5 integral values) on curve k46 is 39.30 (%).

[0407] Furthermore, the standard deviation σDF_k44,k45 of the difference between the multiple integral values ​​(= 5 integral values) on curve k44 and the multiple integral values ​​(= 5 integral values) on curve k45 is 13.02 (%), the standard deviation σDF_k44,k46 of the difference between the multiple integral values ​​(= 5 integral values) on curve k44 and the multiple integral values ​​(= 5 integral values) on curve k46 is 10.04 (%), and the standard deviation σDF_k45,k46 of the difference between the multiple integral values ​​(= 5 integral values) on curve k45 and the multiple integral values ​​(= 5 integral values) on curve k46 is 19.83 (%).

[0408] As a result, the standard deviation of the differences σDF_k43, k44 (=39.20(%)), the standard deviation of the differences σDF_k43, k45 (=44.75(%)), the standard deviation of the differences σDF_k43, k46 (=39.30(%)), and the standard deviation of the differences σDF_k45, k46 (=19.83(%)) are all within the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k44, k45 (=13.02(%)) and the standard deviation of the differences σDF_k44, k46 (=10.04(%)) are larger than the threshold value σ ｔｈ (=15(%)).

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

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

[0413] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=six integral values) in each of curves k47 to k50 were the same as those shown in Table 2 above, except that the predetermined 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 of 768 mV (=integral value extraction potential), and class 6 consists of a predetermined potential section of 1152 mV (=integral value extraction potential).

[0415] FIG. 41 is a diagram showing the results of determining whether 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 Figure 41, the standard deviation σDF_k47,k48 of the difference between the multiple integral values ​​(= 6 integral values) on curve k47 and the multiple integral values ​​(= 6 integral values) on curve k48 is 38.05 (%), the standard deviation σDF_k47,k49 of the difference between the multiple integral values ​​(= 6 integral values) on curve k47 and the multiple integral values ​​(= 6 integral values) on curve k49 is 45.94 (%), and the standard deviation σDF_k47,k50 of the difference between the multiple integral values ​​(= 6 integral values) on curve k47 and the multiple integral values ​​(= 6 integral values) on curve k50 is 40.25 (%).

[0420] Furthermore, the standard deviation σDF_k48,k49 of the difference between the multiple integral values ​​(= 6 integral values) on curve k48 and the multiple integral values ​​(= 6 integral values) on curve k49 is 16.90(%), the standard deviation σDF_k48,k50 of the difference between the multiple integral values ​​(= 6 integral values) on curve k48 and the multiple integral values ​​(= 6 integral values) on curve k50 is 11.89(%), and the standard deviation σDF_k49,k50 of the difference between the multiple integral values ​​(= 6 integral values) on curve k49 and the multiple integral values ​​(= 6 integral values) on curve k50 is 18.04(%).

[0421] As a result, the standard deviation of the differences σDF_k47, k48 (=38.05(%)), the standard deviation of the differences σDF_k47, k49 (=45.94(%)), the standard deviation of the differences σDF_k47, k50 (=40.25(%)), the standard deviation of the differences σDF_k48, k49 (=16.90(%)), and the standard deviation of the differences σDF_k49, k50 (=18.04(%)) are all within the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k48,k50 (=11.89(%)) is larger than the threshold value σ ｔｈ (=15(%)).

[0422] Therefore, the two curves k47 and k48 are different, the two curves k47 and k49 are different, the two curves k47 and k50 are different, the two curves k48 and k49 are different, the two curves k49 and k50 are different, and the two curves k48 and 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 two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water when the number of integral values ​​is eight.

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

[0426] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 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 Figure 43, the standard deviation σDF_k51,k52 of the difference between the multiple integral values ​​(=8 integral values) on curve k51 and the multiple integral values ​​(=8 integral values) on curve k52 is 87.13 (%), the standard deviation σDF_k51,k53 of the difference between the multiple integral values ​​(=8 integral values) on curve k51 and the multiple integral values ​​(=8 integral values) on curve k53 is 88.91 (%), and the standard deviation σDF_k51,k54 of the difference between the multiple integral values ​​(=8 integral values) on curve k51 and the multiple integral values ​​(=8 integral values) on curve k54 is 87.77 (%).

[0433] Furthermore, the standard deviation σDF_k52,k53 of the difference between the multiple integral values ​​(= 8 integral values) on curve k52 and the multiple integral values ​​(= 8 integral values) on curve k53 is 32.87% (%), the standard deviation σDF_k52,k54 of the difference between the multiple integral values ​​(= 8 integral values) on curve k52 and the multiple integral values ​​(= 8 integral values) on curve k54 is 24.08% (%), and the standard deviation σDF_k53,k54 of the difference between the multiple integral values ​​(= 8 integral values) on curve k53 and the multiple integral values ​​(= 8 integral values) on curve k54 is 23.98% (%).

[0434] As a result, all of the standard deviation of differences σDF_k51, k52 (= 87.13(%)), the standard deviation of differences σDF_k51, k53 (= 88.91(%)), the standard deviation of differences σDF_k51, k54 (= 87.77(%)), the standard deviation of differences σDF_k52, k53 (= 32.87(%)), the standard deviation of differences σDF_k52, k54 (= 24.08(%)), and the standard deviation of differences σDF_k53, k54 (= 23.98(%)) were within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0438] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=13 integral values) in each of curves k55 to k58 were the same as those shown in Table 2 above, except that the predetermined potential interval (=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 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 any 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 Figure 45, the standard deviation σDF_k55,k56 of the difference between the multiple integral values ​​(=13 integral values) on curve k55 and the multiple integral values ​​(=13 integral values) on curve k56 is 45.88 (%), the standard deviation σDF_k55,k57 of the difference between the multiple integral values ​​(=13 integral values) on curve k55 and the multiple integral values ​​(=13 integral values) on curve k57 is 75.41 (%), and the standard deviation σDF_k55,k58 of the difference between the multiple integral values ​​(=13 integral values) on curve k55 and the multiple integral values ​​(=13 integral values) on curve k58 is 71.80 (%).

[0445] Furthermore, the standard deviation σDF_k56,k57 of the difference between the multiple integral values ​​(=13 integral values) on curve k56 and the multiple integral values ​​(=13 integral values) on curve k57 is 55.94% (%), the standard deviation σDF_k56,k58 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 is 50.10% (%), and the standard deviation σDF_k57,k58 of the difference between the multiple integral values ​​(=13 integral values) on curve k57 and the multiple integral values ​​(=13 integral values) on curve k58 is 58.32% (%).

[0446] As a result, all of the standard deviation of differences σDF_k55,k56 (=45.88(%)), the standard deviation of differences σDF_k55,k57 (=75.41(%)), the standard deviation of differences σDF_k55,k58 (=71.80(%)), the standard deviation of differences σDF_k56,k57 (=55.94(%)), the standard deviation of differences σDF_k56,k58 (=50.10(%)), and the standard deviation of differences σDF_k57,k58 (=58.32(%)) were within the threshold σ ｔｈ (=15(%)) is greater.

[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-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 ​​(=the 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 σ ＤＦ＿ｋ３，ｋ４ (=48.83(%)) are all within the threshold σ ｔｈ (= 15(%)).

[0449] Therefore, it was found that 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-fold with tap water, Calpis diluted 3-fold with tap water, and Calpis diluted 4-fold 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 integrals is 3, curve k51 when the number of integrals is 8, curve k55 when the number of integrals is 13, and curve k1 when the number of integrals is 26.

[0455] Furthermore, the integral spectrum (= index curve) for uniquely identifying Calpis diluted 2 times with tap water consists of curve k32 when the number of integrals is 3, curve k52 when the number of integrals is 8, curve k56 when the number of integrals is 13, and curve k2 when the number of integrals 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, an integral spectrum (= index curve) for uniquely identifying undiluted Calpis is made up of a plurality of (= four) index curves (= curves k31, k51, k55, k1) with mutually different numbers of integral values, and an integral spectrum (= index curve) for uniquely identifying Calpis diluted 2-fold with tap water is made up of a plurality of (= four) index curves (= curves k32, k52, k56, k2) with mutually different numbers of integral values. The integral spectrum (= index curve) for uniquely identifying Calpis diluted three times with tap water consists of a plurality (= four) of index curves (= curves k33, k53, k57, k3) with different numbers of integral values, and the integral spectrum (= index curve) for uniquely identifying Calpis diluted four times with tap water consists of a plurality (= four) of 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 two-fold with tap water, Calpis diluted three-fold with tap water, and Calpis diluted four-fold with tap water is three.

[0460] (B) Red Wine 1 FIG. 46 shows 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 curves k62 to k64 is a curve CUR created by the above-described method by analysis device 2. Curve k62 represents the integral spectrum of red wine (Chile 2020), curve k63 represents the integral spectrum of red wine (Australia 2010), and curve k64 represents the integral spectrum of red wine (France 2013).

[0462] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 of 1654.4 mV (=integral value extraction potential), and class 3 consists of a predetermined potential section of 1689.6 mV (=integral value extraction potential).

[0464] FIG. 47 is a diagram showing the results of determining whether the curves k62 to k64 shown in FIG. 46 are different from one another.

[0465] Whether or not the curves k62 to k64 differ from one another is determined by determining whether or not any two curves among the curves k62 to k64 differ from one another for all combinations of two curves among 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 or not the two curves differ for three cases: (k62, k63), (k62, k64), and (k63, k64).

[0468] Referring to Figure 47, the standard deviation σDF_k62,k63 of the difference between the multiple integral values ​​(=3 integral values) on curve k62 and the multiple integral values ​​(=3 integral values) on curve k63 is 19.05 (%), the standard deviation σDF_k62,k64 of the difference between the multiple integral values ​​(=3 integral values) on curve k62 and the multiple integral values ​​(=3 integral values) on curve k64 is 71.82 (%), and the standard deviation σDF_k63,k64 of the difference between the multiple integral values ​​(=3 integral values) on curve k63 and the multiple integral values ​​(=3 integral values) on curve k64 is 86.34 (%).

[0469] As a result, all of the standard deviation of the differences σDF_k62, k63 (=19.05(%)), the standard deviation of the differences σDF_k62, k64 (=71.82(%)), and the standard deviation of the differences σDF_k63, k64 (=86.34(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0473] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 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 any 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 or not the two curves differ for three cases: (k65, k66), (k65, k67), and (k66, k67).

[0479] Referring to Figure 49, the standard deviation σDF_k65,k66 of the difference between the multiple integral values ​​(=4 integral values) on curve k65 and the multiple integral values ​​(=4 integral values) on curve k66 is 19.34 (%), the standard deviation σDF_k65,k67 of the difference between the multiple integral values ​​(=4 integral values) on curve k65 and the multiple integral values ​​(=4 integral values) on curve k67 is 80.96 (%), and the standard deviation σDF_k66,k67 of the difference between the multiple integral values ​​(=4 integral values) on curve k66 and the multiple integral values ​​(=4 integral values) on curve k67 is 91.77 (%).

[0480] As a result, all of the standard deviation of the differences σDF_k65, k66 (=19.34(%)), the standard deviation of the differences σDF_k65, k67 (=80.96(%)), and the standard deviation of the differences σDF_k66, k67 (=91.77(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0484] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=six integral values) in each of curves k68 to k70 were the same as those shown in Table 3 above, except that the predetermined potential interval (=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 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 any two curves among the curves k68 to k70 are different for all combinations of two curves among the curves k68 to k70.

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

[0489] Therefore, FIG. 51 shows whether or not the two curves differ for the three cases (k68, k69), (k68, k70), and (k69, k70).

[0490] Referring to Figure 51, the standard deviation σDF_k68,k69 of the difference between the multiple integral values ​​(=6 integral values) on curve k68 and the multiple integral values ​​(=6 integral values) on curve k69 is 27.33 (%), the standard deviation σDF_k68,k70 of the difference between the multiple integral values ​​(=6 integral values) on curve k68 and the multiple integral values ​​(=6 integral values) on curve k70 is 70.78 (%), and the standard deviation σDF_k69,k70 of the difference between the multiple integral values ​​(=6 integral values) on curve k69 and the multiple integral values ​​(=6 integral values) on curve k70 is 80.57 (%).

[0491] As a result, all of the standard deviation of the differences σDF_k68,k69 (=27.33(%)), the standard deviation of the differences σDF_k68,k70 (=70.78(%)), and the standard deviation of the differences σDF_k69,k70 (=80.57(%)) are within the threshold σ ｔｈ (=15(%)) is greater.

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

[0495] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 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 any 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 three cases: (k71, k72), (k71, k73), and (k72, k73).

[0501] Referring to Figure 53, the standard deviation σDF_k71,k72 of the difference between the multiple integral values ​​(=8 integral values) on curve k71 and the multiple integral values ​​(=8 integral values) on curve k72 is 34.73 (%), the standard deviation σDF_k71,k73 of the difference between the multiple integral values ​​(=8 integral values) on curve k71 and the multiple integral values ​​(=8 integral values) on curve k73 is 67.65 (%), and the standard deviation σDF_k72,k73 of the difference between the multiple integral values ​​(=8 integral values) on curve k72 and the multiple integral values ​​(=8 integral values) on curve k73 is 77.44 (%).

[0502] As a result, all of the standard deviations of the differences σDF_k71, k72 (=34.73(%)), the standard deviations of the differences σDF_k71, k73 (=67.65(%)), and the standard deviations of the differences σDF_k72, k73 (=77.44(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0506] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 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 curves among the curves k74 to k76 are different for all combinations of two curves among 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 or not the two curves differ for three cases: (k74, k75), (k74, k76), and (k75, k76).

[0512] Referring to Figure 55, the standard deviation σDF_k74,k75 of the difference between the multiple integral values ​​(=10 integral values) on curve k74 and the multiple integral values ​​(=10 integral values) on curve k75 is 45.00 (%), the standard deviation σDF_k74,k76 of the difference between the multiple integral values ​​(=10 integral values) on curve k74 and the multiple integral values ​​(=10 integral values) on curve k76 is 77.58 (%), and the standard deviation σDF_k75,k76 of the difference between the multiple integral values ​​(=10 integral values) on curve k75 and the multiple integral values ​​(=10 integral values) on curve k76 is 89.41 (%).

[0513] As a result, all of the standard deviation of the differences σDF_k74, k75 (=45.00(%)), the standard deviation of the differences σDF_k74, k76 (=77.58(%)), and the standard deviation of the differences σDF_k75, k76 (=89.41(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0517] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=14 integral values) in each of curves k77 to k79 were the same as those shown in Table 3 above, except that the predetermined potential interval (=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 is made up of a predetermined potential section (=integral value extraction potential) of 352 mV, and class 14 is made up of 422.4 mV.

[0519] FIG. 57 is a diagram showing the results of determining whether 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 any 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 or not the two curves differ for three cases: (k77, k78), (k77, k79), and (k78, k79).

[0523] Referring to Figure 57, the standard deviation σDF_k77,k78 of the difference between the multiple integral values ​​(=14 integral values) on curve k77 and the multiple integral values ​​(=14 integral values) on curve k78 is 61.41 (%), the standard deviation σDF_k77,k79 of the difference between the multiple integral values ​​(=14 integral values) on curve k77 and the multiple integral values ​​(=14 integral values) on curve k79 is 78.91 (%), and the standard deviation σDF_k78,k79 of the difference between the multiple integral values ​​(=14 integral values) on curve k78 and the multiple integral values ​​(=14 integral values) on curve k79 is 91.76 (%).

[0524] As a result, all of the standard deviation of the differences σDF_k77, k78 (=61.41(%)), the standard deviation of the differences σDF_k77, k79 (=78.91(%)), and the standard deviation of the differences σDF_k78, k79 (=91.76(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0528] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 is made up of a predetermined potential section (=integral value extraction potential) of 176 mV, and class 28 is made up of 246.4 mV.

[0530] FIG. 59 is a diagram showing the results of determining whether 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 or not the two curves differ for three cases: (k80, k81), (k80, k82), and (k81, k82).

[0534] Referring to Figure 59, the standard deviation σDF_k80,k81 of the difference between the multiple integral values ​​(=28 integral values) on curve k80 and the multiple integral values ​​(=28 integral values) on curve k81 is 58.15 (%), the standard deviation σDF_k80,k82 of the difference between the multiple integral values ​​(=28 integral values) on curve k80 and the multiple integral values ​​(=28 integral values) on curve k82 is 71.16 (%), and the standard deviation σDF_k81,k82 of the difference between the multiple integral values ​​(=28 integral values) on curve k81 and the multiple integral values ​​(=28 integral values) on curve k82 is 89.81 (%).

[0535] As a result, all of the standard deviation of the differences σDF_k80,k81 (=58.15(%)), the standard deviation of the differences σDF_k80,k82 (=71.16(%)), and the standard deviation of the differences σDF_k81,k82 (=89.81(%)) are within the threshold σ ｔｈ (=15(%)) is greater.

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

[0539] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=57 integral values) in each of curves k83 to k85 were the same as those shown in Table 3 above, except that the predetermined potential interval (=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 any 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 or not the two curves differ for three cases: (k83, k84), (k83, k85), and (k84, k85).

[0545] Referring to Figure 61, the standard deviation σDF_k83,k84 of the difference between the multiple integral values ​​(=57 integral values) on curve k83 and the multiple integral values ​​(=57 integral values) on curve k84 is 67.36 (%), the standard deviation σDF_k83,k85 of the difference between the multiple integral values ​​(=57 integral values) on curve k83 and the multiple integral values ​​(=57 integral values) on curve k85 is 76.13 (%), and the standard deviation σDF_k84,k85 of the difference between the multiple integral values ​​(=57 integral values) on curve k84 and the multiple integral values ​​(=57 integral values) on curve k85 is 95.05 (%).

[0546] As a result, all of the standard deviations of the differences σDF_k83, k84 (=67.36(%)), the standard deviations of the differences σDF_k83, k85 (=76.13(%)), and the standard deviations of the differences σDF_k84, k85 (=95.05(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0550] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 any 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 or not the two curves differ for the three cases (k86, k87), (k86, k88), and (k87, k88).

[0556] Referring to Figure 63, the standard deviation σDF_k86,k87 of the difference between the multiple integral values ​​(=95 integral values) on curve k86 and the multiple integral values ​​(=95 integral values) on curve k87 is 65.84 (%), the standard deviation σDF_k86,k88 of the difference between the multiple integral values ​​(=95 integral values) on curve k86 and the multiple integral values ​​(=95 integral values) on curve k88 is 75.14 (%), and the standard deviation σDF_k87,k88 of the difference between the multiple integral values ​​(=95 integral values) on curve k87 and the multiple integral values ​​(=95 integral values) on curve k88 is 93.73 (%).

[0557] As a result, all of the standard deviation of the differences σDF_k86, k87 (=65.84(%)), the standard deviation of the differences σDF_k86, k88 (=75.14(%)), and the standard deviation of the differences σDF_k87, k88 (=93.73(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0561] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=142 integral values) in each of curves k89 to k91 were the same as those shown in Table 3 above, except that the predetermined potential interval (=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 any 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 and k91: (k89, k90), (k89, k91), and (k90, k91).

[0566] Therefore, FIG. 65 shows whether or not the two curves differ for the three cases (k89, k90), (k89, k91), and (k90, k91).

[0567] Referring to Figure 65, the standard deviation σDF_k89,k90 of the difference between the multiple integral values ​​(=142 integral values) on curve k89 and the multiple integral values ​​(=142 integral values) on curve k90 is 66.26 (%), the standard deviation σDF_k89,k91 of the difference between the multiple integral values ​​(=142 integral values) on curve k89 and the multiple integral values ​​(=142 integral values) on curve k91 is 75.30 (%), and the standard deviation σDF_k90,k91 of the difference between the multiple integral values ​​(=142 integral values) on curve k90 and the multiple integral values ​​(=142 integral values) on curve k91 is 93.45 (%).

[0568] As a result, all of the standard deviation of the differences σDF_k89,k90 (=66.26(%)), the standard deviation of the differences σDF_k89,k91 (=75.30(%)), and the standard deviation of the differences σDF_k90,k91 (=93.45(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

[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] FIG. 16 shows the integral spectra 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 FIG. 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, it was found that when the number of integral values ​​is 3, 4, 6, 8, 10, 14, 28, 57, 95, 142 and 284, it is possible to obtain integral spectrum (= curve CUR) for uniquely identifying 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 spectrum for uniquely identifying red wine (Chile 2020), curve k63 is an integral spectrum for uniquely identifying red wine (Australia 2010), and curve k64 is an integral spectrum for uniquely identifying red wine (France 2013).

[0573] Furthermore, when the number of integral values ​​is four, curve k65 is an integral spectrum for uniquely identifying red wine (Chile 2020), curve k66 is an integral spectrum for uniquely identifying red wine (Australia 2010), and curve k67 is an integral 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] In this case, 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] Furthermore, the integral spectrum (= index curve) for uniquely identifying red wine (Australia 2010) consists of a curve k63 when the number of integrals is 3, a curve k66 when the number of integrals is 4, a curve k69 when the number of integrals is 6, a curve k72 when the number of integrals is 8, a curve k75 when the number of integrals is 10, a curve k78 when the number of integrals is 14, a curve k81 when the number of integrals is 28, a curve k84 when the number of integrals is 57, a curve k87 when the number of integrals is 95, a curve k90 when the number of integrals is 142, and a 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, an integral spectrum (= index curve) for uniquely identifying a red wine (Chile 2020) is made up of multiple (= 11) index curves (= curves k62, k65, k68, k71, k74, k77, k80, k83, k86, k89, k5) with mutually different numbers of integral values, and an integral spectrum (= index curve) for uniquely identifying a red wine (Australia 2010) is made up of multiple (= 11) index curves (= curves k62, k65, k68, k71, k74, k77, k80, k83, k86, k89, k5) with mutually different numbers of integral values. The integral spectrum (= index curve) for uniquely identifying red wine (France 2013) is made up 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) is made up 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 then found that the minimum number of integrals required to obtain integral spectra that can uniquely identify each of red wine (Chile 2020), red wine (Australia 2010), and red wine (France 2013) is three.

[0588] (C) Red Wine 2 FIG. 66 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.

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

[0590] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=three integral values) in each of curves k95 to k97 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 1054 mV.

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

[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 any 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 or not the two curves differ for three cases: (k95, k96), (k95, k97), and (k96, k97).

[0596] Referring to Figure 67, the standard deviation σDF_k95,k96 of the difference between the multiple integral values ​​(=3 integral values) on curve k95 and the multiple integral values ​​(=3 integral values) on curve k96 is 7.62 (%), the standard deviation σDF_k95,k97 of the difference between the multiple integral values ​​(=3 integral values) on curve k95 and the multiple integral values ​​(=3 integral values) on curve k97 is 16.68 (%), and the standard deviation σDF_k96,k97 of the difference between the multiple integral values ​​(=3 integral values) on curve k96 and the multiple integral values ​​(=3 integral values) on curve k97 is 10.33 (%).

[0597] As a result, the standard deviation σDF_k95, k97 (=16.68(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k95, k96 (=7.62(%)) and the standard deviation of the differences σDF_k96, k97 (=10.33(%)) are larger than the threshold value 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 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.

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

[0602] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=four integral values) in 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 any 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 or not the two curves differ for three cases: (k98, k99), (k98, k100), and (k99, k100).

[0608] Referring to Figure 69, the standard deviation σDF_k98,k99 of the difference between the multiple integral values ​​(=4 integral values) on curve k98 and the multiple integral values ​​(=4 integral values) on curve k99 is 30.51 (%), the standard deviation σDF_k98,k100 of the difference between the multiple integral values ​​(=4 integral values) on curve k98 and the multiple integral values ​​(=4 integral values) on curve k100 is 35.29 (%), and the standard deviation σDF_k99,k100 of the difference between the multiple integral values ​​(=4 integral values) on curve k99 and the multiple integral values ​​(=4 integral values) on curve k100 is 9.36 (%).

[0609] As a result, the standard deviation of the difference σDF_k98,k99 (=30.51(%)) and the standard deviation of the difference σDF_k98,k100 (=35.29(%)) are smaller than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the difference σDF_k99, k100 (=9.36(%)) is larger than 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 a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italy, year unknown) when the number of integrals is six.

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

[0614] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=six integral values) for each of curves k101 to k103 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 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 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 any 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 three cases: (k101, k102), (k101, k103), and (k102, k103).

[0620] Referring to Figure 71, the standard deviation σDF_k101, k102 of the difference between the multiple integral values ​​(= 6 integral values) on curve k101 and the multiple integral values ​​(= 6 integral values) on curve k102 is 16.42 (%), the standard deviation σDF_k101, k103 of the difference between the multiple integral values ​​(= 6 integral values) on curve k101 and the multiple integral values ​​(= 6 integral values) on curve k103 is 21.20 (%), and the standard deviation σDF_k102, k103 of the difference between the multiple integral values ​​(= 6 integral values) on curve k102 and the multiple integral values ​​(= 6 integral values) on curve k103 is 13.85 (%).

[0621] As a result, the standard deviation of the differences σDF_k101, k102 (=16.42(%)) and the standard deviation of the differences σDF_k101, k103 (=21.20(%)) are smaller than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k102, k103 (=13.85(%)) is larger than 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 curves k104 to k106 is a curve CUR created by the above-described method by the analysis device 2. Curve k104 represents the integral spectrum of a red wine (Italy 2015), curve k105 represents the integral spectrum of a red wine (France 2016), and curve k106 represents the integral spectrum of a red wine (Italy, year unknown).

[0626] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=7 integral values) in each of curves k104 to k106 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 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 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 or not the two curves differ for three cases: (k104, k105), (k104, k106), and (k105, k106).

[0632] Referring to Figure 73, the standard deviation σDF_k104, k105 of the difference between the multiple integral values ​​(= 7 integral values) on curve k104 and the multiple integral values ​​(= 7 integral values) on curve k105 is 20.05 (%), the standard deviation σDF_k104, k106 of the difference between the multiple integral values ​​(= 7 integral values) on curve k104 and the multiple integral values ​​(= 7 integral values) on curve k106 is 23.59 (%), and the standard deviation σDF_k105, k106 of the difference between the multiple integral values ​​(= 7 integral values) on curve k105 and the multiple integral values ​​(= 7 integral values) on curve k106 is 15.11 (%).

[0633] As a result, all of the standard deviations of the differences σDF_k104, k105 (=20.05(%)), σDF_k104, k106 (=23.59(%)), and σDF_k105, k106 (=15.11(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

[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 a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italy, year unknown) when the number of integrals is eight.

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

[0637] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 interval (=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 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 or not the two curves differ for the three cases (k107, k108), (k107, k109), and (k108, k109).

[0643] Referring to Figure 75, the standard deviation σDF_k107,k108 of the difference between the multiple integral values ​​(=8 integral values) on curve k107 and the multiple integral values ​​(=8 integral values) on curve k108 is 62.94 (%), the standard deviation σDF_k107,k109 of the difference between the multiple integral values ​​(=8 integral values) on curve k107 and the multiple integral values ​​(=8 integral values) on curve k109 is 67.43 (%), and the standard deviation σDF_k108,k109 of the difference between the multiple integral values ​​(=8 integral values) on curve k108 and the multiple integral values ​​(=8 integral values) on curve k109 is 38.08 (%).

[0644] As a result, all of the standard deviations of the differences σDF_k107, k108 (=62.94(%)), σDF_k107, k109 (=67.43(%)), and σDF_k108, k109 (=38.08(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

[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 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.

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

[0648] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=11 integral values) in each of curves k110 to k112 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 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 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 three cases: (k110, k111), (k110, k112), and (k111, k112).

[0654] Referring to FIG. 77 , the standard deviation σDF_k110,k111 of the difference between the multiple integral values ​​(=11 integral values) on curve k110 and the multiple integral values ​​(=11 integral values) on curve k111 is 27.60(%), the standard deviation σDF_k110,k112 of the difference between the multiple integral values ​​(=11 integral values) on curve k110 and the multiple integral values ​​(=11 integral values) on curve k112 is 30.63(%), and the standard deviation σDF_k111,k112 of the difference between the multiple integral values ​​(=11 integral values) on curve k111 and the multiple integral values ​​(=11 integral values) on curve k112 is 16.41(%).

[0655] As a result, all of the standard deviation of the differences σDF_k110,k111 (=27.60(%)), the standard deviation of the differences σDF_k110,k112 (=30.63(%)), and the standard deviation of the differences σDF_k111,k112 (=16.41(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0659] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=16 integral values) in each of curves k113 to k115 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 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 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 or not the two curves differ for three cases: (k113, k114), (k113, k115), and (k114, k115).

[0665] Referring to Figure 79, the standard deviation σDF_k113,k114 of the difference between the multiple integral values ​​(=16 integral values) on curve k113 and the multiple integral values ​​(=16 integral values) on curve k114 is 25.16 (%), the standard deviation σDF_k113,k115 of the difference between the multiple integral values ​​(=16 integral values) on curve k113 and the multiple integral values ​​(=16 integral values) on curve k115 is 29.10 (%), and the standard deviation σDF_k114,k115 of the difference between the multiple integral values ​​(=16 integral values) on curve k114 and the multiple integral values ​​(=16 integral values) on curve k115 is 16.01 (%).

[0666] As a result, all of the standard deviations of the differences σDF_k113, k114 (=25.16(%)), the standard deviations of the differences σDF_k113, k115 (=29.10(%)), and the standard deviations of the differences σDF_k114, k115 (=16.01(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

[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 a red wine (Italy 2015), a red wine (France 2016), and a red wine (Italy, year unknown) when the number of integrals is 19.

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

[0670] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=19 integral values) in each of curves k116 to k118 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 170 mV.

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

[0672] FIG. 81 is a diagram showing the results of determining whether 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 curves among the curves k116 to k118 are different for all combinations of two curves among 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 or not the two curves differ for three cases: (k116, k117), (k116, k118), and (k117, k118).

[0676] Referring to FIG. 81 , the standard deviation σDF_k116, k117 of the difference between the multiple integral values ​​(= 19 integral values) on curve k116 and the multiple integral values ​​(= 19 integral values) on curve k117 is 28.90(%), the standard deviation σDF_k116, k118 of the difference between the multiple integral values ​​(= 19 integral values) on curve k116 and the multiple integral values ​​(= 19 integral values) on curve k118 is 33.05(%), and the standard deviation σDF_k117, k118 of the difference between the multiple integral values ​​(= 19 integral values) on curve k117 and the multiple integral values ​​(= 19 integral values) on curve k118 is 17.31(%).

[0677] As a result, all of the standard deviations of the differences σDF_k116, k117 (=28.90(%)), σDF_k116, k118 (=33.05(%)), and σDF_k117, k118 (=17.31(%)) are within the threshold value σ ｔｈ (=15(%)) is greater.

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

[0681] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=47 integral values) in each of curves k119 to k121 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 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 differ from one another is determined by determining whether or not any two of the curves k119 to k121 differ from one another 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 or not the two curves differ for three cases: (k119, k120), (k119, k121), and (k120, k121).

[0687] Referring to Figure 83, the standard deviation σDF_k119,k120 of the difference between the multiple integral values ​​(=47 integral values) on curve k119 and the multiple integral values ​​(=47 integral values) on curve k120 is 37.61 (%), the standard deviation σDF_k119,k121 of the difference between the multiple integral values ​​(=47 integral values) on curve k119 and the multiple integral values ​​(=47 integral values) on curve k121 is 39.71 (%), and the standard deviation σDF_k120,k121 of the difference between the multiple integral values ​​(=47 integral values) on curve k129 and the multiple integral values ​​(=47 integral values) on curve k121 is 17.49 (%).

[0688] As a result, all of the standard deviations of the differences σDF_k119, k120 (=37.61(%)), the standard deviations of the differences σDF_k119, k121 (=39.71(%)), and the standard deviations of the differences σDF_k120, k121 (=17.49(%)) are within the threshold σ ｔｈ (=15(%)) is greater.

[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] FIG. 18 shows the integral spectra of red wine (Italy 2015), red wine (France 2016), and red wine (Italy, year unknown) when the number of integrals (= number of classes) is 94, and FIG. 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, it was found that when the number of integral values ​​is 7, 8, 11, 16, 19, 47, and 94, integral value spectra (= curves CUR) can be obtained to uniquely identify red wine (Italy 2015), red wine (France 2016), and red wine (Italy, unknown year), 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 integrals is 11, curve k110 is an integral spectrum for uniquely identifying red wine (Italy 2015), curve k111 is an integral spectrum for uniquely identifying red wine (France 2016), and curve k112 is an integral 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 integrals is 19, curve k116 is an integral spectrum for uniquely identifying red wine (Italy 2015), curve k117 is an integral spectrum for uniquely identifying red wine (France 2016), and curve k118 is an integral spectrum for uniquely identifying red wine (Italy, unknown vintage).

[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 integrals is 94, curve k8 is an integral spectrum for uniquely identifying red wine (Italy 2015), curve k9 is an integral spectrum for uniquely identifying red wine (France 2016), and curve k10 is an integral 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 integral values ​​is 7, curve k108 when the number of integral values ​​is 8, curve k111 when the number of integral values ​​is 11, curve k114 when the number of integral values ​​is 16, curve k117 when the number of integral values ​​is 19, curve k120 when the number of integral values ​​is 47, and curve k9 when the number of integral values ​​is 94.

[0701] Furthermore, the integral spectrum (= index curve) for uniquely identifying red wine (Italy, unknown vintage) 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) is made up of a plurality of (= 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) is made up of a plurality of (= 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) is made up of a plurality of (= seven) index curves (= curves k106, k109, k112, k115, k118, k121, k10) with different numbers of integrals.

[0703] It was found that the minimum number of integrals required to obtain integral spectra that can uniquely identify each of the red wines (Italy 2015), red wine (France 2016), and red wine (Italy, unknown year) 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 the red wine (Italy 2015) and the three integral values ​​on the curve k97 representing the red wine (Italy, year unknown) is ｋ９５，ｋ９７ is the threshold σ ｔｈ (=15(%)) (see FIG. 67 ), and when the number of integrals is four, the standard deviation σ of the difference between the four integrals on the curve k98 representing the red wine (Italy 2015) and the four integrals on the curve k100 representing the red wine (Italy, year unknown) is ｋ９８，ｋ１００ is the threshold σ ｔｈ (=15(%)) (see FIG. 69 ), and the number of integrals is six, the standard deviation σ of the difference between the six integrals on the curve k101 representing the red wine (Italy 2015) and the six integrals on the curve k103 representing the red wine (Italy, year unknown) is ｋ１０１，ｋ１０３ is the threshold σ ｔｈ The result is 21.20(%), which is larger than the 15(%) (see FIG. 71).

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

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

[0707] As a result, the standard deviation of the difference between the multiple integral values ​​on the curve representing red wine (Italy 2015) and the multiple integral values ​​on the curve representing red wine (Italy, unknown vintage) was within the threshold σ for all cases where the number of integral values ​​was 3, 4, 6, 7, 8, 11, 16, 19, 47, and 94. ｔｈ (=15(%)) is greater.

[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 vintage) is three.

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

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

[0712] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 of 1656 mV (=integral value extraction potential), and class 3 consists of a predetermined potential section of 1674.4 mV (=integral value extraction potential).

[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 any 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 or not the two curves differ for three cases: (k125, k126), (k125, k127), and (k126, k127).

[0718] Referring to Figure 85, the standard deviation σDF_k125,k126 of the difference between the multiple integral values ​​(=3 integral values) on curve k125 and the multiple integral values ​​(=3 integral values) on curve k126 is 10.36 (%), the standard deviation σDF_k125,k127 of the difference between the multiple integral values ​​(=3 integral values) on curve k125 and the multiple integral values ​​(=3 integral values) on curve k127 is 21.07 (%), and the standard deviation σDF_k126,k127 of the difference between the multiple integral values ​​(=3 integral values) on curve k126 and the multiple integral values ​​(=3 integral values) on curve k127 is 10.73 (%).

[0719] As a result, the standard deviation σDF_k125, k126 (=10.36(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k126, k127 (=10.73(%)) is smaller than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the difference σDF_k125, k127 (=21.07(%)) is smaller than the threshold value σ ｔｈ (=15(%)) is greater.

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

[0723] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 of 1251.2 mV (=integral value extraction potential), and class 4 consists of a predetermined potential section of 1232.8 mV (=integral value extraction potential).

[0725] FIG. 87 is a diagram showing the results of determining whether 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 any two curves among the curves k128 to k130 are different for all combinations of two curves among 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 or not the two curves differ for three cases: (k128, k129), (k128, k130), and (k129, k130).

[0729] Referring to Figure 87, the standard deviation σDF_k128,k129 of the difference between the multiple integral values ​​(=4 integral values) on curve k128 and the multiple integral values ​​(=4 integral values) on curve k129 is 11.64 (%), the standard deviation σDF_k128,k130 of the difference between the multiple integral values ​​(=4 integral values) on curve k128 and the multiple integral values ​​(=4 integral values) on curve k130 is 39.98 (%), and the standard deviation σDF_k129,k130 of the difference between the multiple integral values ​​(=4 integral values) on curve k129 and the multiple integral values ​​(=4 integral values) on curve k130 is 24.26 (%).

[0730] As a result, the standard deviation σDF_k128, k129 (=11.64(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the difference σDF_k128, k130 (=39.98(%)) is smaller than the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k129, k130 (=24.26(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

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

[0734] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=five integral values) for each of curves k131 to k133 were the same as those shown in Table 5 above, except that the predetermined potential interval (=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 of 993.6 mV (=integral value extraction potential), and class 5 consists of a predetermined potential section of 1012 mV (=integral value extraction potential).

[0736] FIG. 89 is a diagram showing the results of determining whether the curves k131 to k133 shown in FIG. 88 are different from one another.

[0737] Whether or not the curves k131 to k133 differ from one another is determined by determining whether or not any two curves among the curves k131 to k133 differ from one another 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 or not the two curves differ for three cases: (k131, k132), (k131, k133), and (k132, k133).

[0740] Referring to Figure 89, the standard deviation σDF_k131, k132 of the difference between the multiple integral values ​​(= 5 integral values) on curve k131 and the multiple integral values ​​(= 5 integral values) on curve k132 is 13.44 (%), the standard deviation σDF_k131, k133 of the difference between the multiple integral values ​​(= 5 integral values) on curve k131 and the multiple integral values ​​(= 5 integral values) on curve k133 is 30.78 (%), and the standard deviation σDF_k132, k133 of the difference between the multiple integral values ​​(= 5 integral values) on curve k132 and the multiple integral values ​​(= 5 integral values) on curve k133 is 19.88 (%).

[0741] As a result, the standard deviation σDF_k131, k132 (=13.44(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k131, k133 (=30.78(%)) is smaller than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k132, k133 (=19.88(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

[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 spectrums for WONDA, Craft Boss, and Gold Brew when the number of integrals is six.

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

[0745] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=six integral values) for each of curves k134 to k136 were the same as those shown in Table 5 above, except that the predetermined potential interval (=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 results of determining whether 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 curves among the curves k134 to k136 are different for all combinations of two curves among 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 or not the two curves differ for three cases: (k134, k135), (k134, k136), and (k135, k136).

[0751] Referring to Figure 91, the standard deviation σDF_k134, k135 of the difference between the multiple integral values ​​(= 6 integral values) on curve k134 and the multiple integral values ​​(= 6 integral values) on curve k135 is 15.26 (%), the standard deviation σDF_k134, k136 of the difference between the multiple integral values ​​(= 6 integral values) on curve k134 and the multiple integral values ​​(= 6 integral values) on curve k136 is 41.83 (%), and the standard deviation σDF_k135, k136 of the difference between the multiple integral values ​​(= 6 integral values) on curve k135 and the multiple integral values ​​(= 6 integral values) on curve k136 is 31.27 (%).

[0752] As a result, the standard deviation σDF_k134, k135 (=15.26(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k134, k136 (=41.83(%)) is larger than the threshold σ ｔｈ (=15(%)), and the standard deviation of the difference σDF_k135, k136 (=31.27(%)) is larger than the threshold σ ｔｈ (=15(%)) is greater.

[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 spectra for WONDA, Craft Boss, and Gold Brew when the number of integrals is seven.

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

[0756] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 or not the two curves differ for three cases: (k137, k138), (k137, k139), and (k138, k139).

[0762] Referring to Figure 93, the standard deviation σDF_k137, k138 of the difference between the multiple integral values ​​(= 7 integral values) on curve k137 and the multiple integral values ​​(= 7 integral values) on curve k138 is 16.64 (%), the standard deviation σDF_k137, k139 of the difference between the multiple integral values ​​(= 7 integral values) on curve k137 and the multiple integral values ​​(= 7 integral values) on curve k139 is 45.35 (%), and the standard deviation σDF_k138, k139 of the difference between the multiple integral values ​​(= 7 integral values) on curve k138 and the multiple integral values ​​(= 7 integral values) on curve k139 is 33.46 (%).

[0763] As a result, the standard deviation σDF_k137, k138 (=16.64(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k137, k139 (=45.35(%)) is larger than the threshold σ ｔｈ (=15(%)), and the standard deviation of the difference σDF_k138, k139 (=33.46(%)) is larger than the threshold σ ｔｈ (=15(%)) is greater.

[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 spectrums for WONDA, Craft Boss, and Gold Brew when the number of integrals is eight.

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

[0767] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 the curves k140 to k142 shown in FIG. 94 are different from one another.

[0770] Whether or not the curves k140 to k142 differ from one another is determined by determining whether or not two curves among the curves k140 to k142 differ from one another 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 three cases: (k140, k141), (k140, k142), and (k141, k142).

[0773] Referring to Figure 95, the standard deviation σDF_k140,k141 of the difference between the multiple integral values ​​(=8 integral values) on curve k140 and the multiple integral values ​​(=8 integral values) on curve k141 is 18.56 (%), the standard deviation σDF_k140,k142 of the difference between the multiple integral values ​​(=8 integral values) on curve k140 and the multiple integral values ​​(=8 integral values) on curve k142 is 59.01 (%), and the standard deviation σDF_k141,k142 of the difference between the multiple integral values ​​(=8 integral values) on curve k141 and the multiple integral values ​​(=8 integral values) on curve k142 is 48.44 (%).

[0774] As a result, the standard deviation σDF_k140, k141 (=18.56(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k140, k142 (=59.01(%)) is larger than the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k141, k142 (=48.44(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

[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 spectrums for WONDA, Craft Boss, and Gold Brew when the number of integrals is nine.

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

[0778] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 any 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 or not the two curves differ for three cases: (k143, k144), (k143, k145), and (k144, k145).

[0784] Referring to Figure 97, the standard deviation σDF_k143, k144 of the difference between the multiple integral values ​​(= 9 integral values) on curve k143 and the multiple integral values ​​(= 9 integral values) on curve k144 is 21.65 (%), the standard deviation σDF_k143, k145 of the difference between the multiple integral values ​​(= 9 integral values) on curve k143 and the multiple integral values ​​(= 9 integral values) on curve k145 is 70.36 (%), and the standard deviation σDF_k144, k145 of the difference between the multiple integral values ​​(= 9 integral values) on curve k144 and the multiple integral values ​​(= 9 integral values) on curve k145 is 65.19 (%).

[0785] As a result, the standard deviation σDF_k143, k144 (=21.65(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k143, k145 (=70.36(%)) is larger than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k144, k145 (=65.19(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

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

[0789] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 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 any two curves among the curves k146 to k148 are different for all combinations of two curves among 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 or not the two curves differ for three cases: (k146, k147), (k146, k148), and (k147, k148).

[0795] Referring to Figure 99, the standard deviation σDF_k146, k147 of the difference between the multiple integral values ​​(= 10 integral values) on curve k146 and the multiple integral values ​​(= 10 integral values) on curve k147 is 25.21 (%), the standard deviation σDF_k146, k148 of the difference between the multiple integral values ​​(= 10 integral values) on curve k146 and the multiple integral values ​​(= 10 integral values) on curve k148 is 84.29 (%), and the standard deviation σDF_k147, k148 of the difference between the multiple integral values ​​(= 10 integral values) on curve k147 and the multiple integral values ​​(= 10 integral values) on curve k148 is 78.52 (%).

[0796] As a result, the standard deviation σDF_k146, k147 (=25.21(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k146, k148 (=84.29(%)) is larger than the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k147, k148 (=78.52(%)) is larger than the threshold σ ｔｈ (=15(%)) is greater.

[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 integrated spectrum of coffee (WONDA), curve k150 represents the integrated spectrum of coffee (Craft Boss), and curve k151 represents the integrated spectrum of coffee (Gold Brew).

[0800] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 any two curves among the curves k149 to k151 are different for all combinations of two curves among 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 three cases: (k149, k150), (k149, k151), and (k150, k151).

[0806] Referring to FIG. 101, the standard deviation σDF_k149,k150 of the difference between the multiple integral values ​​(=15 integral values) on curve k149 and the multiple integral values ​​(=15 integral values) on curve k150 is 54.79(%), the standard deviation σDF_k149,k151 of the difference between the multiple integral values ​​(=15 integral values) on curve k149 and the multiple integral values ​​(=15 integral values) on curve k151 is 67.29(%), and the standard deviation σDF_k150,k151 of the difference between the multiple integral values ​​(=15 integral values) on curve k150 and the multiple integral values ​​(=15 integral values) on curve k151 is 48.38(%).

[0807] As a result, the standard deviation of the difference σDF_k149, k150 (=54.79(%)) is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k149, k151 (=67.29(%)) is larger than the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k150, k151 (=48.38(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

[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 curves k152 to k154 is a curve CUR created by the above-described method by analysis device 2. Curve k152 represents the integrated spectrum of coffee (WONDA), curve k153 represents the integrated spectrum of coffee (Craft Boss), and curve k154 represents the integrated spectrum of coffee (Gold Brew).

[0811] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=20 integral values) in each of curves k152 to k154 were the same as those shown in Table 5 above, except that the predetermined potential interval (=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 of 257.6 mV (=integral value extraction potential), and class 20 consists of a predetermined potential section of 92 mV (=integral value extraction potential).

[0813] FIG. 103 is a diagram showing the results of determining whether 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 any 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 or not the two curves differ for three cases: (k152, k153), (k152, k154), and (k153, k154).

[0817] Referring to Figure 103, the standard deviation σDF_k152, k153 of the difference between the multiple integral values ​​(= 20 integral values) on curve k152 and the multiple integral values ​​(= 20 integral values) on curve k153 is 34.35 (%), the standard deviation σDF_k152, k154 of the difference between the multiple integral values ​​(= 20 integral values) on curve k152 and the multiple integral values ​​(= 20 integral values) on curve k154 is 77.77 (%), and the standard deviation σDF_k153, k154 of the difference between the multiple integral values ​​(= 20 integral values) on curve k153 and the multiple integral values ​​(= 20 integral values) on curve k154 is 64.33 (%).

[0818] As a result, the standard deviation σDF_k152, k153 (=34.35(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k152, k154 (=77.77(%)) is larger than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k153, k154 (=64.33(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

[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 integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 39.

[0821] 104, each of curves k155 to k157 is a curve CUR created by the above-described method by analysis device 2. Curve k155 represents the integrated spectrum of coffee (WONDA), curve k156 represents the integrated spectrum of coffee (Craft Boss), and curve k157 represents the integrated spectrum of coffee (Gold Brew).

[0822] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=39 integral values) in each of curves k155 to k157 were the same as those shown in Table 5 above, except that the predetermined potential interval (=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 of 128.8 mV (=integral value extraction potential), and class 39 consists of a predetermined potential section of 92 mV (=integral value extraction potential).

[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 any 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 or not the two curves differ for three cases: (k155, k156), (k155, k157), and (k156, k157).

[0828] Referring to Figure 105, the standard deviation σDF_k155, k156 of the difference between the multiple integral values ​​(= 39 integral values) on curve k155 and the multiple integral values ​​(= 39 integral values) on curve k156 is 40.61 (%), the standard deviation σDF_k155, k157 of the difference between the multiple integral values ​​(= 39 integral values) on curve k155 and the multiple integral values ​​(= 39 integral values) on curve k157 is 67.02 (%), and the standard deviation σDF_k156, k157 of the difference between the multiple integral values ​​(= 39 integral values) on curve k156 and the multiple integral values ​​(= 39 integral values) on curve k157 is 50.55 (%).

[0829] As a result, the standard deviation σDF_k155, k156 (=40.61(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the difference σDF_k155, k157 (=67.02(%)) is larger than the threshold σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k156, k157 (=50.55(%)) is larger than the threshold σ ｔｈ (=15(%)) is greater.

[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 shows integral spectrums for WONDA, Craft Boss, and Gold Brew when the number of integrals is 54.

[0832] 106, each of curves k158 to k160 is a curve CUR created by the above-described method by analysis device 2. Curve k158 represents the integrated spectrum of coffee (WONDA), curve k159 represents the integrated spectrum of coffee (Craft Boss), and curve k160 represents the integrated spectrum of coffee (Gold Brew).

[0833] The measurement conditions of the cyclic voltammograms that were the basis for calculating the multiple integral values ​​(=54 integral values) in each of curves k158 to k160 were the same as those shown in Table 5 above, except that the predetermined potential interval (=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 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 curves among the curves k158 to k160 are different for all combinations of two curves among 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 or not the two curves differ for three cases: (k158, k159), (k158, k160), and (k159, k160).

[0839] Referring to Figure 107, the standard deviation σDF_k158,k159 of the difference between the multiple integral values ​​(=54 integral values) on curve k158 and the multiple integral values ​​(=54 integral values) on curve k159 is 37.13 (%), the standard deviation σDF_k158,k160 of the difference between the multiple integral values ​​(=54 integral values) on curve k158 and the multiple integral values ​​(=54 integral values) on curve k160 is 64.89 (%), and the standard deviation σDF_k159,k160 of the difference between the multiple integral values ​​(=54 integral values) on curve k159 and the multiple integral values ​​(=54 integral values) on curve k160 is 52.12 (%).

[0840] As a result, the standard deviation of the difference σDF_k158, k159 (=37.13(%)) is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k158, k160 (=64.89(%)) is larger than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the difference σDF_k159, k160 (=52.12(%)) is larger than the threshold σ ｔｈ (=15(%)) is greater.

[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 shows integral value spectra for WONDA, Craft Boss, and Gold Brew when the number of integral values ​​is 135.

[0843] 108, each of curves k161 to k163 is a curve CUR created by the above-described method by analysis device 2. Curve k161 represents the integrated spectrum of coffee (WONDA), curve k162 represents the integrated spectrum of coffee (Craft Boss), and curve k163 represents the integrated spectrum of coffee (Gold Brew).

[0844] The measurement conditions of the cyclic voltammograms that were the basis for calculating 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 predetermined potential interval (=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 the curves k161 to k163 shown in FIG. 108 are different from one another.

[0847] Whether or not the curves k161 to k163 differ from one another is determined by determining whether or not any two curves among the curves k161 to k163 differ from one another 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 or not the two curves differ for three cases: (k161, k162), (k161, k163), and (k162, k163).

[0850] Referring to Figure 109, the standard deviation σDF_k161, k162 of the difference between the multiple integral values ​​(= 135 integral values) on curve k161 and the multiple integral values ​​(= 135 integral values) on curve k162 is 37.87 (%), the standard deviation σDF_k161, k163 of the difference between the multiple integral values ​​(= 135 integral values) on curve k161 and the multiple integral values ​​(= 135 integral values) on curve k163 is 66.31 (%), and the standard deviation σDF_k162, k163 of the difference between the multiple integral values ​​(= 135 integral values) on curve k162 and the multiple integral values ​​(= 135 integral values) on curve k163 is 50.33 (%).

[0851] As a result, the standard deviation σDF_k161, k162 (=37.87(%)) of the difference is ｔｈ (=15(%)), and the standard deviation of the differences σDF_k161, k163 (=66.31(%)) is larger than the threshold value σ ｔｈ (=15(%)), and the standard deviation of the differences σDF_k162, k163 (=50.33(%)) is larger than the threshold value σ ｔｈ (=15(%)) is greater.

[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 deviations of the differences σDF_k11,k12 (=37.00(%)), σDF_k11,k13 (=66.20(%)), and σDF_k12,k13 (=50.55(%)) all being within the threshold value 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 spectrums (=curves CUR) that uniquely identify 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 spectrum for uniquely identifying coffee (WONDA), curve k141 is an integral spectrum for uniquely identifying coffee (Craft Boss), and curve k142 is an integral spectrum for uniquely identifying coffee (Gold Brew).

[0858] Furthermore, when the number of integral values ​​is nine, curve k143 is an integral spectrum for uniquely identifying coffee (WONDA), curve k144 is an integral spectrum for uniquely identifying coffee (Craft Boss), and curve k145 is an integral 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 integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k157 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0863] Furthermore, when the number of integral values ​​is 54, curve k158 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k159 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k160 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0864] Furthermore, when the number of integral values ​​is 135, curve k161 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k162 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k163 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0865] Furthermore, when the number of integral values ​​is 271, curve k11 is an integral value spectrum for uniquely identifying coffee (WONDA), curve k12 is an integral value spectrum for uniquely identifying coffee (Craft Boss), and curve k13 is an integral value spectrum for uniquely identifying coffee (Gold Brew).

[0866] In this case, the integral spectrum (=index curve) for uniquely identifying coffee (WONDA) consists of curve k134 when the number of integrals is 6, curve k137 when the number of integrals is 7, curve k140 when the number of integrals is 8, curve k143 when the number of integrals is 9, curve k146 when the number of integrals is 10, curve k149 when the number of integrals is 15, curve k152 when the number of integrals is 20, curve k155 when the number of integrals is 39, curve k158 when the number of integrals is 54, curve k161 when the number of integrals is 135, and curve k11 when the number of integrals is 271.

[0867] Furthermore, the integral spectrum (=index curve) for uniquely identifying coffee (Craft Boss) consists of a curve k135 when the number of integrals is 6, a curve k138 when the number of integrals is 7, a curve k141 when the number of integrals is 8, a curve k144 when the number of integrals is 9, a curve k147 when the number of integrals is 10, a curve k150 when the number of integrals is 15, a curve k153 when the number of integrals is 20, a curve k156 when the number of integrals is 39, a curve k159 when the number of integrals is 54, a curve k162 when the number of integrals is 135, and a curve k12 when the number of integrals is 271.

[0868] Furthermore, the integral spectrum (=index curve) for uniquely identifying coffee (Gold Brew) consists of a curve k136 when the number of integrals is 6, a curve k139 when the number of integrals is 7, a curve k142 when the number of integrals is 8, a curve k145 when the number of integrals is 9, a curve k148 when the number of integrals is 10, a curve k151 when the number of integrals is 15, a curve k154 when the number of integrals is 20, a curve k157 when the number of integrals is 39, a curve k160 when the number of integrals is 54, a curve k163 when the number of integrals is 135, and a curve k13 when the number of integrals is 271.

[0869] Therefore, in general, an integral spectrum (= index curve) for uniquely identifying coffee (WONDA) is made up of a plurality (= 11) of index curves (= curves k134, k137, k140, k143, k146, k149, k152, k155, k158, k161, k11) with mutually different numbers of integral values, and an integral spectrum (= index curve) for uniquely identifying coffee (Craft Boss) is made up of a plurality (= 11) of index curves (= curves k134, k137, k140, k143, k146, k149, k152, k155, k158, k161, k11) with mutually different numbers of integral values. The integral spectrum (= index curve) for uniquely identifying coffee (Gold Brew) is made up of multiple (= 11) index curves (= curves k136, k139, k142, k145, k148, k151, k154, k157, k160, k163, k13) with different numbers of integral values.

[0870] It was also found that the minimum number of integral values ​​required to obtain integral spectra that can uniquely identify each of coffee (WONDA), coffee (Craft Boss), and coffee (Gold Brew) is six.

[0871] When the number of integral values ​​is three, the standard deviation σ of the difference between the three integral values ​​on the curve k125 representing coffee (WONDA) and the three integral values ​​on the curve k127 representing coffee (Gold Brew) is ｋ１２５，ｋ１２７ is the threshold σ ｔｈ (=15(%)) (see FIG. 85), and when the number of integral values ​​is four, the standard deviation σ of the difference between the four integral values ​​on the curve k128 representing the coffee (WONDA) and the four integral values ​​on the curve k130 representing the coffee (Gold Brew) ｋ１２８，ｋ１３０ is the threshold σ ｔｈ (=15(%)) (see FIG. 87), and when the number of integral values ​​is five, the standard deviation σ of the difference between the five integral values ​​on the curve k131 representing the coffee (WONDA) and the five integral values ​​on the curve k133 representing the coffee (Gold Brew) is ｋ１３１，ｋ１３３ is the threshold σ ｔｈ(=15(%)) (see FIG. 89), and when the number of integral values ​​is six, the standard deviation σ of the difference between the six integral values ​​on the curve k134 representing coffee (WONDA) and the six integral values ​​on the curve k136 representing coffee (Gold Brew) is ｋ１３４，ｋ１３６ is the threshold σ ｔｈ (=15(%)) is 41.83(%) (see FIG. 91).

[0872] Furthermore, when the number of integral values ​​is seven, the standard deviation σ of the difference between the seven integral values ​​on the curve k137 representing coffee (WONDA) and the seven integral values ​​on the curve k139 representing coffee (Gold Brew) is ｋ１３７，ｋ１３９ is the threshold σ ｔｈ (=15(%)) (see FIG. 93), and when the number of integral values ​​is eight, the standard deviation σ of the difference between the eight integral values ​​on the curve k140 representing the coffee (WONDA) and the eight integral values ​​on the curve k142 representing the coffee (Gold Brew) is ｋ１４０，ｋ１４２ is the threshold σ ｔｈ (=15(%)) (see FIG. 95), and when the number of integral values ​​is nine, the standard deviation σ of the difference between the nine integral values ​​on the curve k143 representing the coffee (WONDA) and the nine integral values ​​on the curve k145 representing the coffee (Gold Brew) is ｋ１４３，ｋ１４５ is the threshold σ ｔｈ (=15(%)) (see FIG. 97), and when the number of integral values ​​is 10, the standard deviation σ of the difference between the 10 integral values ​​on the curve k146 representing coffee (WONDA) and the 10 integral values ​​on the curve k148 representing coffee (Gold Brew) is ｋ１４６，ｋ１４８ is the threshold σ ｔｈ (=15(%)) is 84.29(%) (see FIG. 99).

[0873] Furthermore, when the number of integral values ​​is 15, the standard deviation σ of the difference between the 15 integral values ​​on the curve k149 representing coffee (WONDA) and the 15 integral values ​​on the curve k151 representing coffee (Gold Brew) is ｋ１４９，ｋ１５１ is the threshold σ ｔｈ(=15(%)) (see FIG. 101), and when the number of integral values ​​is 20, the standard deviation σ of the difference between the 20 integral values ​​on the curve k152 representing the coffee (WONDA) and the 20 integral values ​​on the curve k154 representing the coffee (Gold Brew) is ｋ１５２，ｋ１５４ is the threshold σ ｔｈ (=15(%)) (see FIG. 103), and the number of integral values ​​is 39, the standard deviation σ of the difference between the 39 integral values ​​on the curve k155 representing the coffee (WONDA) and the 39 integral values ​​on the curve k157 representing the coffee (Gold Brew) is ｋ１５５，ｋ１５７ is the threshold σ ｔｈ (=15(%)) (see FIG. 105), and when the number of integral values ​​is 54, the standard deviation σ of the difference between the 54 integral values ​​on the curve k158 representing the coffee (WONDA) and the 54 integral values ​​on the curve k160 representing the coffee (Gold Brew) is ｋ１５８，ｋ１６０ is the threshold σ ｔｈ (=15(%)) is 64.89(%) (see FIG. 107).

[0874] Furthermore, when the number of integral values ​​is 135, the standard deviation σ of the difference between the 135 integral values ​​on the curve k161 representing the coffee (WONDA) and the 135 integral values ​​on the curve k163 representing the coffee (Gold Brew) is ｋ１６１，ｋ１６３ is the threshold σ ｔｈ (=15(%)) (see FIG. 109 ), and the number of integral values ​​is 271, the standard deviation σ of the difference between the 271 integral values ​​on the curve k11 representing the coffe...

Claims

1. A sensor used for measuring a cyclic voltammogram of an analysis object made of a liquid, which is attachable to and detachable from a measuring device that measures the cyclic voltammogram, and which is discarded each time the measurement of the cyclic voltammogram is performed, comprising: a substrate having a flat plate shape; a first wiring arranged on one side of the substrate along a first direction; a second wiring arranged on one side 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 side of the substrate along the first direction at a predetermined interval from the second wiring in the second direction; a working electrode arranged on one side of the substrate, electrically connected to one end of the first wiring, and which exchanges electrons with the analysis object; and a reference electrode arranged on one side of the substrate, electrically connected to one end of the second wiring, and which serves as a reference when determining the potential of the working electrode. a counter electrode which is an electrode arranged on one side of the substrate, electrically connected to one end of the third wiring, and for returning to a system a current value equal to the 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 counter electrode is arranged between the working electrode and the reference electrode in the second direction; and the working electrode has a circular or rectangular planar shape.

2. An analytical device that creates an index curve, which is a curve that serves as an index for identifying an analytical object consisting of a liquid, based on the current-potential characteristics of a cyclic voltammogram of the analytical object measured using a cyclic voltammetry method, comprising: a calculation unit that executes a calculation process to calculate an integral value in a predetermined potential interval of the current-potential characteristic based on the current-potential characteristics for all of the predetermined potential intervals to calculate a plurality of integral values ​​in the plurality of predetermined potential intervals; and a creation unit that creates, as the index curve, a curve that shows 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.

3. The analytical device according to claim 2, wherein the calculation unit, in the calculation process, calculates the area of ​​the cyclic voltammogram in one of the specified potential sections for all of the multiple specified potential sections to calculate the multiple integral values.

4. The analytical device described in claim 3, wherein the arithmetic unit, in the arithmetic processing, 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 the one specified potential section to calculate the intensity of the cyclic voltammogram at the one unit potential for all unit potentials in the one specified potential section to calculate a plurality of intensities in the one specified potential section, and calculates the sum of the calculated intensities as the area of ​​the cyclic voltammogram in the one specified potential section.

5. The analytical device described in claim 4, wherein the calculation unit, in the calculation process, subtracts the current value of the reduction wave from the current value of the oxidation wave of the cyclic voltammogram at the one unit potential in the one specified potential range to calculate the intensity of the cyclic voltammogram at the one unit potential.

6. The analytical device described in 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. The analytical device of claim 2, further comprising a judgment unit that judges whether or not the P [plurality of integral values] differ from each other when the calculation unit calculates the plurality of integral values ​​in the plurality of specified potential intervals for each of the P (P is an integer of 2 or greater) analysis objects in the calculation process, and the creation unit creates the P curves based on the P [plurality of integral values] when the judgment unit judges that the P [plurality of integral values] differ 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 ] (n represents the total number of the predetermined potential sections, and i ≠ j), 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 8. 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 The judgment unit is associated with 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 analysis objects have P names different from each other, and two of the P analysis objects having different names are designated 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 designated 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 terminal device comprising: a receiving unit that receives measurement data of a cyclic voltammogram of an object to be analyzed consisting of a liquid 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 multiple integral values ​​in multiple predetermined potential intervals of the cyclic voltammogram on the predetermined potential intervals, from an analyzing device via a network as an "index curve, which is a curve that serves as an index for identifying the object to be analyzed," a transmitting unit that transmits analysis data including the current-potential characteristics of the measurement data received by the receiving unit to the analyzing device via the network, and a display unit that displays the curve received by the receiving unit as the index curve.

13. An analysis system comprising: a sensor device comprising the sensor according to claim 1 and a measuring instrument that uses said sensor to measure a cyclic voltammogram of an analysis object consisting of a liquid; and an analysis device according to any one of claims 2 to 11.

14. An analysis system comprising: a sensor device comprising the sensor according to claim 1 and a measuring instrument that uses said sensor to measure a cyclic voltammogram of an analysis object consisting of a liquid; an analysis device according to any one of claims 2 to 11; and a 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 when identifying an analysis object consisting of a liquid, based on the current-potential characteristics of a cyclic voltammogram of the analysis object measured using a cyclic voltammetry method, comprising: a first step in which an arithmetic unit executes an arithmetic process to calculate a plurality of integral values ​​in a predetermined potential interval of the current-potential characteristics based on the current-potential characteristics, for all of the predetermined potential intervals, thereby calculating a plurality of integral values ​​in the plurality of predetermined potential intervals; and a second step in which a 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 in the arithmetic process of the first step.

16. A program for causing a computer to execute the program described in claim 15, wherein the calculation unit, in the calculation processing of the first step, calculates the area of ​​the cyclic voltammogram in one of the specified potential sections for all of the multiple specified potential sections to calculate the multiple integral values.

17. A program for causing a computer to execute the program described in 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 specified potential section to calculate an intensity of the cyclic voltammogram at the one unit potential for all unit potentials in the one specified potential section to calculate a plurality of intensities in the one specified potential section, and calculates the sum of the calculated intensities as the area of ​​the cyclic voltammogram in the one specified potential section.

18. A program to be executed by a computer as described in claim 17, wherein the calculation unit, in the calculation processing of the first step, calculates the intensity of the cyclic voltammogram at the one unit potential in the one specified potential section by subtracting the current value of the reduction wave from the current value of the oxidation wave of the cyclic voltammogram at the one unit potential.

19. A program for causing a computer to execute the program described in claim 18, wherein the calculation 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 in the calculation processing of the first step.

20. A program for causing a computer to execute the program described in claim 15, wherein the judgment unit further causes the computer to execute a third step of judging whether or not the P [plurality of integral values] differ from each other when the calculation unit calculates the plurality of integral values ​​in the plurality of specified potential intervals for each of the P (P is an integer of 2 or more) analysis objects in the calculation processing of the first step, and the creation unit creates P of the curves based on the P [plurality of integral values] in the second step when the judgment unit determines in the third step that the P [plurality of integral values] differ from each other.

21. The number of combinations of the two [several integral values] when extracting two [several integral values] from the P [several integral values] (Z is the number of combinations of the two [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 ] (n represents the total number of the predetermined potential sections, and i ≠ j), the judgment unit, in the third step, 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 In the third step, the determination unit determines 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 22. The program executed by a computer according to claim 21, wherein the program determines that the two are different.

23. The P analysis objects have P names different from each other, and two of the P analysis objects having different names are designated 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 designated 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. 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.

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