JP2022055690A - Food and drink evaluation system and food and drink evaluation program - Google Patents

Abstract

To provide a food and drink evaluation system which can clearly show a sense felt for food and drink.SOLUTION: A food and drink evaluation system comprises a first obtaining unit 10, a second obtaining unit 15, and a graph creating unit 16. The first obtaining unit 10 obtains specific information which specifies target food and drink. The second obtaining unit 15 obtains estimation information based on the specific information. The estimation information includes a first evaluation value regarding taste of the target food and drink, and a second evaluation value regarding at least one of a carbonate degree and flavor of the target food and drink. The graph creating unit 16 creates a graph showing the estimation information. The graph includes a radar chart 21 showing the first evaluation value, and sub charts 22 and 24 showing the second evaluation value around the radar chart 21.SELECTED DRAWING: Figure 1

Description

The present invention relates to a food and drink evaluation system and a food and drink evaluation program.

A taste sensor that estimates the taste of food and drink is known. For example, Patent Document 1 discloses analysis of the taste of food and drink using a taste sensor. In Patent Document 1, the evaluation of the taste of food and drink is shown by a radar chart.

Japanese Unexamined Patent Publication No. 2017-130142

There is a need for potential customers to know the evaluation of the sensation they feel when eating and drinking. For example, if the evaluation of the taste of food and drink is shown by a graph, a potential customer can guess the taste that is felt when eating and drinking food and drink, and decide whether or not to purchase. In this respect, the sensation felt for food and drink is not limited to taste. For example, as a sensation felt for food and drink, there are texture, throat, and aroma in addition to taste. For this reason, the needs of more potential customers can be met if an evaluation of sensations other than taste is shown.

In Patent Document 1, the evaluation of the taste of food and drink is shown by a radar chart. By increasing the number of items on the radar chart, sensations other than taste can also be shown by the radar chart. However, as the number of radar chart items increases, it is difficult for potential customers to recognize which sensory evaluation is shown where. For this reason, it is difficult to show potential customers' purchasing motivation by showing sensations other than taste by radar charts. If the evaluation of taste and the evaluation of non-taste sensations are mixed, it is difficult for potential customers to recognize the evaluation of taste.

One aspect of the present invention is to provide a food and drink evaluation system capable of clearly showing the sensation felt for food and drink. Another aspect of the present invention is to provide a dietary evaluation program in which the sensation felt for food and drink can be clearly shown.

The food and drink evaluation system according to one aspect of the present invention includes a first acquisition unit, a second acquisition unit, and a graph creation unit. The first acquisition unit acquires specific information that identifies the target food and drink. The second acquisition unit acquires estimated information based on the specific information. The estimation information includes a first evaluation value regarding the taste of the target food and drink and a second evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink. The graph creation unit creates a graph showing estimation information. The graph includes a radar chart showing the first evaluation value and a subchart showing the second evaluation value around the radar chart.

In this food and drink evaluation system, the first evaluation value and the second evaluation value are acquired based on the specific information that identifies the target food and drink. Therefore, not only the evaluation value regarding the taste of the target food and drink, but also the evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink is acquired. The evaluation value for taste is shown by a radar chart, and the evaluation value for at least one of the carbonation and aroma of the target food and drink is shown by a subchart. The evaluation values related to taste are collectively shown in the radar chart. Therefore, according to this food and drink evaluation system, the sensation felt for food and drink can be clearly shown.

In one of the above embodiments, the food and beverage evaluation system may further include an estimation unit. The estimation unit estimates at least one of the first evaluation value and the second evaluation value based on the specific information. The specific information is component data including information on the content of each of the plurality of components for the target food and drink. The second acquisition unit may acquire the first evaluation value and the second evaluation value estimated by the estimation unit as estimation information. In this case, since at least one of the first evaluation value and the second evaluation value is estimated based on the component data, more accurate information can be shown.

In one of the above embodiments, the graph may include a frame surrounding the radar chart. The subchart may show a second evaluation value depending on the degree to which the specific area occupies the inside of the frame. In this case, the ease of recognition of the estimated information is ensured while the first evaluation value and the second evaluation value are shown together and well.

In one of the above embodiments, the subchart may indicate a second evaluation value by the length of a bar extending so as to surround the radar chart from a position below the radar chart. In this case, the ease of recognition of the estimated information is ensured while the first evaluation value and the second evaluation value are shown together and well.

In one of the above embodiments, the second evaluation value may include an evaluation value relating to a first-class scent and an evaluation value relating to a second-class scent. The sub-chart may indicate the evaluation value for the first-class fragrance by the length of a bar extending to the left side of the radar chart so as to surround the radar chart from a position below the radar chart. The subchart may indicate the evaluation value for the second kind of fragrance by the length of the bar extending to the right side of the radar chart so as to surround the radar chart from a position below the radar chart. In this case, evaluation values for two types of scents may be shown. While the first evaluation value and the evaluation value related to the scent are well shown, the ease of recognition of the first evaluation value and the evaluation value related to the scent is ensured.

In one of the above embodiments, the graph creating unit has at least one of an evaluation value regarding the taste of a plurality of types of food and drink, an evaluation value regarding the carbonic acid content of the plurality of types of food and drink, and an evaluation value regarding the aroma of the plurality of types of food and drink. You may acquire the reference data based on the above. The graph may show the estimated information based on the value comparing the reference data and the estimated information. In this case, the sensation felt for food and drink can be shown more clearly.

In one of the above embodiments, the reference data may include at least one of the first reference data, the second reference data, and the third reference data. The first criterion data may include the minimum value and the maximum value of the evaluation values relating to the taste of a plurality of types of foods and drinks. The second reference data may include the minimum value and the maximum value of the evaluation values regarding the carbonic acid content of a plurality of types of foods and drinks. The third criterion data may include the minimum value and the maximum value of the evaluation values relating to the aroma of a plurality of kinds of foods and drinks. In the graph, at least one of the evaluation value regarding the taste of the target food and drink, the evaluation value regarding the carbon dioxide content of the target food and drink, and the evaluation value regarding the aroma of the target food and drink is included in the minimum standard data. It may be indicated by a relative evaluation with a lower limit of the value and an upper limit of the maximum value included in the reference data. In this case, the sensation felt for food and drink can be shown more clearly.

In one of the above embodiments, the food or drink may be an alcoholic beverage.

In one of the above embodiments, an output unit for displaying a graph created by the graph creation unit may be further provided.

The dietary food evaluation program in another aspect of the present invention acquires specific information that identifies the target food or drink, acquires estimation information based on the specific information, and creates a graph showing the estimation information. And let the computer do it. The estimation information includes a first evaluation value regarding the taste of the target food and drink and a second evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink. The graph includes a radar chart showing the first evaluation value and a subchart showing the second evaluation value around the radar chart.

In this food and drink evaluation program, the first evaluation value and the second evaluation value are acquired based on the specific information that identifies the target food and drink. Therefore, not only the evaluation value regarding the taste of the target food and drink, but also the evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink is acquired. The evaluation value for taste is shown by a radar chart, and the evaluation value for at least one of the carbonation and aroma of the target food and drink is shown by a subchart. The evaluation values related to taste are collectively shown in the radar chart. Therefore, according to this food and drink evaluation program, the sensation felt for food and drink can be clearly shown.

In another aspect described above, the computer may further perform estimation of at least one of the first evaluation value and the second evaluation value based on the specific information. The specific information may be component data including information on the content of each of the plurality of components in the target food and drink. In this case, since at least one of the first evaluation value and the second evaluation value is estimated based on the component data, more accurate information can be shown.

In another aspect described above, the graph may include a frame surrounding a radar chart. The subchart may show a second evaluation value depending on the degree to which the specific area occupies the inside of the frame.

In another aspect described above, the subchart may indicate a second evaluation value by the length of a bar extending so as to surround the radar chart from a position below the radar chart. In this case, the ease of recognition of the estimated information is ensured while the first evaluation value and the second evaluation value are shown together and well.

In another aspect described above, the second evaluation value may include an evaluation value relating to a first-class scent and an evaluation value relating to a second-class scent. The sub-chart may indicate the evaluation value for the first-class fragrance by the length of a bar extending to the left side of the radar chart so as to surround the radar chart from a position below the radar chart. The subchart may indicate the evaluation value for the second kind of fragrance by the length of the bar extending to the right side of the radar chart so as to surround the radar chart from a position below the radar chart. In this case, evaluation values for two types of scents may be shown. While the first evaluation value and the evaluation value related to the scent are well shown, the ease of recognition of the first evaluation value and the evaluation value related to the scent is ensured.

In another aspect described above, reference data based on at least one of an evaluation value relating to the taste of a plurality of types of food and drink, an evaluation value relating to the carbon dioxide content of the plurality of types of food and drink, and an evaluation value relating to the aroma of the plurality of types of food and drink. You may also have the computer perform the acquisition of. The graph may show the estimated information based on the value comparing the reference data and the estimated information. In this case, the sensation felt for food and drink can be shown more clearly.

In another aspect described above, the reference data may include at least one of the first reference data, the second reference data, and the third reference data. The first criterion data may include the minimum value and the maximum value of the evaluation values relating to the taste of a plurality of types of foods and drinks. The second reference data may include the minimum value and the maximum value of the evaluation values regarding the carbonic acid content of a plurality of types of foods and drinks. The third criterion data may include the minimum value and the maximum value of the evaluation values relating to the aroma of a plurality of kinds of foods and drinks. In the graph, at least one of the evaluation value regarding the taste of the target food and drink, the evaluation value regarding the carbon dioxide content of the target food and drink, and the evaluation value regarding the aroma of the target food and drink is included in the minimum standard data. It may be indicated by a relative evaluation with a lower limit of the value and an upper limit of the maximum value included in the reference data. In this case, the sensation felt for food and drink can be shown more clearly.

In another aspect described above, the food or drink may be an alcoholic beverage.

In another aspect described above, the computer may further execute displaying the created graph on the display unit.

One aspect of the present invention provides a food and drink evaluation system capable of clearly showing the sensation felt for food and drink. Another aspect of the present invention provides a dietary evaluation program in which the sensation felt for food and drink can be clearly shown.

It is a block diagram of the food and drink evaluation system in this embodiment. It is a figure which shows an example of the graph created in the food and drink evaluation system. It is a figure which shows an example of the graph created in the food and drink evaluation system of the modification of this embodiment. It is a block diagram of an estimation model creation system. It is a figure for demonstrating the determination method of an estimation model. It is a figure which shows an example of the hardware composition of the food and drink evaluation system and the estimation model creation system. It is a flowchart which shows the creation method of an estimation model. It is a flowchart which shows the estimation method using an estimation model.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same reference numerals are used for the same or equivalent elements, and duplicate description is omitted.

First, with reference to FIG. 1, the function and configuration of the food and drink evaluation system in the present embodiment will be described. FIG. 1 is a block diagram of a food and drink evaluation system. The food and drink evaluation system 1 evaluates the target food and drink. The food and drink evaluation system 1 acquires component data of the target food and drink as an evaluation of the target food and drink, and estimates a plurality of types of sensations felt for the target food and drink based on the acquired component data. .. The plurality of sensations felt for food and drink include the taste, vision, texture, and aroma felt for food and drink. The sensations felt for food and drink include not only the sensations felt when eating and drinking food and drink, but also the visual sense and aroma felt before eating and drinking food and drink. The component data includes the content of each of the plurality of components contained in the food and drink.

The target food and drink is, for example, a beverage. Beverages include, for example, alcoholic beverages. Examples of alcoholic beverages include brewed liquors such as wine and sake, and distilled liquors such as whiskey and shochu. By evaluating the food and drink in this embodiment, the compatibility between the alcoholic beverage and the food can be determined. In the evaluation of food and drink in the present embodiment, the target food and drink is more preferably brewed liquor, which is often drunk during meals.

The food and drink evaluation system 1 includes a specific information acquisition unit 10, a component data acquisition unit 11, a storage unit 12, an estimation model acquisition unit 13, an evaluation value estimation unit 14, an estimation information acquisition unit 15, and a graph creation unit. A 16 and an output unit 17 are provided. Based on these, the food and drink evaluation system 1 estimates a plurality of types of sensations felt for the target food and drink. For example, the food and drink evaluation system 1 estimates information on taste and information on at least one of carbonic acidity and aroma as information on a plurality of types of sensations felt for the target food and drink. Hereinafter, the sensation felt for food and drink is also simply referred to as "sense". Hereinafter, the information estimated by the food and drink evaluation system 1 is referred to as "estimated information". In the present embodiment, the estimation information includes information on a plurality of tastes, a carbonation degree, and a plurality of aromas. In the present embodiment, the food and drink evaluation system 1 estimates a plurality of tastes felt when eating and drinking the target food and drink, and at least one of the carbonic acid content and the aroma of the target food and drink. The plurality of tastes included in the estimation information include, for example, saltiness, acidity, sweetness, umami, and bitterness. Hereinafter, a plurality of tastes of saltiness, acidity, sweetness, umami, and bitterness are collectively referred to as Gomi. The food and beverage evaluation system creates a graph showing the estimated estimated information.

The specific information acquisition unit 10 acquires specific information that identifies the target food or drink. The specific information is, for example, the name of the target food or drink. The specific information may be an identification number or a product number indicating the target food or drink. The specific information may be image information indicating the target food or drink. The specific information acquisition unit 10 may acquire the specific information directly input from the user, or may acquire the specific information output from another device via the network.

The component data acquisition unit 11 acquires component data for the target food and drink. The ingredient data contains information about the content of each of the plurality of ingredients contained in the food and drink. The component data acquisition unit 11 acquires component data of the target food or drink based on the specific information acquired by the specific information acquisition unit 10. For example, the component data acquisition unit 11 acquires component data associated with the specific information in advance from the storage unit 12. The component data acquisition unit 11 may acquire component data from the outside of the food and drink evaluation system 1, or may acquire the measurement result in the component data acquisition unit 11 as component data. The component data acquisition unit 11 may also serve as the specific information acquisition unit 10. For example, the component data acquired by the component data acquisition unit 11 may be specific information for specifying the target food or drink.

In the component data acquired by the component data acquisition unit 11, the plurality of components included in the component data are, for example, all the components considered to be contained in all foods and drinks. The component data acquisition unit 11 excludes, for example, from all the components considered to be contained in all the foods and drinks, the components which are not related to or have a low relationship with the sensation felt for the target food and drink. Component data related to the components may be acquired. The component data acquired by the component data acquisition unit 11 is component data of a measurable component, and is, for example, component data that can be measured based on various types of chromatography.

The various chromatographies include, for example, at least one of ion chromatography, liquid chromatography, gas chromatography mass spectrometry using derivatization, and headspace gas chromatography mass spectrometry. By ion chromatography, the content of organic acids can be mainly measured. Liquid chromatography can mainly measure the content of amino acids and sugar components. According to the gas chromatography mass spectrometry method using derivatization, the contents of mainly organic acids, amino acids, and sugar components can be measured. The component data may include information about the scent component. According to the headspace gas chromatography mass spectrometry method, the content of the scent component can be mainly measured. For example, in ion chromatography, it is possible to separate ionic components and detect the amount of each ionic component due to differences in properties such as ionic valence, ionic radius, and hydrophobicity. As a result, the amount of the component is measured for each of the plurality of kinds of organic acids.

The component data acquired by the component data acquisition unit 11 may include information on the carbonic acid content of the target food or drink. The degree of carbonic acid is, for example, the content of CO ₂ . As a modification of the present embodiment, the carbonic acid content may be a gas content other than CO ₂ or a bubble size, and both the gas content and the bubble size may be obtained. It may be an index value related to.

For example, a CO ₂ measuring device is used to obtain the degree of carbon dioxide. For example, the CO ₂ measuring instrument is VAISALA GMT222 from Vaisala Corporation. The carbonic acid content is measured, for example, in a state where a CO ₂ measuring device is placed in a cup containing food and drink. For example, the CO ₂ measuring device is placed inside the cup so as not to touch food and drink. For example, with the CO ₂ measuring instrument placed inside the cup, the cup is covered with a lid such as a wrap. After waiting for a predetermined time in this state, the amount of CO ₂ output from the CO ₂ measuring machine is acquired as the degree of carbon dioxide. The predetermined time to wait is, for example, 180 seconds.

The storage unit 12 stores various information in advance, and stores information from various functional units. The storage unit 12 stores, for example, the component data acquired by the component data acquisition unit 11. The storage unit 12 may store the component data acquired by the component data acquisition unit 11 in advance. The storage unit 12 stores a plurality of estimation models corresponding to a plurality of different types of sensations. The estimation model includes component data for each of the multiple types of food and drink, provisional data that quantitatively indicates each of the multiple types of sensations felt for each of the multiple types of food and drink, and each sensation and each component. It is created based on the correlation coefficient. The tentative data includes, for example, data that quantitatively indicates each of the plurality of types of taste for each of the plurality of types of food and drink, data that quantitatively indicates the carbon dioxide degree for each of the plurality of types of food and drink, and data of a plurality of types of food and drink. It contains at least one with data that quantitatively indicates the aroma for each of the objects.

The estimation model acquisition unit 13 acquires an estimation model used for determining the sensation felt for the target food and drink. The estimation model acquisition unit 13 acquires a plurality of estimation models corresponding to a plurality of different types of sensations. In other words, there is a one-to-one relationship between the estimation model acquired by the estimation model acquisition unit 13 and the sensation estimated by the food and drink evaluation system 1. The estimation model acquisition unit 13 acquires, for example, the same number of estimation models as the number of sensation types estimated by the food and drink evaluation system 1. The estimation model acquisition unit 13 acquires, for example, five estimation models corresponding to each of the five tastes and two estimation models corresponding to each of the carbonic acid degree and the scent. The estimation model outputs an evaluation value regarding the sensation felt for the target diet in response to input of information regarding the content of each of the plurality of components in the target diet.

The evaluation value includes, for example, a taste value and at least one of a carbonation degree and an aroma value. For example, the evaluation value regarding taste includes the taste value, the evaluation value regarding carbonic acid content includes the carbonic acid degree, and the evaluation value regarding fragrance includes the fragrance value. The taste value is, for example, a numerical value indicating the degree of taste of the subject. The degree of carbonic acid is, for example, a numerical value indicating the degree of foaming during eating and drinking. The scent value is, for example, a numerical value indicating the degree of intensity of the scent of the target. The estimation model acquisition unit 13 may acquire an estimation model from the outside of the food and drink evaluation system 1, or may acquire an estimation model stored in advance in the storage unit 12.

The evaluation value estimation unit 14 determines the evaluation value regarding the sensation felt for the target food and drink based on the component data acquired by the component data acquisition unit 11 and the estimation model acquired by the estimation model acquisition unit 13. presume. The evaluation value estimation unit 14 has a feeling corresponding to each estimation model for the target food and drink based on the component data acquired by the component data acquisition unit 11 and the plurality of estimation models acquired by the estimation model acquisition unit 13. Estimate the evaluation value for. For example, the evaluation value estimation unit 14 uses a plurality of estimation models to evaluate the food and drink of the target, such as the taste values of a plurality of types of taste in the food and drink of the target, the carbonic acid content, and the aroma values of the plurality of scents. And presume. In the present embodiment, the evaluation value estimation unit 14 obtains the taste value of each taste in the target food or drink from the estimation model corresponding to each of the plurality of tastes, the carbonic acid content, and the aroma values of the plurality of aromas. , Carbonation degree and scent value of each scent are output. The evaluation value estimation unit 14 outputs, for example, an evaluation value indicating the taste value of each of the five tastes, the carbonic acid degree, and the two types of scent values by grade evaluation. The two types of scent values are, for example, the scent value of apple scent and the scent value of banana scent. When the apple scent is the first kind scent, the banana scent corresponds to the second kind scent.

The evaluation value estimation unit 14 outputs the evaluation value estimated by the estimation model to at least one of the storage unit 12, the estimation information acquisition unit 15, and the output unit 17. When the taste value estimated by the evaluation value estimation unit 14 is the first evaluation value, at least one of the carbonic acid degree and the aroma value estimated by the evaluation value estimation unit 14 corresponds to the second evaluation value.

The estimation information acquisition unit 15 acquires estimation information including an evaluation value regarding the taste of the target food and drink and an evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink. The estimation information acquisition unit 15 acquires estimation information based on the specific information acquired by the specific information acquisition unit 10. In the present embodiment, the estimation information acquisition unit 15 acquires the estimation information estimated by the evaluation value estimation unit 14 by using the component data acquired based on the specific information and the estimation model. The estimation information acquisition unit 15 acquires estimation information from the evaluation value estimation unit 14. The estimation information acquisition unit 15 may acquire estimation information from the storage unit 12 based on the specific information. The estimation information acquisition unit 15 may acquire estimation information from the outside of the food and drink evaluation system 1 based on the specific information. The estimation information acquisition unit 15 outputs the acquired information to the graph creation unit 16.

The graph creating unit 16 creates a graph showing an evaluation value regarding the taste of the target food and drink and an evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink based on the estimated information. Creating a graph involves, for example, creating data to display the graph. The graph created by the graph creating unit 16 includes a radar chart showing evaluation values related to the taste of the target food and drink, and a subchart showing evaluation values other than the taste of the target food and drink around the radar chart. .. The subchart shows, for example, an evaluation value for at least one of the carbonation and aroma of the target food and drink. Subcharts include various charts such as ring graphs and bar graphs. "Indicating an evaluation value" includes not only displaying the evaluation value itself but also displaying information about the evaluation value. The graph creation unit 16 outputs the created graph to at least one of the storage unit 12 and the output unit 17.

The graph creation unit 16 acquires, for example, reference data, and creates a graph showing estimated information based on a value comparing the reference data and the estimated information. The graph creation unit 16 acquires, for example, reference data from the storage unit 12. The reference data is based on, for example, at least one of an evaluation value regarding the taste of a plurality of types of food and drink, an evaluation value regarding the carbonic acid content of the plurality of types of food and drink, and an evaluation value regarding the aroma of the plurality of types of food and drink. The reference data includes at least one of the first reference data, the second reference data, and the third reference data. The first criterion data includes at least one of the minimum value and the maximum value of the evaluation values relating to the taste of a plurality of kinds of foods and drinks. The second criterion data includes at least one of the minimum value and the maximum value of the evaluation values regarding the carbonic acid content of a plurality of kinds of foods and drinks. The third criterion data includes at least one of the minimum value and the maximum value of the evaluation values regarding the aromas of a plurality of kinds of foods and drinks.

The graph creating unit 16 shows, for example, a graph showing the evaluation value regarding the feeling of the target food or drink by a relative evaluation with the minimum value included in the reference data as the lower limit and the maximum value included in the reference data as the upper limit. To create. In this graph, for example, evaluation values relating to a plurality of tastes of a target food or drink are shown by relative evaluation with the minimum value in the first reference data as the lower limit and the maximum value in the first reference data as the upper limit. In this graph, for example, the evaluation value regarding the carbonic acid content of the target food or drink is shown by a relative evaluation with the minimum value in the second reference data as the lower limit and the maximum value in the second reference data as the upper limit. In this graph, for example, the evaluation value regarding the aroma of the target food or drink is shown by a relative evaluation with the minimum value in the third reference data as the lower limit and the maximum value in the third reference data as the upper limit. The graph creation unit 16 creates a graph showing the evaluation value regarding the sensation of the target food and drink by a relative evaluation based on only one of the minimum value contained in the reference data and the maximum value contained in the reference data. You may.

For example, the graph creation unit 16 creates a graph showing the evaluation values of the salty taste of the target food or drink by relative evaluation with respect to the reference data. In this case, the first reference data includes, for example, a minimum value and a maximum value among a plurality of numerical values indicating the degree of saltiness in each of a plurality of types of foods and drinks. For example, the graph creating unit 16 sets the minimum value of the first standard data regarding salt taste to 0 at the lower limit of the graph, sets the maximum value of the first standard data regarding salt taste to 9 at the upper limit of the graph, and evaluates the salt taste of the target food or drink. Create a graph showing the values by 11-step relative evaluation from 0 to 10. In this case, for example, the graph creating unit 16 uses the formula {10 / (maximum value in the reference data-minimum value in the reference data)} × (evaluation value-minimum value in the reference data) to eat and drink the target food and drink. Convert the evaluation value related to saltiness to a relative value.

For example, it is assumed that the minimum value of the first standard data regarding salt taste is 2, the maximum value of the first standard data regarding salt taste is 10, and the evaluation value regarding salt taste of the target food and drink is 5. In this case, the graph creating unit 16 creates a graph showing that the relative value of the evaluation value regarding the salty taste of the target food or drink is 3.75 out of 0 to 10.

The output unit 17 outputs the information estimated by the evaluation value estimation unit 14 for the target food and drink. The output unit 17 outputs, for example, evaluation data including estimation information and at least one of the graphs created by the graph creation unit 16. The output unit 17 includes, for example, a display unit and displays a graph created by the graph creation unit 16.

FIG. 2 shows an example of a graph created by the graph creating unit 16. The output unit 17 displays, for example, the graph 20 shown in FIG. 2 on the display unit. The graph 20 shown in FIG. 2 includes a radar chart 21, two types of subcharts 22, 24, and a frame 25. The graph creation unit 16 creates data for displaying the radar chart 21. The graph creation unit 16 creates data for displaying at least one of the two types of subcharts 22 and 24. The graph creation unit 16 creates data for displaying the frame 25.

The radar chart 21 shows, for example, the taste value of each taste of the target food or drink. The graph creation unit 16 determines a position corresponding to the taste value of each taste with reference to the center point 26 of the graph 20. The positions corresponding to the taste values of the plurality of types of tastes are located on the plurality of straight lines 27 extending radially from the center point 26, respectively. The position corresponding to the taste value of each taste is arranged on the corresponding straight line 27. The graph creation unit 16 shows the radar chart 21 by a polygon whose apex is a position corresponding to the taste value of each determined taste. For example, when the evaluation value estimation unit 14 estimates the taste values of the five tastes, the graph creation unit 16 creates a radar chart 21 showing the taste values of each taste as a pentagon. The graph creation unit 16 calculates the relative value of each taste based on the taste value of each taste and the first reference data, and creates a radar chart 21 showing the calculated relative value.

The radar chart 21 is surrounded by a frame 25. The frame 25 has a circular shape centered on the center point 26. Each straight line 27 extends from the center point 26 to the frame 25. The frame 25 may have a polygonal shape corresponding to the shape of the radar chart 21. For example, when the taste value of Gomi is estimated by the evaluation value estimation unit 14, the graph creation unit 16 may create a regular pentagonal frame 25. In this case, each of the plurality of straight lines 27 may extend to the apex of the corresponding frame 25. The plurality of straight lines 27 correspond to different tastes from each other.

In the radar chart 21, the relative values of each taste are plotted along the corresponding straight line 27. At the position where the relative value of each taste is plotted, the vertices of the polygon indicating each taste are displayed. Therefore, in the radar chart 21, the vertices of the polygon indicating each taste are displayed along the corresponding straight line 27. In the radar chart 21, the vertices of the polygon indicating each taste are displayed closer to the center point 26 as the relative value of each taste is smaller, and closer to the frame 25 as the relative value of each taste is larger.

Subcharts 22 and 24 show, for example, evaluation values regarding the carbonic acidity and aroma of the target food and drink. The subchart 22 shows the evaluation value according to the degree to which the specific region α occupies the inside of the frame 25. The specific region α is defined by, for example, a straight line 23 extending inside the frame 25 and the frame 25. The specific region α is shown, for example, on the side of the lowest point of the frame 25. The area occupied by the specific region α from the lowest point of the frame 25 changes according to the indicated evaluation value. For example, the larger the evaluation value, the larger the area occupied by the specific region α from the lowest point of the frame 25. The straight line 23 extends horizontally, for example. The height of the straight line 23 from the lowest point of the frame 25 changes according to the evaluation value shown. For example, the larger the evaluation value, the larger the height of the straight line 23 from the lowest point of the frame 25.

In the present embodiment, the subchart 22 shows the evaluation value regarding the carbonic acid content of the target food and drink. The graph creation unit 16 calculates the relative value of the carbonic acid degree based on, for example, the carbonic acid degree and the second reference data, and creates a subchart 22 showing the calculated relative value. In the subchart 22, the relative value of carbonation corresponds to the height of the straight line 23 from the lowest point of the frame 25. The specific region α is displayed so that the straight line 23 is located at the height corresponding to the relative value of the carbonic acid degree. The smaller the relative value of carbonic acid, the smaller the straight line 23 is displayed near the lowest point of the frame 25, and the smaller the area of the specific region α is. The larger the relative value of the carbonic acid degree, the closer the straight line 23 is displayed near the uppermost point of the frame 25, and the larger the area of the specific region α is.

In FIG. 2, the specific region α is shown only outside the radar chart 21. The specific region α may be shown in a part of the inside of the radar chart 21 so as to correspond to the straight line 23. In FIG. 2, the specific region α is shown by hatching. The specific region α may be a region colored with a color different from other parts inside the frame 25. The specific region α may be represented by a pattern related to carbonation or scent. The specific region α may be represented by a pattern showing a plurality of bubbles, for example, so as to resemble carbonic acid.

The sub-chart 24 indicates an evaluation value by the lengths of the bars 24a and 24b extending so as to surround the radar chart 21 from a position below the radar chart 21. The bars 24a and 24b extend on an arc centered on the center point 26. In the present embodiment, the subchart 24 shows the evaluation value regarding the aroma of the target food and drink. The subchart 24 shows, for example, two types of scents by bars 24a and 24b, respectively. The bar 24a and the bar 24b are connected at a position below the radar chart 21. The boundary between the bar 24a and the bar 24b extends on a straight line passing through the lowest point of the frame 25. The bar 24a extends from a position below the radar chart 21 to the left side of the radar chart 21 so as to surround the radar chart 21. The bar 24b extends from a position below the radar chart 21 to the right side of the radar chart 21 so as to surround the radar chart 21. In FIG. 2, the bars 24a and 24b are shown by different hatching. The bars 24a and 24b may be colored with different colors.

In the subchart 24, for example, the evaluation value regarding the apple scent is indicated by the length of the bar 24a, and the evaluation value regarding the banana scent is indicated by the length of the bar 24b. The lengths of the bars 24a and 24b vary depending on the evaluation values shown. For example, the larger the evaluation value, the longer the lengths of the bars 24a and 24b. For example, the graph creation unit 16 calculates the relative value of the scent based on the scent value of each scent and the third reference data, and creates a subchart 24 showing the calculated relative value. In the subchart 24, the relative value of the scent corresponds to the length of the bars 24a, 24b. The smaller the relative value of the scent, the shorter the length of the bars 24a and 24b. The larger the relative value of the scent, the longer the bars 24a and 24b.

FIG. 3 is a diagram showing an example of a graph created by the graph creating unit 16 in the modified example of the present embodiment. The graph 20A shown in FIG. 3 includes a radar chart 21, two types of subcharts 22, 29, and a frame 25. This modification differs from the above-described embodiment in that the subchart 29 is used instead of the subchart 24.

The subchart 29 shows, for example, an evaluation value regarding the carbonic acidity and aroma of the target food and drink. In this modification, the subchart 29 shows the evaluation value regarding the aroma of the target food and drink. The subchart 29 shows the evaluation value by the lengths of the bars 29a and 29b occupying the rectangular area. The bars 29a and 29b have a rectangular shape. In this modification, the bars 29a and 29b extend in the horizontal direction. The subchart 29 shows, for example, two types of scents by bars 29a and 29b, respectively. The bar 29a extends horizontally at a position below the radar chart 21. The bar 29b is placed below the bar 29a and extends horizontally. The bars 29a and 29b are arranged in parallel. In FIG. 3, the bars 29a and 29b are shown by different hatching. The bars 29a and 29b may be colored with different colors.

In the subchart 29, for example, the evaluation value regarding the apple scent is indicated by the length of the bar 29a, and the evaluation value regarding the banana scent is indicated by the length of the bar 29b. The lengths of the bars 29a and 29b vary depending on the evaluation values shown. For example, the larger the evaluation value, the longer the lengths of the bars 29a and 29b. For example, the graph creation unit 16 calculates the relative value of the scent based on the scent value of each scent and the third reference data, and creates a subchart 29 showing the calculated relative value. Also in the subchart 29, the relative value of the scent corresponds to the length of the bars 29a and 29b. The smaller the relative value of the scent, the shorter the length of the bars 29a and 29b. The larger the relative value of the scent, the longer the bars 29a and 29b.

Next, with reference to FIG. 4, the function and configuration of the estimation model creation system will be described. FIG. 4 is a block diagram of the estimation model creation system. The estimation model creation system 30 creates an estimation model that estimates the sensation felt for the target food and drink. The food and drink evaluation system 1 and the estimation model creation system 30 may be integrated.

The estimation model creation system 30 includes a component data acquisition unit 31, a temporary data acquisition unit 32, a storage unit 33, a first component data selection unit 34, and a correlation coefficient calculation unit 35. The estimation model creation system 30 further includes a second component data selection unit 36, a provisional estimation model creation unit 37, a provisional evaluation value calculation unit 38, an estimation model determination unit 39, and an output unit 40.

The component data acquisition unit 31 acquires component data for each of a plurality of types of foods and drinks. As mentioned above, the component data includes information on the content of each of the plurality of components contained in the food and drink. The component data acquisition unit 31 may acquire component data from the outside of the estimation model creation system 30, or may acquire the measurement result in the component data acquisition unit 31 as component data.

The plurality of types of foods and drinks for which component data is acquired by the component data acquisition unit 31 are the types of foods and drinks targeted by the created estimation model, and are the same types of foods and drinks as the foods and drinks to be estimated. For example, when the food or drink whose sensation is estimated by the estimation model is classified into alcoholic beverages, the component data acquisition unit 31 acquires component data in each sample by using a plurality of types of alcoholic beverages as samples. Hereinafter, the food or drink for which the component data is acquired by the component data acquisition unit 31 is referred to as a “sample”.

In the component data acquired by the component data acquisition unit 31, the plurality of components included in the component data are, for example, all the components considered to be contained in all foods and drinks. In the component data acquired by the component data acquisition unit 31, the plurality of components included in the component data may be all components considered to be contained in the food or drink of the same type as the sample or the food or drink to be estimated. .. The food and drink to be estimated is the food and drink to be the target of the estimation model to be created. The component data acquired by the component data acquisition unit 31 is component data of measurable components, and is component data that can be measured based on various types of chromatography. The component data acquired by the component data acquisition unit 31 may include information on the carbonic acid content of a plurality of types of samples. The component data acquired by the component data acquisition unit 31 may include information regarding the scent component.

For example, the component data acquired by the component data acquisition unit 31 includes information on the content of each of the 153 types of components. When the estimation model creation system 30 creates an estimation model for estimating the sense of taste felt for an alcoholic beverage, the component data acquired by the component data acquisition unit 31 is for each measurable component contained in the alcoholic beverage. Contains information about the content of. The measurable ingredients contained in alcoholic beverages are, for example, 85 kinds of ingredients. The component data acquisition unit 31 acquires, for example, information on the content of each of 50 or more types of components as component data.

The temporary data acquisition unit 32 acquires temporary data for each of a plurality of types of samples. The temporary data acquisition unit 32 may acquire temporary data from the outside of the estimation model creation system 30, or may acquire the measurement result in the temporary data acquisition unit 32 as temporary data. The tentative data is data that quantitatively shows each of the plurality of types of sensations felt for each of the plurality of types of food and drink. The tentative data is acquired by a known sensor such as a taste sensor. As a modification of this embodiment, the provisional data may be data acquired based on a sensory test.

For example, tentative data about taste is acquired by known taste sensors. The taste sensor detects, for example, a taste value indicating the degree of each taste for a plurality of types of taste including salty taste, sour taste, sweet taste, umami taste, and bitter taste. The taste sensor measures, for example, the potential change of the lipid membrane. The taste sensor detects the taste value based on the potential change caused by the attachment of the component corresponding to each taste to the lipid membrane. Hereinafter, the evaluation value detected by a sensor such as a taste sensor is referred to as a detection evaluation value.

The components corresponding to the salty taste include, for example, NaCl, KCl, LiCl and the like. Ingredients corresponding to acidity include, for example, hydrochloric acid, acetic acid, citric acid, malic acid, succinic acid and the like. Ingredients corresponding to sweetness include, for example, glucose, sucrose, fructose, maltose, glycine, and aspartame. Ingredients corresponding to umami include glutamate, inosinic acid, guanylic acid and the like. Ingredients corresponding to bitterness include caffeine, quinine, tannin, phenylalanine, Mg ²⁺ and the like.

With known taste sensors, it is difficult to measure the content of each component because a plurality of components adhere to the lipid membrane at the same time. The types of components that affect the detection results in known taste sensors are far fewer than the types of components that can be measured based on various chromatographies.

The storage unit 33 stores various information in advance, and stores information from various functional units. The storage unit 33 stores, for example, the component data acquired by the component data acquisition unit 31. The storage unit 33 stores the temporary data acquired by the temporary data acquisition unit 32.

The first component data selection unit 34 is a component that is not related to or has a low relationship with the feeling felt for a food or drink of the same type as the food or drink to be estimated from the component data acquired by the component data acquisition unit 31. Select the first component data excluding the component data of. In other words, the first component data selection unit 34 deletes unnecessary data from the component data acquired by the component data acquisition unit 31. The first component data selection unit 34 removes component data related to components whose measured values are zero or close to zero in foods and drinks of the same type as the food and drink to be estimated from the component data acquired by the component data acquisition unit 31. .. The types of components in the first component data are greater than the types of components that affect the detection results in sensors such as known taste sensors. The first component data includes, for example, information on the content of each of 50 or more kinds of components.

The correlation coefficient calculation unit 35 calculates the correlation coefficient between each component and the sensation for each of the plurality of types of sensations based on the component data and the provisional data in each of the plurality of types of samples. This correlation coefficient indicates the degree of influence of each component on each sensation. The correlation coefficient calculation unit 35 has a phase relationship between each component and each sense based on the first component data selected by the first component data selection unit 34 and the temporary data acquired by the temporary data acquisition unit 32. Calculate the number. In other words, the correlation coefficient calculation unit 35 outputs the correlation coefficient between each component and each sensation in the first component data selected by the first component data selection unit 34 from the first component data and the taste sensor or the like. Calculate based on the detected evaluation value.

As an example, a case will be described in which a correlation coefficient for acidity among a plurality of types of sensations is calculated for 19 types of alcoholic beverages. For example, the correlation coefficient calculation unit 35 determines the content and acidity of each component based on the first component data of each of the 19 alcoholic beverages and the detection evaluation value regarding the acidity of each of the 19 alcoholic beverages. Calculate the correlation coefficient of. The calculation of the correlation coefficient is performed by a known method. The correlation coefficient takes a value from -1 to 1. When the correlation coefficient is -1, the influence is large in the negative direction. When the correlation coefficient is 1, the influence is large in the positive direction. If the correlation coefficient is zero, there is no correlation.

For example, 19 kinds of alcoholic beverages are defined as i (i = 1, ..., 19), the detection evaluation value regarding the acidity of each liquor type is xi, and the content of glutamic acid for each liquor type is yi. In this case, the correlation coefficient calculation unit 35 calculates the correlation coefficient between the detection evaluation value xi for the acidity of each liquor type and the glutamic acid content yi for each liquor type as the correlation coefficient of glutamic acid for the liquor type. Similarly, the correlation coefficient calculation unit 35 calculates the correlation coefficient between the content of each component other than glutamic acid in the first component data and the acidity. By repeating the above processing, the correlation coefficient calculation unit 35 calculates the correlation coefficient with each component for all sensations including acidity.

The second component data selection unit 36 selects component data from the first component data based on the correlation coefficient of each component in the first component data. The selection data selected by the second component data selection unit 36 includes information regarding the content of each of some of the components in the first component data. The second component data selection unit 36 selects component data of components whose correlation coefficient satisfies a predetermined condition as selection data for each sense.

The second component data selection unit 36 selects a plurality of patterns of selection data according to the conditions regarding the correlation coefficient for each sense. In other words, the second component data selection unit 36 is based on the correlation coefficient of each component in the first component data, and from the component data, a plurality of types of selection data including information on the content of each of different numbers of components. To sort out. These plurality of types of selection data are component data in which the above-mentioned correlation coefficient satisfies different conditions.

For example, the second component data selection unit 36 uses the component data of the component having the highest absolute value of the correlation coefficient to the fifth highest component among the components in the first component data as the selection data of the first pattern. Sort. For example, the second component data selection unit 36 selects the component data of the component whose absolute value of the correlation coefficient is 0.6 or more as the selection data of the second pattern. For example, the second component data selection unit 36 selects the component data of the component whose absolute value of the correlation coefficient is 0.4 or more as the selection data of the third pattern. The second component data selection unit 36 may further acquire all of the first component data as the selection data of the fourth pattern. The number of patterns of selection data selected by the second component data selection unit 36 is not limited to four. The number of these patterns and the conditions of the correlation coefficient may be appropriately determined according to the number of samples. The greater the number of patterns, the higher the accuracy of the estimation. However, if the number of patterns is too large, the computational load in creating the estimation model is large.

The tentative estimation model creation unit 37 creates a tentative estimation model based on the component data, the tentative data, and the correlation coefficient. The tentative estimation model creation unit 37 creates a tentative estimation model for each sensation. The tentative estimation model creation unit 37 creates a plurality of tentative estimation models based on the component data in which the correlation coefficient satisfies different conditions. In other words, the tentative estimation model creation unit 37 creates a tentative estimation model corresponding to each selection data based on the selection data of each of the plurality of patterns selected by the second component data selection unit 36.

The tentative estimation model creation unit 37 creates a tentative estimation model by, for example, regression analysis. Known techniques are used for regression analysis. The tentative estimation model creation unit 37 performs regression analysis for each sensation and creates a tentative estimation model for each sensation. For example, the tentative estimation model creation unit 37 performs regression analysis using the selection data selected by the second component data selection unit 36 as an explanatory variable and the tentative data as an objective function. The tentative estimation model creation unit 37 performs regression analysis based on the selection data and the tentative data of the sensation of the target in a plurality of types of samples.

The tentative estimation model creation unit 37 creates a tentative estimation model using, for example, linear regression and random forest regression. For example, the tentative estimation model creation unit 37 creates a tentative estimation model of four patterns by linear regression based on the selection data of each of the four patterns, and further creates a tentative estimation model of four patterns by random forest regression. create. In this case, the tentative estimation model creation unit 37 creates tentative estimation models of eight patterns for each sensation.

The tentative evaluation value calculation unit 38 calculates the tentative evaluation value based on the selection data selected by the second component data selection unit 36 and the tentative estimation model created by the tentative estimation model creation unit 37. The provisional evaluation value calculation unit 38 calculates the provisional evaluation value for each sense. In the present embodiment, the tentative evaluation value calculation unit 38 calculates a plurality of tentative evaluation values corresponding to each estimation model based on the plurality of tentative estimation models created by the tentative estimation model creation unit 37.

The estimation model determination unit 39 determines an estimation model based on a plurality of provisional evaluation values calculated using the provisional estimation models of a plurality of patterns. The estimation model determination unit 39 determines an estimation model for each sensation. The estimation model determination unit 39 determines, for each sensation, a tentative estimation model that is determined to have the highest estimation accuracy among the tentative estimation models of a plurality of patterns as the final estimation model. For example, the estimation model determination unit 39 determines an estimation model from the above-mentioned eight patterns of provisional estimation models based on the provisional data and the provisional evaluation values.

An example of a method of determining an estimation model by the estimation model determination unit 39 will be described with reference to FIG. FIG. 5 is a graph plotting tentative evaluation values calculated using tentative estimation models created under different conditions. The vertical axis is the difference between the provisional evaluation value and the reference value, and the horizontal axis is the type of sample. In FIG. 5, 19 kinds of samples a to s are shown. In the example shown in FIG. 5, provisional data is used as a reference value and an alcoholic beverage is used as a sample. FIG. 5 shows, as an example, data on acidity among a plurality of sensations.

FIG. 5 shows the difference between the provisional evaluation value of the sample indicated by the memory and the detection evaluation value of the sample indicated by the memory for each memory on the horizontal axis. The tentative evaluation value of the sample indicated by the memory is calculated by a tentative estimation model created by using 18 kinds of samples other than the sample indicated by the memory. For example, in the memory of "a" on the horizontal axis, the tentative evaluation value of the sample a by the tentative estimation model created by using 18 kinds of samples b to s other than the sample a and the tentative evaluation value of the tentative data detected by the taste sensor or the like. The difference from the detected evaluation value of the sample a is shown. In the memory of "b" on the horizontal axis, the tentative evaluation value of the tentative estimation model created using 18 kinds of samples a, c to s other than the sample b, and the sample detected by the taste sensor among the tentative data. The difference from the detected evaluation value of b is shown.

The data D1 to D8 are data related to the tentative evaluation value calculated by using the corresponding tentative estimation model among the above-mentioned eight patterns of tentative estimation models, respectively. The data D1 relates to the tentative evaluation value of the tentative estimation model created by using the random forest regression based on the selection data of the fourth pattern described above. The data D2 shows the tentative evaluation values of the tentative estimation model created by using linear regression based on the selection data of the fourth pattern. The data D3 relates to the tentative evaluation value of the tentative estimation model created by using the random forest regression based on the selection data of the first pattern described above. The data D4 relates to the tentative evaluation value of the tentative estimation model created by using linear regression based on the selection data of the first pattern. The data D5 relates to the tentative evaluation value of the tentative estimation model created by using the random forest regression based on the selection data of the second pattern described above. The data D6 relates to the tentative evaluation value of the tentative estimation model created by using linear regression based on the selection data of the second pattern. The data D7 relates to the tentative evaluation value of the tentative estimation model created by using the random forest regression based on the selection data of the third pattern described above. The data D8 relates to the tentative evaluation value of the tentative estimation model created by using linear regression based on the selection data of the third pattern.

Based on these data D1 to D8, the estimation model determination unit 39 determines the coefficient of determination ^R2 , the sum of the absolute values of the differences, the maximum value of the absolute values of the differences, and the differences for each of the tentative estimation models of the eight patterns. Calculate at least one of the average values of the absolute values of. The estimation model determination unit 39 determines an estimation model based on at least one of the coefficient of determination ^R2 , the sum of the absolute values of the differences, the maximum value of the absolute values of the differences, and the average value of the absolute values of the differences. When the coefficient of determination ^R2 is used, the estimation model determination unit 39 determines the tentative estimation model of the pattern having the largest coefficient of determination ^R2 as the estimation model. When the sum of the absolute values of the differences is used, the estimation model determination unit 39 determines the tentative estimation model of the pattern having the smallest sum of the absolute values of the differences as the estimation model. When the maximum value of the absolute value of the difference is used, the estimation model determination unit 39 determines the tentative estimation model of the pattern having the smallest maximum value of the absolute value of the difference as the estimation model.

The estimation model determination unit 39 determines an estimation model based on, for example, the coefficient of determination ^R2 , the sum of the absolute values of the differences, and the maximum value of the absolute values of the differences. For example, the estimation model determination unit 39 determines the estimation model by weighting the priority in the order of the condition based on the sum of the absolute values of the differences, the condition based on the maximum value of the absolute values of the differences, and the condition based on the coefficient of determination ^R2 . As a result, the estimation model determination unit 39 determines as the final estimation model a tentative estimation model of the pattern in which the difference is small as a whole, the difference is small, and the explanatory variables are small. As a result, the effect of variable variability is reduced.

For example, as a first choice, the estimation model determination unit 39 selects a tentative estimation model with a pattern in which the sum of the absolute values of the differences is smaller than a predetermined value. When there are a plurality of patterns selected in the first selection, the estimation model determination unit 39 determines, as the second selection, the maximum value of the absolute value of the differences among the patterns selected in the first selection. Select a tentative estimation model with a pattern smaller than the value of. When there are a plurality of patterns selected in the second selection, the estimation model determination unit 39 determines, as the third selection, that the coefficient of determination ^R2 is large among the patterns selected in the second selection. Select a tentative estimation model with a pattern that is greater than the value. When there are a plurality of patterns selected in the third selection, the estimation model determination unit 39 tentatively estimates the pattern selected in the third selection with the fewest explanatory variables as the fourth selection. Select a model. When there are a plurality of patterns selected in the fourth selection, the estimation model determination unit 39 tentatively uses random forest regression among the patterns selected in the fourth selection as the fifth selection. Select an estimation model.

The output unit 40 outputs the estimation model determined by the estimation model determination unit 39 to the storage unit 12 of the food and drink evaluation system 1. When the storage unit 12 and the storage unit 33 are integrated, the output unit 40 may store the estimation model determined by the estimation model determination unit 39 in the storage unit 33.

Next, with reference to FIG. 6, the hardware configuration of the food and drink evaluation system 1 and the estimation model creation system 30 will be described. FIG. 6 is a diagram showing an example of the hardware configuration of the food and drink evaluation system 1 and the estimation model creation system 30. In the example shown in FIG. 6, the food and drink evaluation system 1 and the estimation model creation system 30 are integrally configured. The food and drink evaluation system 1 and the estimation model creation system 30 may be configured separately from each other.

The food and drink evaluation system 1 and the estimation model creation system 30 include the system 100. The system 100 includes a processor 101, a main storage device 102, an auxiliary storage device 103, a communication device 104, an input device 105, and an output device 106. The system 100 includes one or more computers composed of these hardware and software such as a program. The food and drink evaluation system 1 and the estimation model creation system 30 are realized in cooperation with hardware.

When the system 100 is composed of a plurality of computers, these computers may be connected locally or may be connected via a communication network such as the Internet or an intranet. By this connection, one food and drink evaluation system 1 and an estimation model creation system 30 are logically constructed.

The processor 101 executes an operating system, an application program, and the like. The main storage device 102 is composed of a ROM (Read Only Memory) and a RAM (Random Access Memory). For example, evaluation value estimation unit 14, graph creation unit 16, first component data selection unit 34, correlation coefficient calculation unit 35, second component data selection unit 36, provisional estimation model creation unit 37, provisional evaluation value calculation unit 38, And at least a part of the estimation model determination unit 39 is realized by the processor 101 and the main storage device 102.

The auxiliary storage device 103 is a storage medium composed of a hard disk, a flash memory, or the like. The auxiliary storage device 103 generally stores a larger amount of data than the main storage device 102. For example, at least a part of the storage units 12 and 33 is realized by the auxiliary storage device 103.

The communication device 104 is composed of a network card or a wireless communication module. For example, at least a part of the component data acquisition unit 11, the estimation model acquisition unit 13, the output unit 17, the component data acquisition unit 31, the temporary data acquisition unit 32, and the output unit 40 is realized by the communication device 104. The input device 105 includes a keyboard, a mouse, a touch panel, and the like. For example, at least a part of the component data acquisition unit 11, the estimation model acquisition unit 13, the component data acquisition unit 31, and the temporary data acquisition unit 32 is realized by the input device 105. The output device 106 includes a display, a printer, and the like. For example, at least a part of the output unit 17 and the output unit 40 is realized by the output device 106. For example, the output device 106 displays at least one of the estimation information of each sensation estimated by the evaluation value estimation unit 14 and the estimation information of the target food and drink created by the graph creation unit 16.

The auxiliary storage device 103 stores data necessary for the program and processing in advance. The food and drink evaluation program causes a computer to execute each functional element of the food and drink evaluation system 1 or the estimation model creation system 30. Depending on the program, for example, processing S1 to processing S10 or processing S21 to processing S25, which will be described later, is executed in the computer. This program may be provided after being recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. This program may be provided as a data signal via a communication network.

Next, an example of processing in the method of creating an estimation model will be described with reference to FIG. 7. FIG. 7 is a flowchart showing a method of creating an estimation model.

First, the component data acquisition unit 31 acquires component data for each of a plurality of types of samples (process S1). In other words, the component data acquisition unit 31 acquires component data including information on the content of each of the plurality of components in each of the plurality of types of foods and drinks. The component data may include at least one of information regarding the scent component and information regarding the carbonic acid content.

Next, the temporary data acquisition unit 32 acquires temporary data for each of the plurality of types of samples (process S2). In other words, the provisional data acquisition unit 32 acquires provisional data that quantitatively indicates each of the plurality of types of sensations felt for each of the plurality of types of food and drink.

Next, the first component data selection unit 34 deletes unnecessary data from the component data acquired in the process S1 (process S3). From the component data acquired by the component data acquisition unit 31, the first component data selection unit 34 obtains component data of components that are not related to or have a low relationship with the feeling of food and drink of the same type as the food and drink to be estimated. Select the excluded first component data. The types of components in the first component data are greater than the types of components that affect the detection results in sensors such as known taste sensors.

Next, the correlation coefficient calculation unit 35 calculates the correlation coefficient of the component with respect to the sensation of the object (process S4). The correlation coefficient calculation unit 35 calculates the correlation coefficient between the sensation of the target and each component based on the first component data and the provisional data in each of the plurality of types of foods and drinks.

Next, the second component data selection unit 36 selects from the component data after the process of the process S3 based on the correlation coefficient (process S5). The second component data selection unit 36 selects selection data from the component data based on the correlation coefficient of each component. The selection data includes, for example, information on the content of each of a plurality of components among a plurality of components.

Next, the tentative estimation model creation unit 37 creates a tentative estimation model (process S6). The tentative estimation model creation unit 37 creates a tentative estimation model based on the selection data and the tentative data selected in the process S5, for example.

Next, the tentative evaluation value calculation unit 38 calculates the tentative evaluation value using the tentative estimation model (process S7). The tentative evaluation value calculation unit 38 calculates the tentative evaluation value based on the selection data selected in the immediately preceding process S5 and the tentative estimation model created in the immediately preceding process S6.

Next, the estimation model determination unit 39 determines whether the provisional evaluation values of all the provisional estimation models have been calculated (process S8). All the tentative estimation models are tentative estimation models created based on all the patterns in the process S5. If it is determined in the process S8 that the tentative evaluation values of all the tentative estimation models have not been calculated, the process returns to the process S5. As a result, by repeatedly executing the process S5, the second component data selection unit 36 obtains information on the content of each of different numbers of components from the component data, for example, based on the correlation coefficient of each component. Select the first and second selection data to be included. By repeatedly executing the processes S5 and S6, the tentative estimation model creation unit 37 can use, for example, a first tentative estimation model based on the first selection data and the tentative data, and the second selection data and the tentative data. Create a second tentative estimation model based on.

When it is determined in the process S8 that the tentative evaluation values of all the tentative estimation models have been calculated, the estimation model determination unit 39 determines the estimation model for the sensation of the object (process S9). By the steps from process S4 to process S9, an estimation model is created that outputs estimation information of the target food and drink according to the input of the component data in the target food and drink based on the component data, the provisional data, and the correlation coefficient. Will be done. In other words, an estimation model is created based on the selection data and the provisional data. For example, an estimation model is created based on selection data including information on the content of at least one component having a correlation coefficient of a predetermined value or more in the component data, and provisional data.

By repeatedly executing the processes S5 and S6, a plurality of tentative estimation models are created. The estimation model determination unit 39 determines one of a plurality of provisional estimation models as an estimation model based on the provisional data. For example, the estimation model determination unit 39 determines one of the first tentative estimation model and the second tentative estimation model as the estimation model.

Next, the estimation model determination unit 39 determines whether the estimation model has been determined for all the sensations (process S10). If it is determined that the estimation model has been determined for all sensations, the process returns to process S4. If it is determined that the estimation model has not been determined for all sensations, the process is terminated. By repeatedly executing the process S4, the correlation coefficient calculation unit 35 calculates the correlation coefficient between the sensations felt for food and drink and each component for each of the plurality of sensations. By repeatedly executing the processes S4 to S9, the estimation model is created for each sense.

Although an example of a method for producing an estimation model has been described above, the order of each processing is not limited to this. For example, the process S2 may be executed before the process S1 or may be executed after the process S3.

Next, an example of the processing in the estimation method will be described with reference to FIG. In this embodiment, the estimation model is used to estimate the sensation felt for the target food and drink. FIG. 8 is a flowchart showing an estimation method using an estimation model.

First, the specific information acquisition unit 10 acquires specific information of the target food and drink (process S21). For example, the specific information acquisition unit 10 acquires the name of the target food or drink. The specific information may be component data of the target food or drink. In this case, the component data acquisition unit 11 may also serve as the specific information acquisition unit 10, and the process S21 or the process S22 may be omitted.

Next, the component data acquisition unit 11 acquires component data of the target food or drink (process S22). For example, the component data acquisition unit 11 acquires component data of the target food or drink based on the specific information acquired in the process S21. The component data acquisition unit 11 acquires component data including information on the content of each of the plurality of components for the target food and drink. The component data may include at least one of information regarding the scent component and information regarding the carbonic acid content.

Next, the estimation model acquisition unit 13 acquires the estimation model (process S23). This estimation model includes component data for each of the multiple types of food and drink, provisional data that quantitatively indicates one of the multiple types of sensations felt for each of the multiple types of food and drink, and target sensations. It is created based on the correlation coefficient with each component.

Next, the evaluation value estimation unit 14 calculates the evaluation value using the estimation model (process S24). In other words, the evaluation value estimation unit 14 estimates the sensation felt for the target diet based on the component data of the target diet and the estimation model acquired in the immediately preceding process S23.

Next, the evaluation value estimation unit 14 determines whether or not the evaluation values of all the sensations have been acquired (process S25). In other words, the evaluation value estimation unit 14 determines whether or not the evaluation values related to all the senses have been acquired. If it is determined in the process S24 that the evaluation values related to all the sensations have not been acquired, the process proceeds to the process S23. By repeatedly executing the process S23, a plurality of estimation models corresponding to different sensations are acquired. By repeatedly executing the process S23 and the process S24, the evaluation value regarding the sensation corresponding to each estimation model for the target diet is estimated based on the component data in the target diet and the plurality of estimation models. .. In other words, the estimated information is estimated and acquired based on the specific information acquired in the process S21. In the present embodiment, the taste value, carbonic acid degree, and aroma value of Gomi are estimated by repeatedly executing the treatment S23 and the treatment S24. By repeatedly executing the process S23 and the process S24, the taste values of a plurality of tastes are estimated, and at least one of the carbonic acid degree and the aroma value may be acquired from the storage unit 12.

When it is determined in the process S24 that the evaluation values related to all the sensations have been acquired, the graph creation unit 16 creates a graph showing the estimation information of the target food and drink based on the acquired evaluation values (process S26). ). For example, the graph creation unit 16 creates a graph including a radar chart and a subchart. The graph creating unit 16 creates, for example, the graph 20 shown in FIG. 2 or the graph 20A shown in FIG.

Next, the output unit 17 outputs the evaluation data (process S27). In the process S27, the evaluation data output by the output unit 17 includes at least one of the graph created by the graph creation unit 16 and the evaluation value calculated by the evaluation value estimation unit 14. The output unit 17 causes, for example, the graph 20 shown in FIG. 2 or the graph 20A shown in FIG. 3 to be displayed on a display unit (not shown). The output unit 17 may output the graph data created by the graph creation unit 16 to the outside.

Although an example of the taste estimation method has been described above, the order of each process is not limited to this. For example, the process S22 may be executed after the process S23.

Next, the method of creating an estimation model, the food and drink evaluation system 1, and the operation and effect of the food and drink evaluation program in the present embodiment will be described.

In this food and drink evaluation system 1, the first evaluation value and the second evaluation value are acquired based on the specific information that identifies the target food and drink. Therefore, not only the evaluation value regarding the taste of the target food and drink, but also the evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink is acquired. The evaluation value regarding the taste is shown by the radar chart 21, and the evaluation value regarding at least one of the carbonic acid content and the aroma of the food or drink of interest is shown by the subcharts 22, 24, 29. The evaluation values related to taste are collectively shown in the radar chart 21. Therefore, according to this food and drink evaluation system 1, the sensation felt for food and drink can be clearly shown. The potential customer of the target food and drink can intuitively grasp the evaluation of the target food and drink by referring to the graphs 20 and 20A. Therefore, it is expected that purchasing motivation will be improved.

The graph 20 includes a frame surrounding the radar chart 21. The subchart 22 shows the second evaluation value according to the degree to which the specific region α occupies the inside of the frame 25. In this case, the ease of recognition of the estimated information is ensured while the first evaluation value and the second evaluation value are shown together and well.

The subchart 24 shows the second evaluation value by the lengths of the bars 24a and 24b extending from the position below the radar chart 21 so as to surround the radar chart 21. In this case, the ease of recognition of the estimated information is ensured while the first evaluation value and the second evaluation value are shown together and well.

The second evaluation value includes an evaluation value regarding the first-class scent and an evaluation value regarding the second-class scent. The sub-chart 24 shows the evaluation value of the first-class scent by the length of the bar extending to the left side of the radar chart 21 so as to surround the radar chart 21 from a position below the radar chart 21. The subchart 24 shows the evaluation value of the second kind scent by the length of the bar 24b extending to the right side of the radar chart 21 so as to surround the radar chart 21 from a position below the radar chart 21. In this case, evaluation values for two types of scents may be shown. While the first evaluation value and the evaluation value related to the scent are well shown, the ease of recognition of the first evaluation value and the evaluation value related to the scent is ensured.

The graph creating unit 16 is a reference data based on at least one of an evaluation value regarding the taste of a plurality of types of food and drink, an evaluation value regarding the carbon dioxide content of the plurality of types of food and drink, and an evaluation value regarding the aroma of the plurality of types of food and drink. Is getting. Graph 20 shows the estimated information based on the value comparing the reference data and the estimated information. In this case, the sensation felt for food and drink can be shown more clearly.

The reference data includes at least one of the first reference data, the second reference data, and the third reference data. The first criterion data includes the minimum value and the maximum value of the evaluation values relating to the taste of a plurality of types of foods and drinks. The second criterion data includes the minimum value and the maximum value of the evaluation values regarding the carbonic acid content of a plurality of types of foods and drinks. The third criterion data includes the minimum value and the maximum value among the evaluation values regarding the aromas of a plurality of kinds of foods and drinks. In the graph 20, at least one of the evaluation value regarding the taste of the target food and drink, the evaluation value regarding the carbon dioxide content of the target food and drink, and the evaluation value regarding the aroma of the target food and drink is included in the reference data. It is shown by relative evaluation with the minimum value as the lower limit and the maximum value included in the reference data as the upper limit. In this case, the sensation felt for food and drink can be shown more clearly.

It is conceivable that the evaluation of the sensation felt when eating or drinking food or drink is performed by, for example, a sensory evaluation or a taste sensor. However, the evaluation of each food and drink by sensory evaluation requires enormous labor and time, and depends on the evaluator's senses and the environment at the time of evaluation. In this respect, according to the taste sensor, the taste can be qualitatively and easily estimated from the components of food and drink. However, the components detected by sensors such as taste sensors are limited. For this reason, among the components contained in food and drink, the influence of components that are not detected by sensors such as known taste sensors may cause a difference between the sensation actually felt and the output result of the above sensor. .. For example, the taste is changed not only by the sensation of the tongue but also by the aroma and the throat. Considering these, it is not yet known which component of food and drink affects the senses and how. At least more components than are detected by sensors such as known taste sensors affect the sensation felt for food and drink.

The food and drink evaluation system 1 further includes an evaluation value estimation unit 14. The evaluation value estimation unit 14 estimates at least one of the first evaluation value and the second evaluation value based on the specific information. For example, the specific information is component data including information on the content of each of the plurality of components for the target food and drink. The estimation information acquisition unit 15 acquires the first evaluation value and the second evaluation value estimated by the evaluation value estimation unit 14 as estimation information. In this case, since at least one of the first evaluation value and the second evaluation value is estimated based on the component data, more accurate information can be shown.

As described above, the method of creating the estimation model calculates the correlation coefficient between the sensation and the component for each of the plurality of types of sensations based on the component data and the provisional data. The above estimation model is created based on the component data, the provisional data, and the correlation coefficient. According to this estimation model, the accuracy of the estimation is improved as compared with the case of estimating the sensation felt for the target food and drink based only on the provisional data.

In the food and drink evaluation system 1, the estimation model acquisition unit 13 quantitatively indicates component data for each of a plurality of types of food and drink, and provisional data and sensations that quantitatively indicate each of the plurality of types of sensations for each of the plurality of types of food and drink. Obtain an estimation model based on the correlation coefficient with each component. The evaluation value estimation unit 14 estimates the sensation felt for the target food and drink based on the information acquired by the component data acquisition unit 11 and the estimation model acquired by the estimation model acquisition unit 13. In this case, the accuracy of the estimation is improved as compared with the case of estimating the sensation felt for the target food and drink based only on the provisional data.

In the food and drink evaluation program, an estimation model based on component data, provisional data, sensation, and correlation coefficient between each component is acquired. Based on the component data of the target food and drink and the estimation model, the sensation felt for the target food and drink is estimated. In this case, the accuracy of the estimation is improved as compared with the case of estimating the sensation felt for the target food and drink based only on the provisional data.

In particular, the sweetness actually perceived by humans varies greatly depending on the content ratio of components other than the components used for estimating sweetness in known taste sensors. Therefore, quantitative evaluation of only the content of the component used for estimating sweetness in a known taste sensor may deviate from the sweetness actually perceived by humans. Therefore, in known taste sensors, the accuracy of estimation regarding sweetness is particularly low. According to the estimation model created by the above preparation method, the food and drink evaluation system 1, and the food and drink evaluation program, the accuracy of estimation of the taste including sweetness is improved.

In the step of creating an estimation model, selection data including information on the content of each component among a plurality of components is selected from the component data based on the correlation coefficient of each component, and the selection data and provisional data are selected. An estimation model may be created based on. In this case, information on unnecessary components is eliminated, so that the arithmetic processing load is suppressed. Since the information of the highly related components is used, the accuracy of the estimation is further improved.

In the step of creating the estimation model, the second selection data includes information on the content of each component different from the first selection data and the first selection data based on the correlation coefficient of each of the plurality of components. Sorting data is sorted. A first tentative estimation model based on the first selection data and tentative data, and a second tentative estimation model based on the second selection data and tentative data are created. Based on the tentative data, one of the first tentative estimation model and the second tentative estimation model is determined as the estimation model. In this case, information on unnecessary components is eliminated, so that the arithmetic processing load is suppressed. Since the information of the highly related components is used, the accuracy of the estimation is further improved.

The component data includes data based on various chromatographies. In this case, information regarding the content of more types of components can be obtained.

The food and drink may be alcoholic beverages. In the case of alcoholic beverages, not only taste but also carbonation and aroma are important. According to the food and drink evaluation system 1 and the food and drink evaluation program, evaluation is shown with respect to taste and at least one of carbonation and aroma. In the case of alcoholic beverages, sweetness is emphasized as a taste. According to the estimation model created by this preparation method, the food and drink evaluation system 1, and the food and drink evaluation program, the estimation of sweetness, which was not accurate in the conventional estimation, can be improved.

The component data may include at least one of information regarding the scent component and information regarding the carbonic acid content. In this case, the estimation model is created considering at least one of the scent component and the carbonation. Human taste is also associated with, for example, aroma and throat. According to this estimation model, the taste can be estimated in consideration of at least one of the aroma component and the carbonic acid content, so that the accuracy of the taste estimation is further improved. For example, the accuracy of these estimates by estimating at least one of saltiness, acidity, sweetness, umami, and bitterness based on information about at least one of the aroma components and carbonation of the subject food and drink and an estimation model. The degree can also be improved.

The estimation model acquisition unit 13 acquires a plurality of estimation models corresponding to different tastes. The evaluation value estimation unit 14 estimates the sensation corresponding to each estimation model for the target food and drink based on the information acquired by the component data acquisition unit 11 and the plurality of estimation models. In this case, the sensation felt for food and drink is estimated by the estimation model created for each sensation. Therefore, the accuracy of the estimation is improved as compared with the case where a plurality of types of sensations are estimated by one estimation model.

Although the embodiments and modifications of the present invention have been described above, the present invention is not necessarily limited to the above-described embodiments, and various modifications can be made without departing from the gist thereof.

For example, the food and drink evaluation system 1 may not include the estimation model acquisition unit 13 and the evaluation value estimation unit 14. In this case, the storage unit 12 may store information in which a plurality of types of foods and drinks are associated with estimated information of each food and drink. In this case, the food and drink evaluation system 1 acquires, for example, the specific information of the target food and drink acquired by the specific information acquisition unit 10, and acquires the estimated information associated with the acquired specific information from the storage unit 12. do.

1 ... Food and drink evaluation system, 10 ... Specific information acquisition unit, 14 ... Evaluation value estimation unit, 15 ... Estimated information acquisition unit, 16 ... Graph creation unit, 20, 20A ... Graph, 21 ... Radar chart, 22, 24, 29 ... Subchart, 24a, 24b, 29a, 29b ... Bar, 25 ... Frame, α ... Specific area.

Claims (18)

The first acquisition department that acquires specific information that identifies the target food and drink,
Based on the specific information, an estimated information including a first evaluation value regarding the taste of the target food and drink and a second evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink is acquired. With the second acquisition department,
A graph creation unit that creates a graph showing the estimated information is provided.
The graph is a food and drink evaluation system including a radar chart showing the first evaluation value and a subchart showing the second evaluation value around the radar chart. Further, an estimation unit for estimating at least one of the first evaluation value and the second evaluation value based on the specific information is provided.
The specific information is component data including information on the content of each of the plurality of components for the target food and drink.
The food and drink evaluation system according to claim 1, wherein the second acquisition unit acquires the first evaluation value and the second evaluation value estimated by the estimation unit as the estimation information. The graph includes a frame surrounding the radar chart.
The food and drink evaluation system according to claim 1 or 2, wherein the sub-chart shows the second evaluation value depending on the degree to which the specific area occupies the inside of the frame. The subchart is described in any one of claims 1 to 3, wherein the subchart indicates the second evaluation value by the length of a bar extending from a position below the radar chart so as to surround the radar chart. Food and beverage evaluation system. The second evaluation value includes an evaluation value regarding a first-class scent and an evaluation value regarding a second-class scent.
The sub-chart shows the evaluation value of the first-class fragrance by the length of a bar extending to the left side of the radar chart so as to surround the radar chart from a position below the radar chart. The food and drink evaluation according to claim 4, wherein the evaluation values relating to the two types of scents are indicated by the length of a bar extending to the right side of the radar chart so as to surround the radar chart from a position below the radar chart. system. The graph creating unit is based on at least one of an evaluation value regarding the taste of a plurality of types of food and drink, an evaluation value regarding the carbon dioxide content of a plurality of types of food and drink, and an evaluation value regarding the aroma of a plurality of types of food and drink. To get,
The food and drink evaluation system according to any one of claims 1 to 5, wherein the graph shows the estimated information based on a value comparing the reference data and the estimated information. The reference data includes the first reference data including the minimum value and the maximum value among the evaluation values regarding the taste of the plurality of types of food and drink, and the evaluation values regarding the carbonic acidity of the plurality of types of food and drink. At least one of the second standard data including the minimum value and the maximum value and the third standard data including the minimum value and the maximum value among the evaluation values regarding the aromas of the plurality of kinds of foods and drinks. Includes
In the graph, at least one of the evaluation value regarding the taste of the target food and drink, the evaluation value regarding the carbon dioxide content of the target food and drink, and the evaluation value regarding the aroma of the target food and drink is used as the reference data. The food and drink evaluation system according to claim 6, wherein the minimum value included is the lower limit and the maximum value included in the reference data is the upper limit. The food and drink evaluation system according to any one of claims 1 to 7, wherein the food and drink is an alcoholic beverage. The food and drink evaluation system according to any one of claims 1 to 8, further comprising an output unit for displaying the graph created by the graph creating unit. Acquiring specific information that identifies the target food and drink,
Based on the specific information, an estimated information including a first evaluation value regarding the taste of the target food and drink and a second evaluation value regarding at least one of the carbonic acid content and the aroma of the target food and drink is acquired. That and
To create a graph showing the estimated information, and let the computer execute
The graph is a food and drink evaluation program including a radar chart showing the first evaluation value and a subchart showing the second evaluation value around the radar chart. Further, a computer is made to estimate at least one of the first evaluation value and the second evaluation value based on the specific information.
The food and drink evaluation program according to claim 10, wherein the specific information is component data including information on the content of each of the plurality of components in the target food and drink. The graph includes a frame surrounding the radar chart.
The food and drink evaluation program according to claim 10 or 11, wherein the sub-chart shows the second evaluation value depending on the degree to which the specific area occupies the inside of the frame. The subchart is described in any one of claims 10 to 12, which indicates the second evaluation value by the length of a bar extending from a position below the radar chart so as to surround the radar chart. Food and beverage evaluation program. The second evaluation value includes an evaluation value regarding a first-class scent and an evaluation value regarding a second-class scent.
The sub-chart shows the evaluation value of the first-class fragrance by the length of a bar extending to the left side of the radar chart so as to surround the radar chart from a position below the radar chart. The food and drink evaluation according to claim 13, wherein the evaluation values relating to the two types of scents are indicated by the length of a bar extending to the right side of the radar chart so as to surround the radar chart from a position below the radar chart. program. Obtaining standard data based on at least one of the evaluation values regarding the taste of multiple types of food and drink, the evaluation values regarding the carbon dioxide content of multiple types of food and drink, and the evaluation values regarding the aroma of multiple types of food and drink. Let the computer do more,
The food and drink evaluation program according to any one of claims 10 to 14, wherein the graph shows the estimated information based on a value comparing the reference data and the estimated information. The reference data includes the first reference data including the minimum value and the maximum value among the evaluation values regarding the taste of the plurality of types of food and drink, and the evaluation values regarding the carbonic acidity of the plurality of types of food and drink. At least one of the second standard data including the minimum value and the maximum value and the third standard data including the minimum value and the maximum value among the evaluation values regarding the aromas of the plurality of kinds of foods and drinks. Includes
In the graph, at least one of the evaluation value regarding the taste of the target food and drink, the evaluation value regarding the carbon dioxide content of the target food and drink, and the evaluation value regarding the aroma of the target food and drink is used as the reference data. The food and drink evaluation program according to claim 15, wherein the minimum value included is the lower limit and the maximum value included in the reference data is the upper limit. The food and drink evaluation program according to any one of claims 10 to 16, wherein the food and drink is an alcoholic beverage. The food and drink evaluation program according to any one of claims 10 to 17, further causing a computer to display the created graph on a display unit.

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