Taste information providing method and program

Abstract

Provided is a technique for providing objective and highly accurate information about assessment of taste. The present invention involves acquiring, by a device, information about respective amounts of two or more components in a target object (step S21). The two or more components are associated with a specific taste. The device derives the result of determination of whether or not a target object has a specific taste, on the basis of proportion information about proportions of the two or more components and information about the respective amounts of the two or more components (step S23), and outputs the result of the determination (step S24).

Description

[Technical Field]

[0001] The present invention relates to a method for providing taste information, and more particularly to providing information about a specific taste of an object. [Background technology]

[0002] Traditionally, various taste concepts have been defined for various foods, and various studies have been conducted on providing information about each taste. For example, one of the tastes defined for sake is called "fullness." The concept of "fullness" is widely used among sake lovers to describe the taste of sake. Although there is no firm definition of the taste "fullness," for example, Non-Patent Document 1 describes it as "the way the taste and aroma spread roundly in the mouth" and "the way an aroma similar to the taste reaches the nose and leaves a long-lasting aftertaste."

[0003] The evaluation of specific tastes in food is largely left to the judgment of sensory evaluators. Under these circumstances, many attempts have been made to evaluate various tastes using the results of component analysis. For example, regarding whether or not each variety of sake has the aforementioned "fullness," Non-Patent Document 2 reports an evaluation based on the results of an analysis of the contents of specific components (i.e., sake varieties with low contents of ethanolamine, glycine, and proline have the taste "fullness") (see, for example, Non-Patent Document 2). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Sake Taste Encyclopedia, Fukurami, online, August 29, 2019, [Retrieved March 8, 2022], Internet<URL:https: / / twitter.com / sakedic / status / 1167044555163623425> [Non-patent document 2] Kimio Iwano, Toshihiko Ito, Nobuyuki Nakazawa, Correlation Analysis between Sensory Evaluation and Chemical Components of Ginjo Sake, Journal of the Brewing Society of Japan, Japan, Brewing Society of Japan, September 15, 2005, Vol. 100, No. 9, pp. 639-649 Summary of the Invention [Problem to be solved by the invention]

[0005] Evaluations using component analysis are expected to provide objective information on taste, and there is a demand for a system that can provide such objective information with greater accuracy.

[0006] The present invention has been devised in view of the above circumstances, and its purpose is to provide a technique for providing objective and highly accurate information regarding taste evaluation. [Means for solving the problem]

[0007] A taste information providing method according to one aspect of the present disclosure is a method for providing taste information regarding a specific taste, comprising the steps of acquiring object proportion information regarding the proportions of two or more types of components in an object, the two or more types of components being associated with the specific taste, deriving a judgment result of the specific taste for the object based on the object proportion information and reference proportion information regarding the proportions of the two or more types of components, and outputting the judgment result.

[0008] A taste information providing method according to another aspect of the present disclosure is a method for providing taste information regarding a specific taste, comprising a step of acquiring information regarding the amount of each of two or more types of components in an object, the two or more types of components being associated with the specific taste, and further comprising a step of calculating, for the information regarding the amount of each of the two or more types of components, a ratio of the amount of each component to the total amount of the two or more types of components, and a step of outputting the ratio of the amount of each component.

[0009] A program according to an aspect of the present disclosure is a program for providing taste information, which, when executed by a processor of a computer, causes the computer to perform the above-described taste information providing method. [Effects of the Invention]

[0010] According to the present disclosure, objective and highly accurate information is provided regarding taste evaluation. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing the configuration of a device 100 for providing taste information. [Figure 2] This is a diagram showing information on eight types of sake used to select marker components. [Figure 3] FIG. 1 shows the analytical conditions for high-performance liquid chromatography (HPLC) and mass spectrometry (MS) in identifying taste-related components. [Figure 4] FIG. 10 is a diagram showing a score plot created as a result of principal component analysis regarding the identification results of taste-related components. [Figure 5] FIG. 1 shows a loading plot created as a result of principal component analysis regarding the identification results of taste-related components. [Figure 6] FIG. 10 is a diagram showing a score plot created as a result of partial least squares discriminant analysis regarding the identification results of taste-related components. [Figure 7] FIG. 1 shows a loading plot created as a result of partial least squares discriminant analysis regarding the identification results of taste-related components. [Figure 8] FIG. 8 shows the test statistics of the partial least squares discriminant analysis described with reference to FIGS. 6 and 7. [Figure 9] FIG. 9 is a diagram showing the difference in area values ​​between samples with swelling and samples without swelling for four types of components out of the ten types of components shown in FIG. 8. [Figure 10] FIG. 10 is a diagram showing an example of a screen provided as information about bulging. [Figure 11] 1 is a flowchart of an example of a process performed by device 100 to provide information about taste. [Figure 12] FIG. 10 is a diagram showing a screen displayed in a modified example of step S14. [Figure 13] 10 is a flowchart of another example of a process performed by device 100 to provide information about taste. [Figure 14] FIG. 10 shows specific examples of peak areas of marker components and determination results for two types of objects. [Figure 15] FIG. 10 is a diagram showing an example of a screen displaying a determination result. [Figure 16] FIG. 1 shows the analytical conditions for gas chromatography (GC) and mass spectrometry (MS) in identifying aroma components. [Figure 17] FIG. 10 is a diagram showing a score plot created as a result of principal component analysis regarding the identification results of aroma components. [Figure 18] FIG. 1 shows a loading plot created as a result of principal component analysis regarding the identification results of aroma components. [Figure 19] FIG. 10 shows a score plot created as a result of partial least squares discriminant analysis of the aroma component identification results. [Figure 20] FIG. 1 shows a loading plot created as a result of partial least squares discriminant analysis of the aroma component identification results. [Figure 21] FIG. 21 shows the test statistics of the partial least squares discriminant analysis described with reference to FIGS. 19 and 20. [Figure 22] FIG. 22 is a diagram showing the difference in area values ​​between bulging samples and non-bulging samples for the two types of components shown in FIG. 21. [Figure 23] FIG. 10 shows specific examples of peak areas of marker components and determination results for two types of objects. [Figure 24] FIG. 1 is a block diagram showing the configuration of a device 100A for providing taste information. [Figure 25]10 is a flowchart of an example of processing performed by the device 100A. [Figure 26] 10 is a flowchart of a process performed to provide information about taste in the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0013] [First embodiment] <Device configuration> 1 is a block diagram showing the configuration of a device 100 for providing taste information. One example of the taste information provided is the ratio of multiple types of ingredients selected for a specific taste. Another example is a determination result of whether or not a person has a specific taste.

[0014] The device 100 is configured, for example, based on a personal computer, or may be configured as a server that can be accessed from one or more terminal devices via a network such as the Internet.

[0015] The device 100 includes a processor 101, a memory 200, and an input / output port 300. A mouse 400, a keyboard 500, and a display device 600 are connected to the input / output port 300. An analytical device such as a chromatograph may be connected to the input / output port 300. One or more terminal devices may be connected to the input / output port 300 via the Internet or an in-house network. The device 100 itself may include at least some of the mouse 400, the keyboard 500, and the display device 600.

[0016] Measurement data is input to the input / output port 300. The measurement data may be used to provide taste information or may be used to create training data for an estimation model.

[0017] The memory 200 stores measurement data 210, training data 220, an estimation model 230, and an analysis program 240 for executing analysis processing and machine learning processing. These may be non-temporarily stored in the memory 200. In one implementation example, the measurement data may be input to the device 100 through a chromatograph connected to the input / output port 300.

[0018] The training data 220 includes a data set prepared for each of two or more samples. Each data set includes the peak area of ​​a given type of component identified from the measurement data of the sample and a judgment result assigned to the sample. The judgment result indicates the result of the sensory evaluator as to whether each sample has a particular taste. The given type of component will be described later as a marker component.

[0019] The estimation model 230 is a program for performing calculations according to the model. In one implementation example, the estimation model 230 is realized as a discrimination model created by statistical analysis software (e.g., eMSTAT (registered trademark) by Shimadzu Corporation: https: / / www.an.shimadzu.co.jp / ms / emstat / index.htm) and follows the algorithm "support vector machine." Note that the algorithm of the estimation model 230 is not limited to this and may be any algorithm that can determine whether an object has a specific taste based on input data about the object (e.g., values ​​related to the amounts of each of a given type of component of the object).

[0020] The analysis program 240 includes, as its functions, a data creation unit 241, a model creation unit 242, a determination unit 243, a data analysis unit 244, a data processing unit 245, and an output unit 246. In one implementation example, the functions of the data creation unit 241, the model creation unit 242, the determination unit 243, the data analysis unit 244, the data processing unit 245, and the output unit 246 are realized by the processor 101 executing a given program.

[0021] The data creation unit 241 creates teacher data 220 from the measurement data 210. More specifically, the data creation unit 241 identifies the peak area value, peak height, or component concentration of each given type of component from the measurement data 210 for each sample, and assigns the above-mentioned determination results to these peak areas, thereby creating teacher data 220 for each sample. A peak picking technique may be used to identify the peak areas.

[0022] The model creation unit 242 uses the training data 220 to perform machine learning processing on the estimation model 230. In this specification, the estimation model 230 that has been subjected to machine learning processing is also referred to as a "trained model."

[0023] The determination unit 243 derives a determination result for the object by applying the measurement data of the object to the trained model.

[0024] The data analysis unit 244 executes a process for selecting the above-mentioned given type of component from the components contained in the sample, using the measurement data 210. The selection of the given type of component will be described later as the selection of a component for use as a marker.

[0025] The data processing unit 245 processes the measurement data 210 to create graphs such as those described with reference to FIGS.

[0026] The output unit 246 generates image information for displaying the judgment result derived by the judgment unit 243 and the graph created by the data processing unit 245, and instructs the display device 600 (or an external device) to display the image information.

[0027] <Selection of marker components> As described above, in this embodiment, marker components are used to provide information about a specific taste. A specific example of the selection of marker components will be described below. In the following example, the taste of "fullness" associated with sake is used as an example of a specific taste.

[0028] (1) Types of sake used Figure 2 shows information on the eight types of sake used to select the marker components. In Figure 2, "No. 1" to "No. 8" are shown as information to identify each of the eight types of sake.

[0029] Figure 2 also shows the brewing conditions and the sensory evaluation "fullness" for each type of sake. The brewing conditions include two items (rice polishing ratio and yeast). The degree of rice polishing indicates the polishing ratio of the rice used in brewing and includes three values ​​(A, B, and C). The yeast indicates the type of yeast used in brewing and includes three values ​​(X, Y, and Z).

[0030] The sensory evaluation of "fullness" includes three items (rank, agreement of evaluation, and group). Rank represents the ranking given by multiple evaluators. 1st place indicates that the "fullness" was judged to be the greatest among the eight types of sake. Agreement of evaluation represents the degree of agreement between the evaluations of multiple evaluators, and includes three values ​​(P, Q, and R). "P" indicates that the evaluations of multiple evaluators were consistent. "Q" indicates that the majority of the evaluations of multiple evaluators were consistent. "R" indicates that the evaluations of multiple evaluators were divided. Group represents the group into which each type of sake was classified according to the ranking given to that type, and includes two values ​​(yes and no). "yes" represents a group that has "fullness." "no" represents a group that does not have "fullness." In the example in Figure 2, Nos. 2, 5 to 7 belong to the group with "fullness," while Nos. 1, 3, 4, and 8 belong to the group without "fullness." As shown in Figure 2, there are types of sake with an evaluation consistency value of "R," and it can be understood that it can be difficult for evaluators to evaluate "fullness."

[0031] (2) Identification of taste-related components To obtain analytical data for selecting marker components, three samples were prepared from each of the eight types of sake mentioned above, resulting in a total of 24 samples. For each sample, sake was diluted 10 times with water.

[0032] Figure 3 shows the analytical conditions for high-performance liquid chromatography (HPLC) and mass spectrometry (MS) used to identify taste-related components. For each of the 24 samples, the ion-pair-free LC / MS / MS method included in the "LC / MS / MS Method Package for Primary Metabolites Ver. 3" (https: / / www.an.shimadzu.co.jp / lcms / tq-option / mp_primary-metabolites.htm) was used to identify 153 components (taste-related components) that were established by adding sugars to hydrophilic metabolites such as amino acids, organic acids, nucleosides, and nucleotides, which are important in metabolome analysis in the life sciences, according to the conditions shown in Figure 3.

[0033] The results of the above identification are input to the device 100 via the input / output port 300 as measurement data.

[0034] (3) Data analysis using principal component analysis (PCA) Figure 4 shows a score plot created as a result of principal component analysis for the identified taste-related components. Figure 5 shows a loading plot created as a result of principal component analysis for the identified taste-related components.

[0035] The score plot shown in Figure 4 and the loading plot shown in Figure 5 each represent the results of principal component analysis using statistical analysis software (e.g., eMSTAT (registered trademark) manufactured by Shimadzu Corporation: https: / / www.an.shimadzu.co.jp / ms / emstat / index.htm) using the area ratios of the taste-related components identified as described above (internal standard: 2-Morpholinoethanesulfonic acid).

[0036] As shown in Figure 4, for each of No. 1 to No. 8, the points for three samples made from the same type of sake are located close to each other on the score plot. In Figure 4, the value of the group to which each type belongs ("Group" in Figure 2), i.e., "fullness" or "no fullness," is written next to each point for No. 1 to No. 8. For example, Figure 2 shows that sake No. 1 belongs to the "no fullness" group. And in Figure 4, "no fullness" is written next to the three points corresponding to No. 1.

[0037] In Figure 4, areas 11, 12, and 13 represent the types of yeast used in brewing each type of sake (see Figure 2). Area 11 represents the area where points corresponding to the types of sake that used yeast X (Nos. 1, 5 to 7) are located. Area 12 represents the area where points corresponding to the types of sake that used yeast Y (Nos. 2, 3, and 8) are located. Area 13 represents the area where points corresponding to the type of sake that used yeast Z (No. 4) are located. According to the results shown in Figure 4, sake can be classified by the type of yeast used.

[0038] The names of the main components are annotated in Figure 5. The annotated compound names include disaccharide, monosaccharide, lysine, valine, leucine, choline, and arginine.

[0039] From Figures 4 and 5, it can be seen that principal component analysis allows sake to be classified by the type of yeast used. Furthermore, quadrant 2 is characterized by monosaccharides and disaccharides, while quadrants 1 and 4 are characterized by amino acids such as leucine and valine. However, in Figure 4, region 11 is located between regions 12 and 13. This makes it difficult to derive conditions for distinguishing between the region containing "full" points and the region containing "no full" points on the score plot.

[0040] From the above, the results shown in Figures 4 and 5 indicate that it is difficult to find components that characterize whether or not sake has "fullness" through principal component analysis.

[0041] (4) Data analysis using partial least squares discriminant analysis (PLS-DA) Figure 6 shows a score plot created as a result of partial least squares discriminant analysis of the results of identifying taste-related components. Figure 7 shows a loading plot created as a result of partial least squares discriminant analysis of the results of identifying taste-related components. Note that in Figures 6 and 7, sake types for which evaluations differed among multiple evaluators (No. 3 and No. 6 in Figure 2, for which the "evaluation agreement" value was "R") were excluded from the analysis.

[0042] In the score plot shown in Figure 6, the points for the "no swell" sakes (No. 1, 4, 8) are located in the second and third quadrants. That is, the first principal components of these sakes form a group in the negative region of the score plot. In the loading plot, the points for amino acids and sugar compounds (leucine, valine, lysine, and disaccharides) are located in the region corresponding to the negative region of the score plot. Therefore, it can be seen that these compounds are contained in large amounts in sakes rated as "no swell."

[0043] (5) Marker components Fig. 8 is a diagram showing the test statistics of the partial least squares discriminant analysis described with reference to Fig. 6 and Fig. 7. Fig. 8 shows the test statistics (p-values) for each of 10 components (cytidine, histamine, adenosine, disaccharide, ornithine, adenine, malic acid, citric acid, isocitric acid, and glyoxylic acid). These 10 components are included in No. 1 to No. 8.

[0044] 8 shows test statistics including statistics for a t-test and statistics for a u-test. Both the t-test and the u-test are examples of test statistics calculated to analyze the difference between sake with "fullness" and sake without "fullness."

[0045] Components with low test statistics are considered to be components whose content differs significantly between the group of sakes with "fullness" and the group of sakes without "fullness." In this embodiment, the 10 components shown in FIG. 8 are selected as such components (marker components). The selection criterion is that both the values ​​of the statistics for the t-test and the statistics for the u-test are 0.05 or less. The device 100 stores information identifying the selected components in memory 200 in association with the taste "fullness."

[0046] The 10 types shown in Figure 8 include a large number of organic acids such as malic acid, followed by sugars, nucleic acid bases, and amino acids. Of the 10 components, disaccharides have a characteristic "sweet" taste. Malic acid and citric acid have a characteristic "sour" taste.

[0047] Figure 9 is a diagram showing the difference in area values ​​(peak area values ​​in a chromatogram) between samples with and without swelling for four of the ten components shown in Figure 8. In Figure 9, four graphs 31 to 34 are shown within a frame 30. Graph 31 shows the area value for cytidine, graph 32 shows the area value for disaccharides, graph 33 shows the area value for malic acid, and graph 34 shows the area value for ornithine. Each of graphs 31 to 34 is a box plot.

[0048] In each of graphs 31 to 34, the vertical axis represents the peak area value of each component in the chromatogram. "No swell" represents three types of sake samples (No. 1, 4, 8) without swell (total of nine samples). "With swell" represents three types of sake samples (No. 2, 5, 7) with swell (total of nine samples).

[0049] Graphs 31 to 34 show that all four of the above components are present in greater amounts in the non-full samples than in the full-bodied samples. This suggests that whether a given type of sake has a full, balanced taste is influenced not by the amount of a single component, but by the balance between components such as organic acids (acidity) and sugars (sweetness).

[0050] <Display of the percentage of marker components> In this embodiment, the ten components shown in Figure 8 were selected as components (marker components) that influence whether or not sake has a "full" taste. Figure 10 is a diagram showing an example of a screen provided as information about fullness.

[0051] Screen 40 in FIG. 10 includes graphs 41 and 42. Graphs 41 and 42 both show the proportions of the 10 components in sake. More specifically, graphs 41 and 42 show the proportion of the peak area of ​​each of the 10 components to the total peak area in the chromatogram of the 10 components. Graph 41 is for No. 7, an example of a type with fullness. Graph 42 is for No. 1, an example of a type without fullness.

[0052] Comparing graphs 41 and 42, it is easy to see visually that sake with swell has a higher ratio of disaccharides and a lower ratio of malic acid than sake without swell. It is also easy to see visually that sake with swell has a lower ratio of cytidine, a nucleic acid base, than sake without swell. This leads to the conclusion that sake with swell has a higher ratio of sweet components and a lower ratio of sour components than sake without swell.

[0053] The device 100 may present the proportions of ten components as shown in FIG. 10 as an example of information relating to the taste "fullness" of sake, which is the target product.

[0054] Fig. 11 is a flowchart of an example of processing performed by device 100 to provide information related to taste. In one implementation example, in device 100, the processing of Fig. 11 is performed by processor 101 executing a given program. Also, in one implementation example, the processing of Fig. 11 is started in response to a start instruction being input to mouse 400 and / or keyboard 500. The processing of Fig. 11 will be described below.

[0055] In step S11, device 100 acquires (reads) measurement data of the object. In step S12, the apparatus 100 acquires the peak area value of each of the marker components from the measurement data acquired in step S11. In addition to the peak area value, peak height or component concentration may also be used. The peak area of ​​each component is acquired, for example, using peak picking technology. When the apparatus 100 acquires the peak area of ​​each component as measurement data, in step S12, the peak area of ​​each of the marker components is read out from the measurement data.

[0056] In step S13, the apparatus 100 calculates the proportion of each component from the peak area of ​​each of the marker components acquired in step S12. For example, if the marker components are composed of 10 types of compounds as shown in Figure 8, in step S13, the proportion of the peak area of ​​each of the 10 components to the total peak area of ​​the 10 components is calculated. The proportion of each component is an example of target substance proportion information.

[0057] In step S14, the apparatus 100 uses the proportions of each component calculated in step S13 to create a graph such as the graph 41 or 42 shown in Fig. 10 and displays it on the display device 600. Then, the apparatus 100 ends the processing in Fig. 11.

[0058] In the process described with reference to FIG. 11, in step S14, the ratio of the peak area of ​​each marker component to the total peak area of ​​the marker components for the object is displayed as a graph. In this example, the peak area of ​​each component is an example of information regarding the amount of each component. In this example, the objects displayed in terms of peak area ratio are narrowed down to the marker components. This allows the user to narrow down the (number of) components to be considered when examining the presence or absence of "bulge" to components that are likely to affect the presence or absence of "bulge."

[0059] In step S14, device 100 may display the graph created for the target object together with the graphs created for sake with expansion and sake without expansion.

[0060] FIG. 12 is a diagram showing a screen displayed in the above-described modified example of step S14. In FIG. 12, screen 45 includes graph 40X, graph 41, and graph 42. Graph 40X represents a graph created for the object. As shown in FIG. 12, screen 45 displays the graph of the object (graph 40X) together with a graph created for sake with expansion (graph 41) and a graph created for sake without expansion (graph 42). A user viewing screen 45 can determine whether the object has expansion based on, for example, whether the proportion in graph 40X is close to the proportion with expansion or the proportion without expansion.

[0061] <Creating a trained model> The device 100 uses the training data 220 to perform a learning process for the estimation model 230. In the data set constituting the training data 220, each piece of data includes information for each sample. More specifically, each piece of data includes, for each sample, information representing the peak area in the chromatogram of each of the marker components (the above-mentioned 10 components) and the determination result assigned to the target sample (whether or not there is a bulge). The peak area is an example of information regarding the amount of each component in the sample.

[0062] As a result, when the peak areas of the marker components of the object are input, the trained estimation model 230 uses the peak areas of the marker components to output a determination result of whether or not the object has a bulge. Note that in the device 100, when the peak areas of the marker components of the object are input, the estimation model 230 can determine two types of events (the object has a bulge, and the object GafuThe probability of each of the events (having no blindness) may be output, and the determination unit 243 may specify the event with the highest probability as the determination result, thereby acquiring the determination result.

[0063] <Verdict> Fig. 13 is a flowchart of another example of processing performed by device 100 to provide information related to taste. In one implementation example, in device 100, processor 101 executes a given program to perform the processing of Fig. 13. Also, in one implementation example, the processing of Fig. 13 is started in response to a start instruction being input to mouse 400 and / or keyboard 500. The processing of Fig. 13 will be described below.

[0064] In step S21, device 100 acquires (reads) measurement data of the object. In step S22, the apparatus 100 acquires the peak area value of each of the marker components from the measurement data acquired in step S21. In addition to the peak area value, peak height or component concentration may also be used. The peak area of ​​each component is acquired, for example, using peak picking technology. When the apparatus 100 acquires the peak area of ​​each component as measurement data, in step S22, it reads out the peak area of ​​each of the marker components from the measurement data.

[0065] In step S23, the device 100 inputs the peak areas of the marker components obtained in step S22 into the estimation model 230, thereby obtaining a determination result.

[0066] In step S24, device 100 displays the determination result acquired in step S23 on display device 600. An example of a screen for displaying the determination result will be described later with reference to Fig. 15. Then, device 100 ends the processing of Fig. 13.

[0067] Apparatus 100 may perform the process of Fig. 13 in parallel with or successively to the process of Fig. 11. Apparatus 100 may display the determination result of step S24 on display device 600 together with the graph of step S14.

[0068] FIG. 14 shows specific examples of peak areas of marker components and determination results for two types of objects.

[0069] FIG. 14 shows the type of sake, sample number, evaluation result, score, and ingredients.

[0070] More specifically, two types of sake (No. 3 and No. 6 in FIG. 2) are shown in Fig. 14. That is, in the example of Fig. 14, the target objects are sake No. 3 and sake No. 6 in Fig. 2.

[0071] The sample number identifies each of the two or more samples created from the object. In the example of Figure 14, three samples were created from each type of sake. Therefore, sample numbers 1 to 3 are shown in Figure 14 for each type.

[0072] The judgment results represent the judgment results output by device 100 for sake No. 3 and sake No. 6. The judgment result for each of the three samples of sake No. 3 is "no fullness" (no fullness). The judgment result for each of the three samples of sake No. 6 is "fullness" (fullness).

[0073] The score represents the likelihood of the judgment result for each sample. The judgment result indicates which of two groups (bulge / no bulge) each sample belongs to. The higher the score in Figure 14, the higher the likelihood that each sample belongs to the group derived as the judgment result.

[0074] The components represent the peak area values ​​of the marker components input into the estimation model 230 to obtain the determination results.

[0075] As shown in Figure 14, the same judgment result (no fullness) was derived for all three samples of sake No. 3. Furthermore, the same judgment result (fullness) was derived for all three samples of sake No. 6. In other words, judgments using the trained estimation model 230 can derive a fairly reliable judgment result for types of sake for which evaluations by multiple evaluators differ. This judgment method is difficult to determine based solely on the amount (peak area) of a specific component, but it is possible to distinguish between tastes whose presence or absence varies depending on the ratio of multiple components to the total amount. The ratio may be calculated by assuming the total amount of multiple specific components to be 100.

[0076] FIG. 15 is a diagram showing an example of a screen displaying the evaluation results. In the screen 70 of FIG. 15, a character string "bulging" is displayed in a field 71 as an example of the evaluation results. The output form of the evaluation results is not limited to displaying a character string, but may be displaying a picture, outputting a sound, or a combination of these. As described above, tastes that are subject to differing evaluations by sensory evaluators can be objectively evaluated. In other words, information that complements the taste descriptions of the sensory evaluators can be provided.

[0077] <Summary> In the present embodiment described above, information relating to a specific taste (taste information) is output for a target object. The graph displayed in step S14 (FIG. 11) is an example of taste information. The determination result displayed in step S24 (FIG. 13) is another example of taste information. The trained estimation model 230 is used to derive the determination result. In this sense, the information (model and parameters) constituting the trained estimation model 230 is an example of reference ratio information relating to the ratio of marker components.

[0078] In this embodiment, taste information is created using the amounts of components in the object. This allows the taste information to be provided as objective information regarding taste evaluation. Furthermore, the taste information is created using the amounts (peak areas) of multiple components (marker components) in the object, rather than the amount of a single component. This allows the taste information to be provided as highly accurate information.

[0079] In addition, in the present disclosure, "fullness" is an example of a specific taste, but the taste targeted in the present disclosure is not limited to "fullness."

[0080] Another example of a specific taste is "sweetness." Even if a food product contains a large amount of sugars, if it also contains a large amount of bitter or sour components, people may find it difficult to detect sweetness. In other words, in the present disclosure, taste information regarding the taste "sweetness" can be provided by including a component having sweetness and a component having bitterness and / or sourness as taste characteristics in the marker component.

[0081] Another example of a particular taste is "richness." In some cases, people perceive a "richness" in food when the balance of the content of multiple ingredients is appropriate, rather than the content of a single ingredient. In other words, in the present disclosure, taste information regarding the taste of "richness" can be provided by including multiple ingredients that make up the "richness" in the marker ingredients.

[0082] Yet another example of a specific taste is "umami." In food, the balance and / or total amount of multiple umami components (such as glutamic acid and inosinic acid) affects the degree to which a person perceives umami. That is, in the present disclosure, taste information regarding the taste "umami" can be provided by including multiple umami components in the marker component.

[0083] [Second embodiment] In the second embodiment, as in the first embodiment, the "fullness" of sake is used as an example of a specific taste. While the first embodiment focuses on the taste-related components of sake, the second embodiment focuses on the aroma components of sake.

[0084] <Selection of marker components> In the second embodiment, the aroma components were identified for the same eight types of sake (Fig. 2) used in the first embodiment. Fig. 16 shows the analytical conditions for the gas chromatograph (GC) and mass spectrometer (MS) used in the identification of the aroma components.

[0085] Fig. 17 shows a score plot created as a result of principal component analysis of the identified aroma components. Fig. 18 shows a loading plot created as a result of principal component analysis of the identified aroma components.

[0086] In Figure 17, areas 51, 52, and 53 represent the types of yeast used in brewing each type of sake (see Figure 2). Area 51 represents the area where points corresponding to the types of sake (Nos. 1, 5 to 7) that used yeast X are located. Area 52 represents the area where points corresponding to the types of sake (Nos. 2, 3, and 8) that used yeast Y are located. Area 53 represents the area where points corresponding to the type of sake (No. 4) that used yeast Z are located.

[0087] In Figure 18, the names of the main components are indicated. The names of the compounds indicated are isobutanol, ethyl acetate, 2-methyl-1-butanol (2-Methyl-1-butanol), and h These include isoamyl-1-butanol, isoamyl acetate, isoamyl alcohol, acetaldehyde, and 1-propanol.

[0088] 17 and 18, it can be seen that, according to principal component analysis, sake can be classified by the type of yeast used, just as with taste-related components. Furthermore, yeast X is characterized by various esters and higher alcohols (ethyl acetate, isobutanol, etc.), while yeast Y is characterized by 1-propanol. However, just as with taste-related components, it is difficult to derive conditions for distinguishing between the region of "fullness" and the region of "no fullness" on the score plot for aroma components.

[0089] Figure 19 shows a score plot created as a result of partial least squares discriminant analysis of the aroma component identification results. Figure 20 shows a loading plot created as a result of partial least squares discriminant analysis of the aroma component identification results. Note that in Figures 19 and 20, sake types for which evaluations differed among multiple evaluators (No. 3 and No. 6 in Figure 2, for which the "evaluation agreement" value was "R") were excluded from the analysis. Isobutyl acetate has been newly added to Figure 20.

[0090] In the score plot shown in Figure 19, the points for the "full" types of sake (Nos. 2, 5, and 7) are located near the first quadrant, and from the loading plot, it can be seen that the types classified near the first quadrant in the score plot are characterized by isobutanol, isobutyl acetate, and ethyl acetate.

[0091] Fig. 21 is a diagram showing the test statistics of the partial least squares discriminant analysis described with reference to Fig. 19 and Fig. 20. Fig. 21 shows the test statistics (p-values) for each of two components (isobutanol and isobutyl acetate). These two components are included in No. 1 to No. 8.

[0092] FIG. 21 shows statistics for a t-test and statistics for a u-test as test statistics. In this embodiment, the two types of components shown in FIG. 21 are selected as marker components. The device 100 stores information identifying the selected components in memory 200 in association with the taste "fullness." The selection criterion is that both the values ​​of the statistics for the t-test and the statistics for the u-test are 0.2 or less. Of these, isobutyl acetate is characterized by a banana-like ginjo aroma (a fruity aroma found in ginjo sake, which is made by using rice that has been thoroughly polished to remove much of the bran and then slowly fermenting the mash at low temperature).

[0093] Figure 22 shows the difference in the composition ratios of the two components shown in Figure 21 between swelled and non-swelled samples. In Figure 22, two graphs 61 and 62 are shown within a box 60. Graph 61 shows the ratio for isobutanol, and graph 62 shows the ratio for isobutyl acetate. Each of graphs 61 and 62 is a box plot. As with Figure 9, the vertical axis represents the peak area value of each component in the chromatogram. "No swell" represents three types of sake (No. 1, 4, 8) without swell (total of nine samples). "Swelled" represents three types of sake (No. 2, 5, 7) with swell (total of nine samples).

[0094] Graphs 61 and 62 show that both of the above two components are contained in larger amounts in the full-bodied sample than in the non-full-bodied sample. This suggests that whether a given type of sake has a full-bodied taste is influenced not by the amount of a single component, but by the amount of the above two components.

[0095] <Creating a trained model> The device 100 uses the training data 220 to perform a learning process for the estimation model 230. In the data set constituting the training data 220, each piece of data includes information for each sample. More specifically, each piece of data includes, for each sample, information representing the peak area in the chromatogram of each of the marker components (the above-mentioned two types of components) and the judgment result assigned to the target sample (whether or not there is a bulge). The peak area is an example of information regarding the amount of each component in the sample. As a result, when the peak area of ​​each of the marker components for the target object is input, the trained estimation model 230 uses the peak area of ​​each of the marker components to output a judgment result as to whether or not there is a bulge.

[0096] <Verdict> In this embodiment, as in the first embodiment, the device 100 may provide information about taste according to the flowchart shown in FIG.

[0097] Fig. 23 shows specific examples of the peak areas of the marker components and the determination results for two types of objects. As with Fig. 14, Fig. 23 shows the type of sake, sample number, determination results, score, and components for two types (No. 3 and No. 6 in Fig. 2). Note that Fig. 23 lists the two types of aroma components mentioned above as the types of components.

[0098] As shown in Figure 23, the same judgment result (no expansion) is derived for all three samples of sake No. 3. Furthermore, the same judgment result (expansion) is derived for all three samples of sake No. 6. In other words, when making a judgment using the trained estimation model 230, it is possible to derive a judgment result with a certain degree of reliability for types of sake for which evaluations by multiple evaluators are divided.

[0099] [Third embodiment] In the third embodiment, as in the first embodiment, the "fullness" of sake is used as an example of a specific taste. In the third embodiment, a determination result as to whether or not an object has a specific taste is obtained according to a predetermined criterion.

[0100] <Device configuration> Fig. 24 is a block diagram showing the configuration of a device 100A for providing taste information. In the device 100A shown in Fig. 24, reference data 250 is stored in memory 200. The reference data 250 is an example of reference ratio information, and represents the standard for whether or not the target sake has "fullness."

[0101] 24, the functions of analysis program 240 include a determination unit 243A. Determination unit 243A derives a determination result as to whether or not a target object has a specific taste, based on measurement data 210 and reference data 250.

[0102] The memory 200 stores information specifying the marker components selected as described in the first embodiment in association with the taste "fullness."

[0103] <Verdict> The reference data 250 defines the criteria for determining whether an object has a "bulge."

[0104] An example of the criteria is to define the ratio of disaccharides and malic acid to the total of 10 marker components selected using the results of partial least squares discriminant analysis in the first embodiment for the peak area in the chromatogram. More specifically, the ratio of disaccharides is 66 to 71%, and the ratio of malic acid is 4.0 to 5.0%.

[0105] In this case, the apparatus 100A calculates the sum of the peak areas of the 10 types of marker components from the measurement data of the object, and then calculates the ratio of the peak area of ​​disaccharides to that sum and the ratio of the peak area of ​​malic acid to that sum. If both of the calculated ratios are within the ranges specified by the above criteria, the apparatus 100A outputs a determination result that the object has "swelling." On the other hand, if at least one of the calculated ratios is outside the range specified by the above criteria, the apparatus 100A outputs a determination result that the object does not have "swelling."

[0106] The reference data 250 may define the criteria for determining that an object does not have a "bulge."

[0107] Another example of the criteria is that the ratio of disaccharides is 62-63% and the ratio of malic acid is 5.5-8.0% relative to the total of the 10 marker components. If both of the calculated two ratios are within the ranges specified in the criteria, the device 100A outputs a determination result that the object does not have "swelling." On the other hand, if at least one of the calculated two ratios is outside the range specified in the criteria, the device 100A outputs a determination result that the object has "swelling."

[0108] The names and values ​​of the components described above are merely examples, and may be changed as appropriate when the technology according to the present disclosure is implemented.

[0109] Fig. 25 is a flowchart of an example of processing performed by device 100 A. Compared to the processing shown in Fig. 13, the processing in Fig. 25 includes step S23A instead of step S23.

[0110] More specifically, the device 100A acquires (reads out) measurement data of the object in step S21, and acquires the peak area values ​​of the marker components in step S22. In addition to the peak area values, peak heights or component concentrations may also be acquired.

[0111] Then, in step S23A, the apparatus 100A uses the peak areas of the marker components obtained in step S22 and the criteria stored in the reference data 250 to obtain a determination result.

[0112] Then, in step S24, device 100A displays the determination result acquired in step S23A on display device 600. The determination result is displayed, for example, as screen 70 in Fig. 15. Then, device 100A ends the processing in Fig. 25.

[0113] [Fourth embodiment] Device 100 or device 100A can provide the information output in each of the first to third embodiments for each of a plurality of types of taste. Device 100 or device 100A may provide information for each of a plurality of types of taste from the user. Device 100 or device 100A may also accept selection of a type from the plurality of types of taste for which information is to be provided. Device 100 or device 100A provides information on the selected type of taste using information corresponding to the selected type.

[0114] In one implementation example, the memory 200 of the device 100 stores information identifying marker components identified for each of the multiple types and their reference ratio information. Furthermore, the estimation model 230 has undergone a learning process for each of the multiple types. That is, the memory 200 stores, as parameters for the estimation model 230, parameter sets resulting from the learning process for each of the multiple types.

[0115] In one implementation example, the memory 200 of the device 100A stores information identifying the marker components identified for each of the multiple types and reference ratio information for the components. The memory 200 also stores the reference ratio information for each of the multiple types as reference data 250.

[0116] Fig. 26 is a flowchart of an example of processing performed to provide information related to taste in the fourth embodiment. As the processing in the fourth embodiment, a modified example of the processing (Fig. 25) described in the third embodiment will be described.

[0117] The process of FIG. 26 further includes step S21X compared to the process of FIG. 25. More specifically, after device 100A acquires measurement data of the object in step S21, control proceeds to step S21X. In step S21X, device 100A accepts a selection of a target type from a plurality of taste types from the user. Then, device 100A selects reference ratio information for the accepted type from reference data 250. Thereafter, device 100A performs control from step S22 onwards for the selected type of taste.

[0118] [Aspect] It will be appreciated by those skilled in the art that the exemplary embodiments described above are examples of the following aspects.

[0119] (Clause 1) A taste information providing method according to one embodiment is a method for providing taste information regarding a specific taste, and may include a step of acquiring object proportion information regarding the proportion of two or more types of components in an object, the two or more types of components being associated with the specific taste, and a step of deriving a judgment result of the specific taste for the object based on the object proportion information and reference proportion information regarding the proportion of the two or more types of components, and a step of outputting the judgment result.

[0120] According to the taste information providing method described in paragraph 1, objective and highly accurate information is provided regarding the evaluation of taste that is realized by combining multiple types of ingredients in a complex manner.

[0121] (Clause 2) In the taste information providing method described in paragraph 1, the two or more types of components may include a first component and a second component, and the standard ratio information may specify standards for the ratio of the amount of the first component to the total amount of the two or more types of components, and the ratio of the amount of the second component to the total amount of the two or more types of components.

[0122] According to the taste information providing method described in paragraph 2, more specific information is provided regarding the evaluation of the taste achieved by the specific ratio of the first component and the second component.

[0123] (Item 3) In the taste information providing method described in Item 2, the first component may be a disaccharide, and the second component may be malic acid.

[0124] According to the taste information providing method described in item 3, more specific information regarding the evaluation of tastes containing disaccharides and malic acid is provided.

[0125] (4) In the taste information providing method described in any one of paragraphs 1 to 3, the step of deriving the judgment result may include deriving a judgment result as to whether or not the person has the specific taste.

[0126] According to the taste information providing method described in paragraph 4, specific information, such as a determination result as to whether or not a person has a specific taste, can be provided regarding the taste evaluation.

[0127] (Item 5) The taste information providing method according to any one of items 1 to 4 may further include a step of selecting the specific taste from a plurality of tastes.

[0128] According to the taste information providing method described in paragraph 5, the user can selectively obtain only the desired type of information from among a plurality of types of taste.

[0129] (Clause 6) The taste information providing method described in any one of clauses 1 to 5 includes a step of acquiring the amount of each of two or more types of components in the object, and the step of acquiring the object proportion information may include acquiring the object proportion information based on the amount of the components.

[0130] According to the taste information providing method described in paragraph 6, the object proportion information is calculated so as to more accurately represent the proportion of the object.

[0131] (Clause 7) The taste information providing method described in paragraph 2 may further include a step of calculating, in the information regarding the amount of each of the two or more types of components, the ratio of the amount of each component to the total amount of the two or more types of components, and a step of outputting the ratio of the amount of each component.

[0132] According to the taste information providing method described in paragraph 7, the basis for the judgment result can be provided by displaying the ratio of components that constitute a specific taste.

[0133] (Item 8) A taste information providing method according to one embodiment is a method for providing taste information relating to a specific taste, comprising a step of acquiring information relating to the amount of each of two or more types of components in an object, the two or more types of components being associated with the specific taste, and may further comprise a step of calculating, for the information relating to the amount of each of the two or more types of components, a ratio of the amount of each component to the total amount of the two or more types of components, and a step of outputting the ratio of the amount of each component.

[0134] According to the taste information providing method described in paragraph 8, objective and highly accurate information is provided regarding the evaluation of taste, which is composed of the ratio of complex components that cannot be evaluated solely by the amount of the characteristic components of the object.

[0135] (Clause 9) In the taste information providing method described in clause 7 or clause 8, the step of outputting the proportion of the amount of each component may further include outputting the proportion of each component in a sample having the specific taste and the proportion of each component in a sample not having the specific taste.

[0136] According to the taste information providing method described in paragraph 9, the user is provided with more information to judge the specific taste of an object.

[0137] (Item 10) In the taste information providing method described in any one of Items 1 to 9, the two or more types of components may be selected based on test statistics calculated for each component of one or more samples having the specific taste and one or more samples not having the specific taste, in order to analyze the difference between the one or more samples having the specific taste and one or more samples not having the specific taste using partial least squares discriminant analysis.

[0138] According to the taste information providing method described in item 10, components that characterize a particular taste are selected as the two or more types of components.

[0139] (Item 11) In the taste information providing method described in items 1 to 8, the specific taste may be fullness of sake.

[0140] According to the taste information providing method described in paragraph 11, objective and highly accurate information is provided regarding the taste "fullness" that is mentioned about sake.

[0141] (Item 12) In the taste information providing method described in Item 11, the two or more types of components may include at least a portion of the group consisting of cytidine, histamine, adenosine, disaccharides, ornithine, adenine, malic acid, citric acid, isocitric acid, and glyoxylic acid.

[0142] According to the taste information providing method described in paragraph 12, more accurate information on the "fullness" of taste is provided.

[0143] (Item 13) A program according to one embodiment may be a program for providing taste information, and when executed by a processor of a computer, may cause the computer to implement the taste information providing method described in any one of items 1 to 12.

[0144] No. 1 3 According to the program described in the paragraph, the program provides objective and highly accurate information regarding taste evaluation.

[0145] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present disclosure is defined by the claims, not by the description of the above-described embodiments, and is intended to include all modifications within the meaning and scope of the claims. Furthermore, it is intended that each technique in the embodiments can be implemented alone or, if necessary, in combination with other techniques in the embodiments to the extent possible. [Explanation of symbols]

[0146] 11,12,13,51,52,53 Area, 31,32,33,34,40X,41,42,61,62 Graph, 40,45,70 Screen, 71 Column, 100,100A Device, 101 Processor, 200 Memory, 210 Measurement data, 220 Training data, 230 Estimation model, 240 Analysis program, 241 Data creation unit, 242 Model creation unit, 243,243A Judgment unit, 244 Data analysis unit, 245 Data processing unit, 246 Output unit, 250 Reference data, 300 Input / output port, 400 Mouse, 500 Keyboard, 600 Display device.

Claims

1. A method for providing taste information regarding a particular taste, comprising: acquiring object proportion information relating to proportions of two or more types of components in the object; the two or more types of ingredients are associated with the particular taste; deriving a judgment result of the specific taste for the object based on the object ratio information and reference ratio information regarding the ratios of the two or more types of components; and outputting the determination result. the two or more types of components include a first component and a second component, A taste information providing method in which the standard ratio information specifies standards for the ratio of the amount of the first component to the total amount of the two or more types of components, and the ratio of the amount of the second component to the total amount of the two or more types of components.

2. The taste information providing method according to claim 1 , wherein the first component is a disaccharide and the second component is malic acid.

3. The taste information providing method according to claim 1 , wherein the step of deriving a determination result includes deriving a determination result as to whether or not the person has the specific taste.

4. The taste information providing method according to claim 1 , further comprising the step of selecting the specific taste from a plurality of tastes.

5. acquiring amounts of two or more types of components in the object; The taste information providing method according to claim 1 , wherein the step of acquiring the object ratio information includes acquiring the object ratio information based on the component amounts.

6. calculating a ratio of the amount of each component to the total amount of the two or more types of components based on information about the amounts of each of the two or more types of components; The taste information providing method according to claim 1 , further comprising the step of outputting the ratio of the amount of each component.

7. The taste information providing method according to claim 6 , wherein the step of outputting the proportion of the amount of each component displays the proportion of the amount of each component in a pie chart.

8. The taste information providing method of claim 6, wherein the step of outputting the proportion of the amount of each component further includes outputting the proportion of each component in a sample having the specific taste and the proportion of each component in a sample not having the specific taste.

9. 1. A method for providing taste information regarding a particular taste, comprising: obtaining information about the amounts of each of two or more types of components in the object; Only components associated with the specific taste are selected as the two or more types of components, calculating a ratio of the amount of each component to the total amount of the two or more types of components based on information about the amounts of each of the two or more types of components; The taste information providing method further comprises a step of displaying the proportion of the amount of each component.

10. The taste information providing method according to claim 9 , wherein the step of displaying the proportion of the amount of each component displays the proportion of the amount of each component in a pie chart.

11. The taste information providing method according to claim 9 , wherein the step of displaying the proportion of the amount of each component displays the proportion of the amount of each component as a numerical value.

12. The taste information providing method of claim 9, wherein the step of displaying the proportion of the amount of each component comprises displaying a pie chart showing the proportion of each component in a sample having the specific taste and a pie chart showing the proportion of each component in a sample not having the specific taste in a comparable manner.

13. A method for providing taste information regarding a particular taste, comprising: acquiring object proportion information relating to proportions of two or more types of components in the object; the two or more types of ingredients are associated with the particular taste; deriving a judgment result of the specific taste for the object based on the object ratio information and reference ratio information regarding the ratios of the two or more types of components; and outputting the determination result. A taste information providing method in which the two or more types of components are selected based on test statistics calculated for each component of one or more samples having the specific taste and one or more samples not having the specific taste, in order to analyze the differences between the one or more samples having the specific taste and one or more samples not having the specific taste using partial least squares discriminant analysis.

14. The taste information providing method according to claim 1 , wherein the specific taste includes the fullness of sake.

15. The taste information providing method of claim 14, wherein the two or more types of components include at least a portion of the group consisting of cytidine, histamine, adenosine, disaccharides, ornithine, adenine, malic acid, citric acid, isocitric acid, and glyoxylic acid.

16. A program for providing taste information, the program being executed by a processor of a computer to cause the computer to implement the taste information providing method according to claim 1.

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