Method for predicting target function of substance

Abstract

To provide a technique for predicting the target function of a substance (this is the function of enhancing or suppressing the flavor function of a food), and to provide use thereof.SOLUTION: By utilizing a predictive model constructed based on data reflecting differences in receptor activation profiles among multiple foods including food variation groups, and data indicating the flavor functions of each of the multiple foods, the target function of a substance (which is a function that enhances or suppresses the flavor function of a food) is predicted based on the receptor activation profile of the substance.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a technology for predicting the target function of a substance (that is, the function of enhancing or suppressing the flavor function of food) and use of the same. [Background technology]

[0002] Flavor functions such as aroma and taste are important factors that affect the preference of foods, cosmetics, etc. Therefore, the technology of screening components necessary to reproduce a specific flavor function and the technology of combining multiple components to reproduce a specific flavor function are industrially important technologies for developing foods, cosmetics, etc.

[0003] Conventionally, screening of ingredients that affect specific flavor functions has been carried out by humans evaluating the flavor functions of test substances through sensory tests, but sensory tests have problems such as the need to train experts who can evaluate flavor functions and low throughput.

[0004] In recent years, methods have been reported for screening substances that exhibit flavor functions such as specific aromas and flavors, using responses of receptors such as olfactory receptors and gustatory receptors as indicators (Patent Documents 1 and 2, etc.). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2019-037197 [Patent Document 2] Patent Publication 2018-014999 Summary of the Invention [Problem to be solved by the invention]

[0006] An objective of the present invention is to provide a technology for predicting the target function of a substance (that is, the function of enhancing or suppressing the flavor function of food) and use of the same. [Means for solving the problem]

[0007] As a result of intensive research to solve the above-mentioned problems, the inventors discovered that by utilizing a predictive model constructed based on data reflecting the differences in receptor activation profiles between multiple variations that make up a food variation group and data indicating the flavor functions of each of the multiple variations, it is possible to predict the target function of a substance (which is the function of enhancing or suppressing the flavor function of a food) based on the receptor activation profile of the substance, and thus completed the present invention.

[0008] That is, the present invention can be exemplified as follows. [1] A method for producing a predictive model, comprising: The predictive model is a model for predicting a target function of a test substance based on a receptor activation profile of the test substance; The target function is a function of enhancing or suppressing the flavor function of a target food; The method further comprises constructing the predictive model based on the following data (A) and (B): (A) Data reflecting differences in receptor activation profiles among the multiple foods that make up the control food group; (B) data indicative of the flavor function of each of the plurality of foods; Including, the control food group comprises a variation group of the target food group; the receptor activation profile of the test substance is a receptor activation profile for a first receptor; The receptor activation profile of (A) is a receptor activation profile for a second receptor, The method, wherein the second receptor comprises the first receptor. [2] The method (specifically, described in [1]) in which the step is a step of constructing the prediction model by regression analysis using the data (A) as explanatory variables and the data (B) as a response variable. [3] The method (specifically described in [2]) wherein variables are eliminated in the regression analysis. [4] The method (specifically described in [3]) in which the reduction of variables is the reduction of explanatory variables. [5] The method (specifically described in [3] or [4]) wherein the reduction of variables is performed by regularization. [6] The method (specifically described in [5]) wherein the regularization is performed by Lasso regression. [7] The method (specifically, the method described in any one of [1] to [6]) above, wherein cross-validation is performed in the regression analysis. [8] The method (specifically, the method described in any one of [1] to [7]) wherein the prediction model includes a regression equation that outputs a predicted value of the target function based on the receptor activation profile of the test substance. [9] The method (specifically described in [8]) wherein the regression equation is a linear regression equation passing through the origin.

[10] The method (specifically, any one of [1] to [9]) wherein the variation group is a group consisting of multiple foods of the same type as the food, but with different manufacturing ingredients, manufacturing conditions, and / or manufacturers.

[11] The method (specifically, the method described in any one of [1] to [9]) above, wherein the variation group is the following group (1) or (2): (1) A group consisting of two or more variations of any one of the target food products, each of which has increased amounts of two or more ingredients; (2) A group consisting of two or more products of the targeted food that are manufactured by different manufacturers.

[12] The method (specifically, the method described in any one of [1] to

[11] ) wherein the data (A) is the following data (A1) or (A2): (A1) a receptor activation profile for the second receptor of a component whose content differs between foods selected from the control food group; (A2) A receptor activation profile for the second receptor for each of the multiple foods constituting the control food group.

[13] The control food group is composed of the variation group of group (1), The method (specifically described in

[11] ) wherein the data (A) is a receptor activation profile for the second receptor for each of the two or more components.

[14] The control food group includes the variation group of group (3), The method (specifically described in

[11] ) wherein the data (A) is a receptor activation profile for the second receptor for each of a plurality of foods constituting the control food group.

[15] The method (specifically, the method described in any one of [1] to

[14] ) above, wherein the number of variations constituting the variation group is 10 or more.

[16] The method (specifically, any one of [1] to

[15] ) wherein the ratio of the target food in the control food group is 80% or more.

[17] The method (specifically, the method according to any one of [1] to

[16] ) wherein the number of the first receptors is 5 or more.

[18] The method (specifically, the method according to any one of [1] to

[17] ) wherein the number of the second receptors is 300 or more.

[19] The method (specifically, the method described in any one of [1] to

[18] ) above, wherein the second receptor is selected from the group consisting of an olfactory receptor and a gustatory receptor.

[20] 50% or more of the total number of the second receptors are selected from the group consisting of OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D5, OR1E1, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR2A1, OR2A2, OR2A4, OR2A5, OR2A12, OR2A14, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P, OR2AK 2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C16, OR4C46, OR 4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K2, OR4K5, OR4K13, OR4 K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR5A1, OR5A2, OR5AC2, OR5AK2, OR5AK3P, OR5AN1, OR5AP2, OR5AR1, OR5AS1, OR5AU1, OR5B2, OR5B3, OR5B12, OR5B17, OR5B21, OR5C1, OR5D13, OR5D14, OR5D16, OR5D18, OR5F1, OR5H1, OR5H2, OR5H6, OR5H14,OR5I1、OR5J2、OR5K1、OR5K3、OR5K4、OR5L2、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1、OR5W2、OR6A2、OR6B1、OR6B2、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G2、OR8G5、 , OR8H3, OR8I2, OR8J1, OR8J3, OR8K1, OR8K3, OR8K5, OR8S1, OR8U1, OR9A4, OR9G1, OR9G4, OR9I1, OR9K2, OR9Q1, OR9Q2, OR10A3, OR10A4, OR10A5, OR10A6, OR10A7, OR10AD1, OR10AG1, OR10C1, OR10D3, OR10D4P, OR10G2, OR10G3, OR10G4, OR10G6, OR10G7, OR10G9, OR10H2, OR10H4, OR10J1, OR1 0J3, OR10J5, OR10K1, OR10K2, OR10P1, OR10Q1, OR10R2, OR10S1, OR10T2, OR10V1, OR10W1, OR10X1, OR10Z1, OR11A1, OR11G2, OR11H4, OR11H6, OR 11H12, OR11L1, OR12D2, OR12D3, OR13A1, OR13C2, OR13C3, OR13C4, OR13C8, OR13D1, OR13F1, OR13G1, OR13H1, OR13J1, OR14A2, OR14A16, OR14C3 6, OR14I1, OR14J1, OR14K1, OR14L1P, OR51A1P, OR51A4, OR51A7, OR51B2, OR51B4, OR51B5, OR51B6, OR51D1, OR51E1, OR51E2, OR51F1, OR51F2, OR 51F5P, OR51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, The method (specifically, the method described in any one of [1] to

[19] ) above, wherein the nucleotide sequence is selected from the group consisting of OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52E8, OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4. [twenty one] The second receptor is selected from the group consisting of OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D5, OR1E1, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR2A1, OR2A2, OR2A4, OR2A5, OR2A12, OR2A14, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P, OR2AK2, and OR2AP1. , OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR 2L13, OR2M2, OR2M3, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR 3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C16, OR4C46, OR4D1, OR4 D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K2, OR4K5, OR4K13, OR4K14, OR4 K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR5A1, OR5A2, OR5AC2, OR5AK2, OR5AK3P, OR5AN1, OR5AP2, OR5AR1, OR5AS1, OR5AU1, OR5B2, OR5B3, OR5B12, OR5B17, OR5B21, OR5C1, OR5D13, OR5D14, OR5D16, OR5D18, OR5F1, OR5H1, OR5H2, OR5H6, OR5H14, OR5I1,OR5J2、OR5K1、OR5K3、OR5K4、OR5L2、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1、OR5W2、OR6A2、OR6B1、OR6B2、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G2、OR8G5、OR8H3、OR8I2、OR8J1、OR8J3、OR8K1、OR8K3、OR8K5、OR8S1、OR8U1、OR9A4、OR9G1、OR9G4、OR9I1、OR9K2、OR9Q1、OR9Q2、OR10A3、OR10A4、OR10A5、OR10A6、OR10A7、OR10AD1、OR10AG1、OR10C1、OR10D3、OR10D4P、OR10G2、OR10G3、OR10G4、OR10G6、OR10G7、OR10G9、OR10H2、OR10H4、OR10J1、OR10J3、OR10J5、OR10K1、OR10K2、OR10P1、OR10Q1、OR10R2、OR10S1、OR10T2、OR10V1、OR10W1、OR10X1、OR10Z1、OR11A1、OR11G2、OR11H4、OR11H6、OR11H12、OR11L1、OR12D2、OR12D3、OR13A1、OR13C2、OR13C3、OR13C4、OR13C8、OR13D1、OR13F1、OR13G1、OR13H1、OR13J1、OR14A2、OR14A16、OR14C36、OR14I1、OR14J1、OR14K1、OR14L1P、OR51A1P、OR51A4、OR51A7、OR51B2、OR51B4、OR51B5、OR51B6、OR51D1、OR51E1、OR51E2, OR51F1, OR51F2, OR51F5P, OR51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, O R51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52E8, OR52 The method (specifically, the method described in any one of [1] to

[20] ) includes 50% or more of OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4. [twenty two] The method (specifically, the method described in any one of [1] to

[21] ) above, wherein the second receptor is a human receptor. [twenty three] The method (specifically, the method described in any one of [1] to

[22] ) above, wherein the target flavor function is a function of presenting retronasal aroma, taste, or mouthfeel. [twenty four] The method (specifically, the method described in any one of [1] to

[23] ) comprising a step of acquiring the data (A) and / or a step of acquiring the data (B). [twenty five] A method for predicting a target function of a test substance, comprising: The target function is a function of enhancing or suppressing the flavor function of a target food; The method comprises predicting a target function of a test substance based on a receptor activation profile of the test substance using a prediction model produced by the method described above (specifically, any one of [1] to

[24] ). Includes The method, wherein the receptor activation profile of the test substance is a receptor activation profile for the first receptor.

[26] A method for screening a substance having a target function, comprising the steps of: The target function is a function of enhancing or suppressing the flavor function of a target food; The method comprises predicting a target function of a test substance based on a receptor activation profile of the test substance using a prediction model produced by the method described above (specifically, any one of [1] to

[24] ). Includes the receptor activation profile of the test substance is a receptor activation profile for the first receptor; When the test substance is predicted to have the target function, the test substance is selected as a candidate substance having the target function.

[27] The method (specifically described in

[25] or

[26] ) further comprises a step of evaluating the result of the prediction.

[28] The method (specifically, the method described in

[27] ) above, wherein the evaluation is carried out by sensory evaluation. Effect of the Invention

[0009] According to the present invention, it is possible to predict the target function of a substance (this is the function of enhancing or suppressing the flavor function of a food). Also, according to the present invention, it is possible to screen for a substance having a target function (this is the function of enhancing or suppressing the flavor function of a food). In one aspect, according to the present invention, it is possible to screen for a substance that does not have the above flavor function itself but enhances the above flavor function of a food when blended with the food. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present invention will be described in detail below.

[0011] <1> The present invention relates to a method for producing a predictive model. The method for producing a predictive model of the present invention is a method for producing a predictive model that predicts the target function of a substance.

[0012] Specifically, the method for producing a prediction model of the present invention is a method for producing a prediction model, The predictive model is a model for predicting a target function of a test substance based on a receptor activation profile of the test substance; The target function is a function of enhancing or suppressing the flavor function of a target food; The method further comprises constructing the predictive model based on the following data (A) and (B): (A) Data reflecting differences in receptor activation profiles among the multiple foods that make up the control food group; (B) data indicative of the flavor function of each of the plurality of foods; Including, the control food group comprises a variation group of the target food group; the receptor activation profile of the test substance is a receptor activation profile for a first receptor; The receptor activation profile of (A) is a receptor activation profile for a second receptor, The method may be such that the second receptor comprises the first receptor.

[0013] Predicting the target function of a test substance based on the receptor activation profile of the test substance is also referred to simply as "prediction" hereinafter. A model predicting the target function of a test substance based on the receptor activation profile of the test substance is also referred to simply as "prediction model" hereinafter. The above process is also referred to as "prediction model construction process." The flavor function is also referred to as "target flavor function."

[0014] <1-1> Prediction model "Predictive model" refers to a model that predicts the target function of a test substance based on the receptor activation profile of the test substance. The predictive model can be used for prediction. Specifically, the predictive model can be used for prediction in the manner described in the prediction method of the present invention.

[0015] The prediction model is not particularly limited as long as it enables prediction with a desired accuracy. The prediction model may be one that outputs a conclusion that serves as an index of prediction based on the receptor activation profile of the test substance. Examples of conclusions that serve as an index of prediction include a predicted value of the target function of the test substance. That is, the prediction model may be one that outputs, for example, a predicted value of the target function of the test substance based on the receptor activation profile of the test substance. The predicted value of the target function of the test substance can be output, for example, by a regression equation. That is, the prediction model may include a regression equation. The regression equation may be one that outputs a predicted value of the target function of the test substance based on the receptor activation profile of the test substance. The regression equation may be, for example, a linear regression equation. The regression equation may be, for example, a regression equation that passes through the origin. Specifically, the regression equation may be, for example, a linear regression equation that passes through the origin. "Based on the receptor activation profile of the test substance" may mean that the receptor activation profile of the test substance is substituted as a variable into the prediction model (for example, a regression equation). That is, the prediction model (eg, regression equation) may specifically output a predicted value of the target function of a test substance using, for example, the receptor activation profile of the test substance as a variable.

[0016] <1-2> Target function The term "target function" refers to a function to be predicted in the present invention. Specifically, the term "target function" refers to a function of enhancing or suppressing the target flavor function of a target food. That is, the "target function" of a certain substance may refer to a function of the substance that enhances or suppresses the target flavor function of the target food when the substance is blended into the target food. For example, the "target function" of a test substance may refer to a function of the test substance that enhances or suppresses the target flavor function of the target food when the test substance is blended into the target food. The target function may be, in particular, a function that enhances the target flavor function of the target food. A substance having a target function may or may not have a target flavor function by itself, for example. That is, a substance having a target function may, for example, not have a target flavor function by itself, but enhance the target flavor function of the target food when blended into the target food.

[0017] The term "target food" as used herein means a food whose target flavor function is to be enhanced or suppressed. The type of target food is not particularly limited. That is, the target food may be any food. Foods include beverages and seasonings. Foods include beverages such as soft drinks, lactic acid bacteria beverages, alcoholic beverages, and soups; noodle soups such as udon soup, soba soup, somen soup, ramen soup, champon soup, and pasta sauce; broths such as beef stock, pork stock, chicken stock, Japanese-style stock, and chicken stock; sauces such as soy sauce, fish sauce, Chinese sauce, oyster sauce, cheese sauce, tomato sauce, white sauce, demi-glace sauce, curry sauce, Genoa sauce, chili sauce, Tabasco sauce, and dressing; and other seasonings such as miso, mayonnaise, tomato ketchup, and creaming powder. "Soft drinks" may mean non-alcoholic beverages (drinks with an alcohol concentration of less than 1%) excluding milk and dairy products. Soft drinks include beverages containing fruit juice, beverages containing vegetable juice, tea beverages, black tea beverages, coffee beverages, carbonated beverages, and sports drinks. Examples of the soup include egg soup, seaweed soup, shark fin soup, Chinese soup, consommé soup, curry-flavored soup, potage soup, corn soup, dal soup, tom yum goong, clear soup, and miso soup. Examples of the food include, in particular, stock and sauce. A certain food may be an ingredient of another food. That is, examples of the food include foods made from the above-exemplified foods (e.g., foods containing the above-exemplified foods). The food may be provided in a form that can be eaten as is, or may be provided in a form that requires preparation before or at the time of eating, such as a concentrated product or a dried product.

[0018] The term "target flavor function" refers to a flavor function to be enhanced or suppressed in the present invention. "Flavor function" refers to a function that presents a flavor. The flavor presented by the target flavor function is also called "target flavor". In other words, the target flavor function is a function that presents a target flavor. The type of target flavor is not particularly limited. In other words, the target flavor may be any flavor. Flavors include sensations felt in the oral cavity and / or nasal cavity when eating a target object (e.g., food). Specific examples of flavors include aroma, taste, and mouthfeel. Examples of aromas include retronasal aroma. "Retronasal aroma" refers to the aroma that escapes to the nose after a food is held in the oral cavity.

[0019] Aromas (which may be retronasal aromas) include absinthe, acacia, acai, acerola, acetic, acetone, acidic, acorn, acrylate, agarwood, alcoholic, aldehydic, alfalfa, algae, alliaceous, allspice, almond, almond bitter almond, almond roasted almond, almond toasted almond, amber, ambergris, ambrette, ammoniacal, angelica, animal, anise, anisic, apple, apple cooked apple, apple dried apple, apple green apple, apple red apple, apple skin, apricot, aromatic, arrack, artichoke, asafetida, asparagus, astringent, autumn, avocado, bacon, baked, balsamic, banana, banana peel, banana ripe banana, banana unripe banana, barley roasted barley, basil, bay, bean green bean, beany, beef juice, beefy, beefy roasted beefy, beer, beeswax, benzoin, bergamot, berry, berry ripe berry, bitter, blackberry, bloody, blueberry, bois de rose, boronia, bouillon, boysenberry, brandy, bread baked, bread crust, bread rye bread, bready, broccoli, brothy, brown, bubble gum, buchu, burnt, butterrancid、buttermilk、butterscotch、buttery、cabbage、calamus、camphoreous、cananga、candy、cantaloupe、capers、caramellic、caraway、cardamom、carnation、carrot、carrot seed、carvone、cascarilla、cashew、cassia、castoreum、catty、cauliflower、cedar、cedarwood、celery、cereal、chamomile、charred、cheesy、cheesy bleu cheese、cheesy cheddar cheese、cheesy feta cheese、cheesy gorgonzola cheese、cheesy gouda cheese、cheesy limburger cheese、cheesy parmesan cheese、cheesy roquefort cheese、chemical、cherry、cherry maraschino cherry、chervil 、chestnut、chicken、chicken coup、chicken fat、chicken roasted chicken、chicory、chive、chocolate、chocolate dark chocolate、chocolate white chocolate、chrysanthemum、cider、cilantro、ciltrano、cinnamon、cinnamyl、cistus、citronella、citrus、citrus peel、citrus rind、civet、clam、clean、cloth laundered cloth、clove、clover、cocoa、coconut、coffee、coffee roasted coffee、cognac、cologne、cooked、cookie、cooling、copaiba、coriander、corn、corn chip、cornmeal、cornmint、cortex、costus、cotton candy、coumarinic、cranberry、creamy、cubeb、cucumber、cucumber skin、cumin、currant black currant、currant bud black currant bud、currant red currant、curry、custard、cyclamen、cypress、dairy、date、davana、deertongue、dewy、dill、dirty、dragon fruit、dry、durian、dusty、earthy、egg nog、egg yolk、eggy、elderberry、elderflower、elemi、estery、ethereal、eucalyptus、fatty、fecal、fennel、fenugreek、fermented、fig、filbert、fir needle、fishy、fleshy、floral、foliage、forest、fougere、frankincense、freesia、fresh、fresh outdoors、fried、fruit dried fruit、fruit overripe fruit、fruit ripe fruit、fruittropical fruit、fruity、fudge、fungal、fusel、galanga、galbanum、gardenia、garlic、gasoline、gassy、genet、geranium、ginger、ginseng、goaty、goji berry、gooseberry、gourmand、graham cracker、grain、grain toasted grain、grape、grape skin、grapefruit、grapefruit peel、grassy、gravy、greasy、green、grilled、guaiacol、guaiacwood、guava、hairy、ham、harsh、hawthorn、hay、hay new mown hay、hazelnut、hazelnut roasted hazelnut、heather、heliotrope、herbal、hibiscus、honey、honeydew、honeysuckle、hops、horehound、horseradish、huckleberry、humus、hyacinth、hyssop、immortelle、incense、jackfruit、jammy、jasmin、jonquil、juicy、juicy fruit、juniper、ketonic、kimchi、kiwi、kokumi、kumquat、labdanum、lachrymatory、lactonic、lamb、lard、lavandin、lavender、lavender spike lavender、leafy、leathery、leek、lemon、lemon peel、lemongrass、lettuce、licorice、licorice black licorice、lilac、lily、lily of the valley、lime、lindenflower、lingonberry、liver、lobster、loganberry、lovage、lychee、macadamia、mace、magnolia、mahogany、malty、mandarin、mango、maple、marigold、marine、marjoram、marshmallow、marzipan、mastic、meaty、meaty roasted meaty、medicinal、melon、melon rind、melon unripe melon、mentholic、metallic、milky、mimosa、minty、molasses、moldy、mossy、muguet、mulberry、mushroom、musk、mustard、musty、mutton、myrrh、naphthyl、narcissus、nasturtium、natural、neroli、noni fruit、nut flesh、nut skin、nutmeg、nutty、oakmoss、oatmeal、oats、ocean、oily、onion、onion cooked onion、onion green onion、opoponax、orange、orange bitter orange、orange peel、orange rind、orangeflower、orchid、oriental、origanum、orris、osmanthus、oyster、ozone、painty、palmarosa、papaya、paper、parsley、passion fruit、patchouli、pea green pea、peach、peanut、peanut butter、peanut roasted peanut、pear、pear skin、pecan、peely、pennyroyal、peony、pepper bell pepper、pepper black pepper、peppermint、peppery、peru balsam、petal、petitgrain、petroleum、phenolic、pimenta、pine、pineapple、pistachio、plastic、plum、plumskin、pomegranate、popcorn、pork、potato、potato baked potato、potato chip、potato raw potato、powdery、praline、privet、privetblossom、prune、pulpy、pumpkin、pungent、quince、radish、rain、raisin、rancid、raspberry、raw、reseda、resinous、rhubarb、rindy、ripe、roasted、root beer、rooty、rose、rose dried rose、rose red rose、rose tea rose、rose white rose、rosemary、rubbery、rue、rummy、saffron、sage、sage clary sage、salmon、salty、sandalwood、sandy、sappy、sarsaparilla、sassafrass、sauerkraut、sausage、sausage smoked sausage、savory、sawdust、scallion、seafood、seashore、seaweed、seedy、sesame、sharp、shellfish、shrimp、skunk、smoky、soapy、soft、solvent、soup、sour、spearmint、spicy、spinach、spruce、starchy、starfruit、storax、strawberry、stringent、styrene、sugar、sugar brown sugar、sugar burnt sugar、sulfurous、sweaty、sweet、sweet pea、taco、tagette、tallow、tamarind、tangerine、tansy、tarragon、tart、tea、tea black tea、tea green tea、tea rooibos tea、tea white tea、tequila、terpenic、thujonic、thyme、toasted、tobacco、toffee、tolu balsam、tomato、tomatoleaf、tonka、tropical、truffle、tuberose、tuna、turkey、turmeric、turnup、tutti frutti、umami、urine、valerian root、vanilla、vegetable、verbena、vetiver、vinegar、violet、violet leaf、walnut、warm、wasabi、watercress、watermelon、watermelon rind、watery、waxy、weedy、wet、whiskey、winey、wintergreen、woody、woody burnt wood、woody oak wood、woody old wood、wormwood、yeasty、ylang、yogurt、yuzu、zedoary、zesty、bark、birch bark、blood、raw meat、burnt candle、burnt milk、burnt pepper、burnt rubber、cadaverous (dead animal)、cardboard、cat urine、chalky、cleaning fluid、cooked vegetables、cork、creosote、crushed grass、crushed weeds、dirty linen、disinfectant、carbolic、fermented (rotten) fruit、fragrant、fresh green vegetable、fresh tobacco smoke、fried chicken , heavy, household gas, kerosene, kippery (smoked fish), laurel leaves, light, mothballs, mouse, nail polish remover, new rubber, peanut butter, perfumery, putrid, four, decaye, rope, seasoning (for meat), seminal, sperm-like, sewer, sickening, sooty, sour milk, stale, stale tobacco smoke, tab, tea leaves, turpentine (pine oil), varnish, wet paper, wet wool, and wet dog.

[0020] The tastes include sweet taste, salty taste, sour taste, bitter taste, umami taste, kokumi taste, and fatty taste.

[0021] The mouthfeel is thick and astringent.

[0022] The target flavor function may or may not be a flavor function possessed by the target food. In other words, "enhancing the target flavor function" is not limited to enhancing the target flavor function possessed by the target food, but also includes imparting a target flavor function that the target food does not possess to the target food. "Suppressing the target flavor function" means suppressing the target flavor function possessed by the target food, and also includes the case where the target flavor function is completely lost in the target food.

[0023] <1-3> Test substance In the present invention, the term "test substance" refers to a substance that is the subject of prediction of a target function. In other words, the term "test substance" refers to a substance that is used as a candidate for a substance having a target function in a method for screening a substance having a target function.

[0024] The test substance is not particularly limited as long as its receptor activation profile can be utilized. The test substance may be a single component (i.e., a pure substance) or a combination of two or more components (i.e., a mixture). When the test substance is a mixture, the number of types and composition ratio of the components constituting the mixture are not particularly limited. The test substance may be a known substance or a new substance. The test substance may be a natural product or an artificial product. The test substance may be, for example, a compound library created using combinatorial chemistry technology. The test substance may be an aroma component, a taste component, or various other food components. Specific examples of the test substance include alcohols, ketones, aldehydes, ethers, esters, hydrocarbons, sugars, organic acids, nucleic acids, amino acids, peptides, and various other organic or inorganic components. In addition, the test substance may in particular be an existing food additive. The term "existing food additive" means a substance that has already been approved for use as a food additive. The receptor activation profile of the test substance may be known or may not be known. When the receptor activation profile of the test substance is not known, the receptor activation profile of the test substance may be appropriately obtained before carrying out the prediction. The receptor activation profile of the test substance may be obtained, for example, by a method for obtaining a receptor activation profile of a substance described below.

[0025] The term "receptor activation profile" refers to data showing the presence or absence or the degree of the property of activating or inactivating a receptor. In other words, the "receptor activation profile" of a certain substance refers to data showing whether the substance activates or inactivates a receptor, or whether the substance activates or inactivates a receptor. The term "receptor activation profile" may refer to data showing the degree to which a substance activates or inactivates the body. Also, the term "receptor activation profile" of a substance for two or more receptors may refer to the pattern of which receptors the substance activates or inactivates and which receptors it does not activate or inactivate for those two or more receptors, or the pattern of the degree to which the substance activates or inactivates each receptor. In the explanation of the receptor activation profile, "substance" may be read as "object," "food," "ingredient," or the like.

[0026] The receptor activation profile of the test substance is the receptor activation profile for the first receptor. That is, the "first receptor" refers to the receptor used in a prediction model (e.g., a regression equation). The "receptor is used in a prediction model (e.g., a regression equation)" may mean that the receptor activation profile for the receptor is substituted as a variable into the prediction model (e.g., a regression equation) during prediction.

[0027] The first receptor is not particularly limited as long as it can perform prediction with the desired accuracy. That is, the first receptor may be any receptor as long as it can perform prediction with the desired accuracy. The first receptor can be appropriately selected according to various conditions such as the type of target food and the type of target flavor function. However, the first receptor is selected from the second receptor. In other words, the second receptor includes the first receptor. The second receptor may be composed of the first receptor, or may be composed of the first receptor and another receptor. That is, all of the second receptors may be selected as the first receptor, or a part of the second receptors may be selected as the first receptor. The second receptor will be described later.

[0028] Receptors include G protein-coupled receptors and ion channel receptors. Examples of receptors include olfactory receptors and taste receptors. Olfactory receptors are G proteins. Taste receptors are an example of G protein-coupled receptors or ion-coupled receptors. The nucleophilic receptor may be an example of a channel-type receptor.

[0029] Olfactory receptors (which may be G protein-coupled receptors) include OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D4, OR1D5, OR1E1, OR1E2, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L6, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR1S2, OR2A1, OR2A2, OR2A4, OR2A5, OR2A7, OR2A12, OR2A1 4, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P, OR2AK2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2F2, OR2G2 , OR2G3, OR2G6, OR2H1, OR2H2, OR2J1P, OR2J2, OR2J3, OR2K2, OR2L2, OR2L3, OR2L5, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M5, OR2M7, OR2S2, OR2T1, OR 2T2, OR2T3, OR2T4, OR2T5, OR2T6, OR2T7, OR2T8, OR2T10, OR2T11, OR2T12, OR2T27, OR2T29, OR2T33, OR2T34, OR2T35, OR2V1, OR2V2, OR2W1, OR2W3, OR2Y1 , OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A4P, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C16, O R4C45, OR4C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F4, OR4F5, OR4F6, OR4F14P, OR4F15, OR4F17, OR4F21, OR4G11P, O R4H12P, OR4K1, OR4K2, OR4K5, OR4K13, OR4K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4M2, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2,OR5A1、OR5A2、OR5AC2、OR5AK2、OR5AK3P、OR5AN1、OR5AP2、OR5AR1、OR5AS1、OR5AU1、OR5B2、OR5B3、OR5B12、OR5B17、OR5B21、OR5C1、OR5D13、OR5D14、OR5D16、OR5D18、OR5F1、OR5H1、OR5H2、OR5H6、OR5H14、OR5H15、OR5I1、OR5J2、OR5K1、OR5K2、OR5K3、OR5K4、OR5L1、OR5L2、OR5M1、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P2、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1、OR5W2、OR6A2、OR6B1、OR6B2、OR6B3、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B2、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G1、OR8G2、OR8G5、OR8H1、OR8H2、OR8H3、OR8I2、OR8J1、OR8J3、OR8K1、OR8K3、OR8K5、OR8S1、OR8U1、OR8U8、OR9A2、OR9A4、OR9G1、OR9G4、OR9I1、OR9K2、OR9Q1、OR9Q2、OR10A2、OR10A3、OR10A4、OR10A5、OR10A6、OR10A7、OR10AD1、OR10AG1、OR10C1、OR10D3、OR10D4P、OR10G2、OR10G3、OR10G4、OR10G6、OR10G7、OR10G8、OR10G9、OR10H1、OR10H2、OR10H3、OR10H4、OR10H5、OR10J1、OR10J3、OR10J5、OR10K1、OR10K2、OR10P1、OR10Q1、OR10R2、OR10S1、OR10T2, OR10V1, OR10W1, OR10X1, OR10Z1, OR11A1, OR11G2, OR11H1, OR11H2, OR11H4, OR11H6, OR11H12, O R11L1, OR12D2, OR12D3, OR13A1, OR13C2, OR13C3, OR13C4, OR13C5, OR13C8, OR13C9, OR13D1, OR13F1, OR13 G1, OR13H1, OR13J1, OR14A2, OR14A16, OR14C36, OR14I1, OR14J1, OR14K1, OR14L1P, OR51A1P, OR51A2, OR5 1A4, OR51A7, OR51B2, OR51B4, OR51B5, OR51B6, OR51D1, OR51E1, OR51E2, OR51F1, OR51F2, OR51F5P, OR51G 1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, O R52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52E6, OR52E8, OR52H1, OR52I1, OR52 I2, OR52J3, OR52K1, OR52K2, OR52L1, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4 (also referred to as "Group A").

[0030] Olfactory receptors (which may be G protein-coupled receptors) include, in particular, OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D5, OR1E1, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR2A1, OR2A2, OR2A4, OR2A5, OR2A12, OR2A14, OR2A25, OR2AE1, OR2AG1, OR2 AG2, OR2AJ1P, OR2AK2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J2, O R2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, O R2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C1 5, OR4C16, OR4C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K 2, OR4K5, OR4K13, OR4K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR5A1, OR5A2, OR5AC2, OR5A K2, OR5AK3P, OR5AN1, OR5AP2, OR5AR1, OR5AS1, OR5AU1, OR5B2, OR5B3, OR5B12, OR5B17, OR5B21, OR5C1, OR5D13, OR5D14, OR5D16, OR5D18, OR5F1, OR5H1,OR5H2、OR5H6、OR5H14、OR5I1、OR5J2、OR5K1、OR5K3、OR5K4、OR5L2、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1、OR5W2、OR6A2、OR6B1、OR6B2、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G2、OR8G5、OR8H3、OR8I2、OR8J1、OR8J3、OR8K1、OR8K3、OR8K5、OR8S1、OR8U1、OR9A4、OR9G1、OR9G4、OR9I1、OR9K2、OR9Q1、OR9Q2、OR10A3、OR10A4、OR10A5、OR10A6、OR10A7、OR10AD1、OR10AG1、OR10C1、OR10D3、OR10D4P、OR10G2、OR10G3、OR10G4、OR10G6、OR10G7、OR10G9、OR10H2、OR10H4、OR10J1、OR10J3、OR10J5、OR10K1、OR10K2、OR10P1、OR10Q1、OR10R2、OR10S1、OR10T2、OR10V1、OR10W1、OR10X1、OR10Z1、OR11A1、OR11G2、OR11H4、OR11H6、OR11H12、OR11L1、OR12D2、OR12D3、OR13A1、OR13C2、OR13C3、OR13C4、OR13C8、OR13D1、OR13F1、OR13G1、OR13H1、OR13J1、OR14A2、OR14A16、OR14C36、OR14I1、OR14J1、OR14K1、OR14L1P、OR51A1P、OR51A4、OR51A7、OR51B2、OR51B4、OR51B5、OR51B6, OR51D1, OR51E1, OR51E2, OR51F1, OR51F2, OR51F5P, OR51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, O R51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52 E4, OR52E5, OR52E8, OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4 (also referred to as "group B").

[0031] Further examples of G protein-coupled receptors include taste receptor type I (Tas1R), taste receptor type II (Tas2R), glucose transporter (GLUT), sodium / glucose cotransporter (SGLT), calcium sensing receptor (CasR), metabotropic glutamate receptor (mGluR), CD36, free fatty acid receptor (FFAR), and other G protein-coupled receptors. Examples of Tas1R include Tas1R1, Tas1R2, and Tas1R3. Examples of FFAR include FFAR1, FFAR2, and FFAR3. Examples of other G protein-coupled receptors include GPRC6A, GPR92, GPR120, GPR113, GPR40, GPR43, and GPR41. Any of these G protein-coupled receptors may function as, for example, a taste receptor. Tas1R1 and Tas1R3 may, for example, form a heterocomplex to function as an umami receptor. Tas1R2 and Tas1R3 may, for example, form a heterocomplex to function as a sweet receptor. Tas2R may, for example, function as a bitter receptor. GLUTs and SGLTs may all, for example, function as sweet receptors. CasR may, for example, function as a kokumi receptor. mGluR, GPRC6A, and GPR92 may all, for example, function as an umami receptor. CD36, GPR120, and GPR113 may all, for example, function as a fat receptor. FFAR1 and GPR40, FFAR2 and GPR43, and FFAR3 and GPR413 may all, for example, form a heterocomplex to function as a fat receptor.

[0032] Examples of ionotropic receptors include epithelial sodium channel (ENaC), acid-sensing ion channel (ASIC), transient receptor potential channel (TRP channel), transmembrane channel-like protein (TMC protein), voltage-gated potassium ion channel, polycystic kidney disease 1-like 3 (PKD1L3), and polycystic kidney disease 2-like 1 (PKD2L1). Examples of TRP channels include TRPV1t and TRPML3. Examples of TMC proteins include TMC4 and TMC6. Examples of voltage-gated potassium ion channels include Kv3.2. Any of these ionotropic receptors may function, for example, as a taste receptor. Any of ENaC, ASIC, TRP channel, TMC protein, and voltage-gated potassium ion channel may function, for example, as a salty taste receptor. ASICs, PKD1L3, and PKD2L1, may all function as sour taste receptors, for example.

[0033] The gene encoding the receptor is also called the receptor gene.

[0034] The receptor genes and receptors include those of various organisms. Organisms include, for example, animals such as mammals. Specific examples of animals such as mammals include Homo sapiens (human), Mus musculus (mouse), and Rattus norvegicus. (rat), Canis lupus familiaris (dog), Felis catus (cat), Bos taurus (dog) Examples of the receptors include the following: Sus scrofa (pig), Pan troglodytes (chimpanzee), Macaca fascicularis (cynomolgus monkey), and Equus caballus (horse). Examples of animals such as mammals include, in particular, humans. The nucleotide sequences of the receptor genes and the amino acid sequences of the receptors of various organisms can be obtained from public databases such as NCBI and Ensembl.

[0035] The receptor may be, for example, a protein having a known or naturally occurring amino acid sequence of a receptor such as those described above. The receptor may also be, for example, a variant of a protein having a known or naturally occurring amino acid sequence of a receptor such as those described above. The receptor to be specified includes, for example, a protein having a known or natural amino acid sequence of the receptor specified by the name, and a variant thereof. The expression "a protein has an amino acid sequence" means that the protein contains the amino acid sequence, unless otherwise specified, and also includes the case where the protein consists of the amino acid sequence. Examples of variants include proteins having an amino acid sequence in which one or several amino acids at one or several positions in a known or natural amino acid sequence are substituted, deleted, inserted, and / or added. "One or several" may specifically mean, for example, 1 to 50, 1 to 40, 1 to 30, preferably 1 to 20, more preferably 1 to 10, even more preferably 1 to 5, and particularly preferably 1 to 3. Examples of variants include proteins having an amino acid sequence that has, for example, 50% or more, 65% or more, 80% or more, preferably 90% or more, more preferably 95% or more, even more preferably 97% or more, and particularly preferably 99% or more identity to the entire known or natural amino acid sequence. In addition, the receptor specified by the biological species of origin is not limited to the receptor itself found in the biological species, but includes proteins having the amino acid sequence of the receptor found in the biological species and variants thereof. The variant may or may not be found in the biological species. That is, for example, "human receptor" is not limited to the receptor itself found in humans, but includes proteins having the amino acid sequence of the receptor found in humans and variants thereof. The receptor may be, for example, a chimeric protein of two or more receptors of different origins. That is, the receptor specified by each of the above names is also intended to include, for example, chimeric proteins of two or more receptors of different origins specified by the name.

[0036] In addition, "identity" between amino acid sequences means the identity between amino acid sequences calculated by blastp using the default scoring parameters (Matrix: BLOSUM62; Gap Costs: Existence = 11, Extension = 1; Compositional Adjustments: Conditional compositional score matrix adjustment).

[0037] As the first receptor, one type of receptor may be used, or two or more types of receptors may be used. The number of the first receptors is not particularly limited as long as prediction can be performed with the desired accuracy. The number of the first receptors can be appropriately set according to various conditions such as the type of the target food and the type of the target flavor function. However, the number of the first receptors is equal to or less than the number of the second receptors. That is, the number of the first receptors is the same as or smaller than the number of the second receptors. The number of the first receptors may be smaller than the number of the second receptors, in particular. The number of the second receptors will be described later. That is, the number of the first receptors may be, for example, in the range of the number of the second receptors described later. The number of the first receptors may be, for example, 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 12 or more, 15 or more, 20 or more, or 25 or more, or 30 or less, 25 or less, 20 or less, 15 or less, 12 or less, 10 or less, 9 or less, 8 or less, 7 or less, 6 or less, 5 or less, 4 or less, 3 or less, or 2 or less, or any combination thereof that is not contradictory. The number of the first receptors may be, for example, 1 to 2, 2 to 3, 3 to 4, 4 to 5, 5 to 6, 6 to 7, 7 to 8, 8 to 9, 9 to 10, 10 to 12, 12 to 15, 15 to 20, 20 to 25, or 25 to 30. The number of the first receptors may be, for example, 2 to 30, 5 to 25, or 10 to 20. The first receptor may include, for example, a part or all of the receptors exemplified above. The first receptor may consist of, for example, a part or all of the receptors exemplified above. For example, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the first receptors may be selected from the receptors exemplified above. For example, particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the first receptors may be selected from olfactory receptors. For example, more particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the first receptors may be selected from the olfactory receptor group A exemplified above.For example, more particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the first receptors may be selected from the olfactory receptor group B exemplified above. The first receptor may also include, for example, 1% or more, 2% or more, 5% or more, 10% or more, 20% or more, 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the olfactory receptor group B exemplified above. In addition, in the case of a receptor that functions by forming a heterocomplex, the entire heterocomplex is regarded as one type of receptor.

[0038] The method of acquiring the receptor activation profile of a substance is not particularly limited. The receptor activation profile of a substance can be acquired, for example, by a known method for identifying the presence or absence or the degree of activation or inactivation of a receptor such as an olfactory receptor or a gustatory receptor by a substance. Specifically, the receptor activation profile of a substance can be acquired, for example, by contacting a receptor with a substance and measuring the presence or absence or the degree of activation or inactivation of the receptor due to contact with the substance. The contact between a receptor and a substance and the measurement of the presence or absence or the degree of activation or inactivation of the receptor due to the contact can be carried out, for example, with reference to a screening method for a substance that exhibits a target odor using an olfactory receptor response as an index (JP Patent Publication No. 2019-037197, etc.) or a screening method for a substance that exhibits a target taste using a gustatory receptor response as an index (JP Patent Publication No. 2018-014999, etc.). The receptor may be used by being supported on a cell such as an animal cell. When the receptor is used by being supported on a cell, "activation or inactivation of the receptor" may mean activation or inactivation of the cell that supports the receptor. A cell carrying a receptor can be obtained, for example, by introducing a receptor gene into a cell and expressing it. In the case of a receptor that functions by forming a heterocomplex, a cell carrying a receptor can be obtained, for example, by introducing genes encoding each of the subunits constituting the heterocomplex into a cell and co-expressing them.

[0039] Cells bearing a receptor may have components that are useful or necessary for obtaining a receptor activation profile, such as, for example, a protein involved in signal transduction, a protein that promotes membrane expression of the receptor, and components depending on the parameter to be measured. Proteins involved in signal transduction include G proteins (e.g., Golf), G protein activators (e.g., Ric8B), Examples of the proteins include adenylate cyclase and calcium channels. Examples of Golf include Golf from animals such as human Golf (GenBank accession No. NP_892023). Examples of Ric8B include Ric8B from animals such as rat Ric8B (GenBank accession No. NP_783188). Examples of proteins that promote membrane expression of receptors include RTP1s (Zhuang H and Matsunami H, J Biol Chem 282, 15284-15293 (2007)). Examples of RTP1s include RTP1s from animals such as human RTP1s (GenBank accession No. AAT70680), mouse RTP1s (GenBank accession No. ABU23737), and bat RTP1s (amino acid sequence from the methionine residue at position 37 to the C-terminus of GenBank accession No. XP_006765914). Examples of components depending on the parameter to be measured include probes such as calcium indicators and membrane potential-sensitive dyes, and reporter genes such as luciferase genes.

[0040] Activation of G protein-coupled receptors, such as olfactory receptors, increases, for example, intracellular cAMP concentration or cellular It can be measured using an increase in intracellular calcium concentration as an indicator (Patent Publication No. 2019-037197, etc.). For example, in HEK293T cells, when olfactory receptors are activated by odorants, the cells It is known that it couples with intracellular G proteins (e.g., Golf) to activate adenylate cyclase, thereby increasing the amount of intracellular cAMP (Kajiya K. et al., Molecular bases of odor discrimination: Reconstitution of olfactory receptors that recognize overlapping sets of odorants. Journal of Neuroscience, 2001, 21:6018-6025). Methods for measuring intracellular cAMP concentration include ELISA and reporter assays. An example of a reporter assay is the luciferase assay. With the reporter assay, intracellular cAMP concentration can be measured using a reporter gene (e.g., luciferase gene) that is constructed to be expressed depending on the cAMP concentration. A method for measuring intracellular calcium concentration includes calcium imaging using a calcium indicator.

[0041] Activation of ionotropic receptors such as TMC proteins increases ion influx into cells, for example. The membrane potential of the cell, the inward membrane current of the cell, or the increase in the ion concentration in the cell can be used as an indicator (JP Patent Publication 2018-014999, etc.). Ions include sodium ions, calcium ions, and potassium ions. Examples of the methods for measuring intracellular sodium concentration include a method using sodium indicators such as CoroNa Green Sodium Indicator (Thermo Fisher Scientific). Examples of the methods for measuring intracellular calcium concentration include calcium imaging using calcium indicators.

[0042] Specifically, the contact between a receptor and a substance and the presence or absence or the degree of the resulting receptor activation or inactivation can be measured, for example, by the following procedure. In the following procedure, the "substance" that is brought into contact with the receptor may be read as "target object," "food," "ingredients," or the like.

[0043] In other words, the presence or absence or degree of receptor activation or inactivation by a substance can be determined by contacting the receptor with the substance and using as an indicator the degree of receptor activation (also referred to as the "degree of activation D1") when the contact is carried out (i.e., under the conditions in which the receptor is contacted with the substance).

[0044] A system in which the contact between the receptor and the substance is carried out is also called a "reaction system". The contact between the receptor and the substance can be carried out in a suitable liquid. That is, the reaction system may be a liquid. The liquid in which the contact between the receptor and the substance is carried out (that is, the reaction system which is a liquid) is also called a "reaction liquid". That is, for example, the receptor and the substance can be brought into contact with each other by making them coexist in a suitable reaction liquid. Specifically, for example, the receptor (for example, in the form exemplified above, such as a cell having a receptor) and the substance can be brought into contact with each other by dissolving, suspending, dispersing, or the like in a suitable liquid medium. Examples of the liquid medium include aqueous media such as water and aqueous buffer solutions. In addition, when two or more kinds of components are brought into contact with the receptor together, the contact between the components and the receptor may or may not start simultaneously. That is, for example, after the contact between a certain component and the receptor is started, another component may be further added to the reaction system. The reaction conditions (conditions under which the contact between the receptor and the substance is carried out) are not particularly limited as long as it is possible to measure the presence or absence or the degree of activation or inactivation of the receptor by the substance. The reaction conditions can be appropriately set depending on various conditions such as the form of use of the receptor, the type of substance, and the method of measuring the activation or inactivation of the receptor. As the reaction conditions, for example, known reaction conditions for measuring interactions between substances such as interactions between proteins and ligands may be used as they are or may be appropriately modified. The concentration of the substance (which may be, for example, a test substance or a component whose content differs between control foods, such as a control substance) may be, for example, 0.01 nM to 500 mM, 10 nM to 100 mM, 1 μM to 10 mM, or 3 μM to 1 mM. The concentration of the substance (which may be, for example, a substance that is a mixture or a food, such as a control food) may be, for example, 0.001 to 20% (w / w), 0.01 to 20% (w / w), 0.05 to 15% (w / w), or 0.1 to 10% (w / w). The concentration of the receptor may be, for example, 1 pg / mL to 10 mg / mL. When receptor-containing cells are used, the concentration of the receptor-containing cells may be, for example, 10 6 cell / mL to 10,000,000 cell / mL.The contact between the receptor and the substance may or may not be terminated at an appropriate time. The contact between the receptor and the substance may be continued until the activation or inactivation of the receptor by the substance is measured. The duration of the contact between the receptor and the substance may be, for example, 0.1 seconds or more, 0.5 seconds or more, 1 second or more, 5 seconds or more, 10 seconds or more, 30 seconds or more, 1 minute or more, 5 minutes or more, 10 minutes or more, 30 minutes or more, 1 hour or more, or 2 hours or more, or 24 hours or less, 12 hours or less, 6 hours or less, 2 hours or less, or 1 hour or less, or a compatible combination thereof. The duration of the contact between the receptor and the substance may be, specifically, for example, 1 hour to 6 hours. The reaction system may contain other components in addition to the receptor (for example, those in the form exemplified above, such as cells having the receptor) and the substance, as long as it is possible to measure the presence or absence or the degree of activation or inactivation of the receptor by the substance. The other components can be appropriately set according to various conditions such as the form of use of the receptor, the type of substance, and the method of measuring the activation or inactivation of the receptor. Other components include salts such as calcium salts, carbon sources such as glucose, other medium components, and pH buffers.

[0045] In addition, when measuring the inactivation of a receptor by a substance, the contact between the receptor and the substance can be carried out, for example, in the presence of a substance that activates the receptor (hereinafter also referred to as "receptor activator"). The expressions "contacting the receptor with a substance in the presence of a receptor activator", "contacting the receptor with a receptor activator in the presence of a substance", and "contacting the receptor with a receptor activator and a substance" may be used synonymously. The description of the contact between the receptor and a substance can also be applied mutatis mutandis to the contact between the receptor and a receptor activator. The contact between the receptor activator and the substance and the receptor may or may not be started simultaneously. For example, the substance and the receptor activator may be mixed in advance and then contacted with the receptor. Also, for example, the receptor activator may be further added to the reaction system after the contact between the substance and the receptor is started, or the substance may be further added to the reaction system after the contact between the receptor activator and the receptor is started. The concentration of the receptor activator may be, for example, 0.01 nM to 500 mM, 10 nM to 100 mM, 1 μM to 10 mM, or 3 μM to 1 mM. When contact between the receptor and the substance is carried out in the presence of a receptor activator, "the duration of contact between the receptor and the substance (here, the substance for obtaining a receptor activation profile)" may be read as "the duration of contact between the receptor and the receptor activator and the substance (here, the substance for obtaining a receptor activation profile)."

[0046] The receptor activator is not particularly limited as long as it can activate the receptor. The receptor activator may be composed of a single component (i.e., a pure substance) or may be composed of a combination of two or more components (i.e., a mixture). When the receptor activator is a mixture, the number of types and composition ratio of the components constituting the mixture are not particularly limited. The receptor activator may be a known substance or a new substance. The receptor activator may be a natural product or an artificial product. The receptor activator may be known to be able to activate the receptor, or may not be known to be able to do so. The receptor activator can be screened, for example, with reference to a method for screening a substance that exhibits a target odor using an olfactory receptor response as an index (JP Patent Publication No. 2019-037197, etc.) or a method for screening a substance that exhibits a target taste using a taste receptor response as an index (JP Patent Publication No. 2018-014999, etc.).

[0047] The timing for measuring the degree of receptor activation (degree of activation D1) when the receptor is brought into contact with a substance is not particularly limited, as long as it is possible to measure the presence or absence or the degree of receptor activation or inactivation by the substance. The timing for measuring the degree of activation D1 can be appropriately set depending on various conditions such as the form of use of the receptor, the type of substance, and the method for measuring receptor activation or inactivation. Specifically, the timing for measuring the degree of activation D1 may be an appropriate time from the time when contact between the receptor and the substance begins to the time when the activation or inactivation of the receptor by the substance disappears. The timing for measuring the degree of activation D1 may be, for example, the time when the degree of receptor activation or inactivation by the substance is maximized. In addition, the timing for measuring the degree of activation D1 may be, for example, 0.1 seconds or later, 0.5 seconds or later, 1 second or later, 5 seconds or later, 10 seconds or later, 30 seconds or later, 1 minute or later, 5 minutes or later, 10 minutes or later, 30 minutes or later, 1 hour or later, Alternatively, it may be 2 hours or later, up to 24 hours, up to 12 hours, up to 6 hours, up to 2 hours, or up to 1 hour, or any combination thereof that is not contradictory. Specifically, the timing for measuring the degree of activation D1 may be, for example, 1 hour or later to 6 hours after the contact between the receptor and the substance starts. When the contact between the receptor and the substance is carried out in the presence of a receptor activator, "the time when the contact between the receptor and the substance (here, the substance for obtaining the receptor activation profile) starts" may be read as "the time when the contact between the receptor and the receptor activator and the substance (here, the substance for obtaining the receptor activation profile) starts."

[0048] The presence or absence or the degree of receptor activation or inactivation by a substance can be determined by specifically comparing the degree of activation D1 with the degree of receptor activation under control conditions (also referred to as "degree of activation D2"). An example of the control condition is a condition in which the receptor is not contacted with the substance. When the contact between the receptor and the substance (here, the substance for which the receptor activation profile is obtained) is performed in the presence of a receptor activating substance, an example of the control condition is a condition in which the receptor is contacted with the receptor activating substance but not with the substance (i.e., a condition in which the receptor is contacted with the receptor activating substance in the absence of the substance).

[0049] The activation levels D1 and D2 can both be obtained and used as data reflecting parameters that are indicative of receptor activation or inactivation. Examples of parameters that are indicative of receptor activation or inactivation include the amount of intracellular calcium and the amount of intracellular cAMP. In the case of luciferase assay, examples of data reflecting the amount of intracellular cAMP include luminescence intensity. Data reflecting parameters that are indicative of receptor activation or inactivation can be used as is or after being appropriately processed. Examples of processing include logarithmic transformation, correction, and standardization. Processing such as logarithmic transformation, correction, and standardization can be performed, for example, by the procedures described in the Examples.

[0050] When the degree of activation D1 is high, it may be determined that the receptor is activated by the substance. For example, when the ratio of the degree of activation D1 to the degree of activation D2 (i.e., D1 / D2) is within a predetermined range, it may be determined that the receptor is activated by the substance. The predetermined range for determining that the receptor is activated by the substance can be set appropriately depending on various conditions such as the type of receptor and the type of data indicating the degrees of activation D1 and D2. The predetermined range for determining that the receptor is activated by the substance may be, for example, 1.5 or more, 2 or more, 3 or more, 5 or more, 10 or more, 20 or more, 50 or more, or 100 or more. The ratio of the degree of activation D1 to the degree of activation D2 may be a "normalized response value" calculated by the procedure described in JP 2019-037197 A.

[0051] When the degree of activation D1 is low, it may be determined that the receptor has been inactivated by the substance. For example, when the ratio of the degree of activation D1 to the degree of activation D2 (i.e., D1 / D2) is within a predetermined range, it may be determined that the receptor has been activated by the substance. The predetermined range for determining that the receptor has been inactivated by the substance can be set appropriately depending on various conditions such as the type of receptor and the type of data indicating the degrees of activation D1 and D2. The predetermined range for determining that the receptor has been inactivated by the substance may be, for example, 0.8 or less, 0.7 or less, 0.6 or less, 0.5 or less, 0.4 or less, 0.3 or less, 0.2 or less, or 0.1 or less. The ratio of the degree of activation D1 to the degree of activation D2 may be a "normalized response value" calculated by the procedure described in JP 2019-037197 A.

[0052] In addition, the degree of activation or inactivation of the receptor by the substance can be determined by using the result of comparing the degree of activation D1 with the degree of activation D2 as an index. For example, the ratio of the degree of activation D1 to the degree of activation D2 (i.e., D1 / D2) can be regarded as the degree of activation or inactivation of the receptor by the substance. The ratio of D1 can be a "normalized response value" calculated by the procedure described in JP 2019-037197 A. In addition, for example, the logarithmic value of the activation level D1 is subtracted from the logarithmic value of the activation level D2, and the value standardized between receptors by appropriate correction can be regarded as the degree of receptor activation or inactivation by a substance. Examples of such standardized values ​​include the "Response value" calculated by the procedure described in the Examples.

[0053] <1-4> Prediction model construction process The predictive model producing method of the present invention includes a predictive model constructing step.

[0054] The prediction model construction step is a step of constructing a prediction model based on the following data (A) and (B): (A) Data reflecting differences in receptor activation profiles among the multiple foods that make up the control food group; (B) Data showing the target flavor function for each of the multiple foods that make up the control food group.

[0055] That is, based on data (A) and (B), a relationship between the receptor activation profile of a substance and the target flavor function of a target food can be identified, and a predictive model can be constructed.

[0056] The term "control food group" refers to a group consisting of multiple foods with different compositions. The control food group includes a variation group of the target food. In other words, the multiple foods constituting the control food group include a variation group of the target food. The control food group may be composed of a variation group of the target food, or may be composed of a variation group of the target food and one or more other foods. The control food group may be composed of a variation group of the target food in particular. Each of the multiple foods constituting the control food group is also referred to as a "control food." "Multiple foods constituting the control food group" refers to all foods constituting the control food group (i.e., all control foods constituting the control food group). All foods constituting the control food group (i.e., all control foods constituting the control food group) are also referred to as "all control foods." Each of the other one or more foods that may be included in the control food group (i.e., one or more foods other than the variation group of the target food) is also referred to as "other foods." The type of other foods is not particularly limited. That is, the other foods may be any foods. Examples of the other foods include the foods exemplified above as the target foods. The other foods may or may not be of the same type of food as the target food. If the control food group contains more than one other food, the other foods may or may not be of the same type of food as each other.

[0057] The "variation group of the target food" means a group consisting of multiple foods of the same type as the target food but with different compositions. For example, multiple foods that are commonly classified as any of the foods selected from the above-mentioned foods may be considered as the same type of food for the "variation group of the target food". That is, for example, if the target food is beef stock, the variation group of the target food may be a group consisting of multiple beef stocks with different compositions. Also, for example, if the target food is oyster sauce, the variation group of the target food may be a group consisting of multiple oyster sauces with different compositions. Each of the multiple foods that make up the variation group of the target food is also called a "variation of the target food". "Multiple variations that make up the variation group of the target food" means all the foods that make up the variation group of the target food (i.e., all the variations that make up the variation group of the target food). All the foods that make up the variation group of the target food (i.e., all the variations that make up the variation group of the target food) are also called "all the variations of the target food". Each variation of the target food is an example of a target food. Also, each variation of the target food is an example of a control food.

[0058] "The composition differs between foods or variations" means that the types or amounts of ingredients contained differ between foods or variations. The difference in composition may be due to differences in the raw materials used (e.g., differences in the types or amounts of raw materials used), or due to other factors, or due to a combination of these. Other factors include differences in manufacturing conditions. The difference in composition may in particular be due to differences in at least the raw materials used.

[0059] The composition of the control food group (e.g., the composition of the target food variation group) is not particularly limited as long as the data (A) and (B) can be used and a prediction model that enables prediction with the desired accuracy can be obtained. The composition of the control food group (e.g., the composition of the target food variation group) can be appropriately set according to various conditions such as the type of target food and the type of target flavor function. However, one or more variations selected from all the variations of the target food have the target flavor function. In addition, two or more variations selected from all the variations of the target food have different receptor activation profiles and target flavor functions. The target food variation group may or may not include two or more variations that have the same receptor activation profile. The target food variation group may or may not include two or more variations that have the same target flavor function. The target food variation group may or may not include two or more variations that have the same receptor activation profile and target flavor function. Typically, all the variations of the target food may have different receptor activation profiles. Typically, all the variations of the target food may have different target flavor functions. Typically, all the variations of a target food may differ from one another in receptor activation profiles and target flavor functions. Also, typically, all the control foods may differ from one another in receptor activation profiles. Typically, all the control foods may differ from one another in target flavor functions. Typically, all the control foods may differ from one another in receptor activation profiles and target flavor functions. "Receptor activation profiles differ between foods or variations" means that the presence or absence or degree of a property that activates or inactivates a receptor differs between foods or variations. "Target flavor functions differ between foods or variations" means that the presence or absence or degree of a target flavor function differs between foods or variations.

[0060] The target food variations include any of the variations of the target food (e.g., existing variations of the target food, such as existing products of the target food) in which the content of one or more ingredients is increased and / or the content of one or more ingredients is decreased. The ingredients whose content is increased may or may not be ingredients that are inherently contained in any of the variations of the target food. The ingredients whose content is increased may be selected, for example, from ingredients that are inherently contained in any of the variations of the target food and contribute to the flavor function of the target food. The variations in which the content of an ingredient is increased can be produced, for example, by increasing the amount of the ingredient used as a raw material. The ingredients whose content is increased may be blended in advance with the raw material, may be added during the production of the food, or may be added to the finished food. The variations in which the content of an ingredient is decreased can be produced, for example, by decreasing the amount of the ingredient used as a raw material. The target food variations may be, for example, commercially available products of the target food, prototypes of the target food under development, or products with modified compositions. A specific example of a variation group of a target food is a group of multiple foods of the same type as the target food, but with different manufacturing ingredients, manufacturing conditions, and / or manufacturers. A more specific example of a variation group of a target food is a group of two or more variations of the target food, each of which has an increased content of two or more ingredients. A more specific example of a variation group of a target food is a group of two or more products of the target food, but with different manufacturers. Products from different manufacturers may usually be products with different manufacturing ingredients and / or manufacturing conditions.

[0061] The number of variations of the target food (i.e., the number of variations constituting the group of variations of the target food) is not particularly limited as long as a prediction model that enables prediction with the desired accuracy can be obtained. The number of variations of the target food can be set appropriately depending on various conditions such as the type of the target food and the type of the target flavor function. However, the number of variations of the target food is two or more. The number of variations of a target food product may be, for example, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 12 or more, 15 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 150 or more, or 200 or more; 300 or less, 200 or less, 150 or less, 100 or less, 90 or less, 80 or less, 70 or less, 60 or less, 50 or less, 40 or less, 30 or less, 20 or less, 15 or less, 12 or less, 10 or less, 9 or less, 8 or less, 7 or less, 6 or less, 5 or less, 4 or less, or 3 or less, or any non-contradictory combination thereof. The number of variations of the target food may be, for example, 2 to 3, 3 to 4, 4 to 5, 5 to 6, 6 to 7, 7 to 8, 8 to 9, 9 to 10, 10 to 12, 12 to 15, 15 to 20, 20 to 30, 30 to 40, 40 to 50, 50 to 60, 60 to 70, 70 to 80, 80 to 90, 90 to 100, 100 to 150, 150 to 200, or 200 to 300. The number of variations of the target food may be, for example, 5 to 300, 7 to 200, or 10 to 100.

[0062] The number of other foods (i.e., foods other than the variation group of the target food that may be included in the control food group) is not particularly limited as long as a prediction model that enables prediction with the desired accuracy can be obtained. The number of other foods can be appropriately set according to various conditions such as the type of target food and the type of target flavor function. The number of other foods may be, for example, 10 or less, 9 or less, 8 or less, 7 or less, 6 or less, 5 or less, 4 or less, 3 or less, 2 or less, 1 or less, or zero. The smaller the number of other foods, the higher the accuracy of the prediction. For example, particularly when making a prediction for a substance that does not itself have the target flavor function, the smaller the number of other foods, the higher the accuracy of the prediction.

[0063] The ratio of the target food in the control food group is not particularly limited as long as a prediction model that enables prediction with the desired accuracy can be obtained. The ratio of the target food in the control food group can be appropriately set according to various conditions such as the type of target food and the type of target flavor function. The ratio of the target food in the control food group may be, for example, 80% or more, 85% or more, 90% or more, 95% or more, 97% or more, 99% or more, or 100%. The higher the ratio of the target food in the control food group, the higher the accuracy of the prediction. For example, particularly when prediction is performed for a substance that does not itself have the target flavor function, the higher the ratio of the target food in the control food group, the higher the accuracy of the prediction. "The ratio of the target food in the control food group" means the ratio of the number of variations of the target food to the number of control foods (i.e., the ratio of the number of variations constituting the target food variation group to the number of foods constituting the control food group).

[0064] Data (A) is data reflecting the difference in receptor activation profile between the multiple foods constituting the control food group (i.e., all control foods). For example, when the control food group is composed of a variation group of a target food, data (A) is data reflecting the difference in receptor activation profile between the multiple variations constituting the variation group of the target food (i.e., all variations of the target food). The configuration of data (A) is not particularly limited as long as a prediction model that enables prediction with the desired accuracy can be obtained. The configuration of data (A) can be appropriately set depending on various conditions such as the type of target food, the configuration of the control food group, the configuration of the variation group of the target food, and the type of target flavor function.

[0065] Data (A) is the difference in the amount of ingredients in the control foods selected from all the control foods. The receptor activation profile may be a receptor activation profile. For example, when the control food group is composed of a variation group of the target food, the data (A) may be a receptor activation profile of a component whose content differs among the variations selected from all the variations of the target food. A component whose content differs among the control foods selected from all the control foods (e.g., a component whose content differs among the variations selected from all the variations of the target food) is also referred to as a "control component". "Between control foods selected from all the control foods" means between any control food selected from all the control foods and another control food. "Between variations selected from all the variations of the target food" means between any variation of the target food selected from all the variations of the target food and another variation of the target food. In other words, the receptor activation profile of the control component reflects the difference in the receptor activation profile among the variations of the target food due to the difference in the content of the control component. For example, the receptor activation profile of the control component reflects the difference in the receptor activation profile among the variations of the target food due to the difference in the content of the control component. The control component may be a component that is contained in any control food but not contained in another control food. The control component specifically includes a component that is contained in one variation of the target food but not in another variation of the target food. The control component also includes a component that is contained in two or more control foods at different contents. The control component specifically includes a component that is contained in two or more variations of the target food at different contents. The control component more specifically includes two or more components when the target food variation group is a group of two or more variations in which the contents of the two or more components are increased in any one variation of the target food. The data (A) may be a receptor activation profile of one control component or may be a receptor activation profile of two or more control components (i.e., the receptor activation profile of each of the two or more control components).When the variation group of the target food contains two or more kinds of control components, the data (A) may be the receptor activation profile of some of the control components (i.e., some of the two or more kinds of control components) or may be the receptor activation profile of all of the control components (i.e., all of the two or more kinds of control components). The part of the control components is not particularly limited as long as a prediction model that enables prediction with the desired accuracy can be obtained. The part of the control components can be appropriately set according to various conditions such as the type of the target food, the composition of the control food group, the composition of the variation group of the target food, and the type of the target flavor function. For example, among the two or more kinds of control components, only the receptor activation profile of the control component that can contribute to the target flavor function may be used as the data (A). In other words, for example, among the two or more kinds of control components, the receptor activation profile of the control component that is assumed to have a low contribution to the target flavor function may be excluded from the data (A). When the control food group contains two or more kinds of control components (for example, when the variation group of the target food contains two or more kinds of control components), the data (A) may be the receptor activation profile of all of the control components in particular.

[0066] Data (A) may also include the receptor activation profile of each of all control foods. That is, by comparing the receptor activation profiles between the control foods, the difference in the receptor activation profiles between the control foods can be obtained. For example, when the control food group is composed of a group of variations of a target food, data (A) may also include the receptor activation profile of each of all variations of the target food. That is, by comparing the receptor activation profiles between the variations of the target food, the difference in the receptor activation profiles between the variations of the target food can be obtained, and the receptor activation profile of each of all variations of the target food can be used as data reflecting the difference in the receptor activation profiles. Data (A) may be the receptor activation profile of each of all control foods (e.g., the receptor activation profile of each of all variations of the target food) in either case. Data (A) may be the receptor activation profile of each of all control foods (e.g., the receptor activation profile of each of all variations of the target food) in the case where the group of variations of the target food is a group consisting of two or more products of the target food made by different manufacturers.

[0067] The data (A) may be publicly known or may not be publicly known. When the data (A) is not publicly known, the data (A) may be appropriately acquired before the prediction model construction step is carried out. That is, the prediction model production method of the present invention may include a step of acquiring the data (A) before the prediction model construction step is carried out. The method of acquiring the data (A) is not particularly limited. The data (A) can be acquired, for example, by the method of acquiring the receptor activation profile of the substance described above.

[0068] The receptor activation profile of data (A) (e.g., the receptor activation profile of the control component, the receptor activation profile of each control food, the receptor activation profile of each variation of the target food) is a receptor activation profile for a second receptor. That is, "second receptor" means a receptor used in constructing a predictive model (e.g., a regression equation). "A receptor is used in constructing a predictive model (e.g., a regression equation)" may mean that the receptor activation profile for the receptor is used as an explanatory variable in regression analysis when constructing a predictive model (e.g., a regression equation).

[0069] The second receptor is not particularly limited as long as a prediction model that allows prediction with the desired accuracy can be obtained. That is, the second receptor may be any receptor as long as a prediction model that allows prediction with the desired accuracy can be obtained. The second receptor can be appropriately selected according to various conditions such as the type of target food and the type of target flavor function. However, the second receptor includes the first receptor.

[0070] As the second receptor, one type of receptor may be used, or two or more types of receptors may be used. The number of second receptors is not particularly limited as long as a prediction model that enables prediction with the desired accuracy is obtained. The number of second receptors can be appropriately set according to various conditions such as the type of target food and the type of target flavor function. The number of second receptors may be, for example, 1 or more, 2 or more, 5 or more, 10 or more, 20 or more, 50 or more, 100 or more, 150 or more, 200 or more, 250 or more, 300 or more, 400 or more, 500 or more, 700 or more, 1000 or more, or 1500 or more, or 2000 or less, 1500 or less, 1000 or less, 700 or less, 500 or less, 400 or less, 300 or less, 250 or less, 200 or less, 150 or less, 100 or less, 50 or less, 20 or less, 10 or less, 5 or less, or 2 or less, or a combination thereof that does not contradict. The number of second receptors may be, in particular, 50 or more, 100 or more, 150 or more, 200 or more, 250 or more, or 300 or more. The number of second receptors may specifically be, for example, 1 to 2, 2 to 5, 5 to 10, 10 to 20, 20 to 50, 50 to 100, 100 to 150, 150 to 200, 200 to 250, 250 to 300, 300 to 400, 400 to 500, 500 to 700, 700 to 1000, 1000 to 1500, or 1500 to 2000. The number of the second receptors may be, specifically, for example, 50 to 2000, 50 to 1000, 50 to 500, 50 to 100, 100 to 2000, 100 to 1000, 100 to 500, 300 to 2000, 300 to 1000, 300 to 500, or 300 to 400. The second receptor may include, for example, a part or all of the receptors exemplified above. The second receptor may be, for example, a part or all of the receptors exemplified above. For example, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the second receptors may be selected from the receptors exemplified above. For example, in particular, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the second receptors may be selected from olfactory receptors.For example, more particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the second receptors may be selected from the olfactory receptor group A exemplified above. For example, more particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the total number of the second receptors may be selected from the olfactory receptor group B exemplified above. In addition, the second receptor may include, for example, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, or all of the olfactory receptor group B exemplified above. In addition, in the case of a receptor that functions by forming a heterocomplex, the entire heterocomplex is regarded as one type of receptor.

[0071] Data (B) is data showing the target flavor function of each of the multiple foods that make up the control food group (i.e., all control foods). For example, when the control food group is composed of a variation group of a target food, data (B) is data showing the target flavor function of each of the multiple variations that make up the variation group of the target food (i.e., all variations of the target food).

[0072] "Data indicating target flavor function" means data indicating the presence or absence or the degree of the target flavor function. In other words, "data indicating target flavor function" of a certain food or variation may mean data indicating whether the food or variation exhibits the target flavor, or the degree to which the food or variation exhibits the target flavor. The configuration of the data indicating the target flavor function is not particularly limited as long as a prediction model that enables prediction with the desired accuracy is obtained. The configuration of the data indicating the target flavor function can be appropriately set according to various conditions such as the type of target food, the configuration of the control food group, the configuration of the variation group of the target food, and the type of target flavor function. The data indicating the target flavor function may be calculated, for example, based on the presence or absence of detection of the target flavor in each control food (e.g., each variation of the target food) and / or the detection ranking of the target flavor in each control food (e.g., each variation of the target food). The data indicating the target flavor function may include a quantitative value of the target flavor function. The quantitative value of the target flavor function may include a "function strength" value calculated by the procedure described in the Examples.

[0073] The data (B) may or may not be publicly known. When the data (B) is not publicly known, the data (B) may be appropriately acquired before the predictive model construction step is carried out. That is, the predictive model production method of the present invention may include a step of acquiring the data (B) before the predictive model construction step is carried out. The method of acquiring the data (B) is not particularly limited. The data (B) can be acquired, for example, by a known method for identifying the flavor function of a substance. Specifically, the data (B) can be acquired, for example, by a sensory evaluation by a specialist panel.

[0074] The embodiment of the prediction model construction step is not particularly limited as long as a prediction model that enables prediction with the desired accuracy can be obtained. The embodiment of the prediction model construction step can be appropriately set according to various conditions such as the type of target food, the type of target flavor function, the number of variations of the target food, the number of other foods, the number of first receptors, the number of second receptors, the configuration of data (A), and the configuration of data (B).

[0075] The prediction model can be constructed, for example, by regression analysis using data (A) as explanatory variables and data (B) as response variables. That is, the prediction model construction step may be, for example, a step of constructing a prediction model by regression analysis using data (A) as explanatory variables and data (B) as response variables.

[0076] The embodiment of the regression analysis is not particularly limited as long as a prediction model that enables prediction with a desired accuracy can be obtained. The embodiment of the regression analysis can be appropriately selected depending on various conditions such as the number and configuration of variables. The regression analysis may be, for example, linear regression analysis. Examples of algorithms for regression analysis (specifically, linear regression analysis) include the LASSO regression model, the Ridge regression model, and the Elastic Net regression model. Regression analysis (specifically, linear regression analysis) The algorithm includes, in particular, the Lasso regression model.

[0077] In the regression analysis, for example, variables may be reduced. Examples of the reduction of variables include reduction of explanatory variables. Examples of the reduction of explanatory variables include reduction of second receptors (i.e., reduction of the receptor activation profile for the second receptor used as the explanatory variable). "Reduction of second receptors" may mean a reduction in the number of second receptors selected as the first receptor, that is, a reduction in the number of first receptors selected from the second receptors. That is, the second receptors may be reduced by reducing variables, thereby selecting the first receptor. "Number of second receptors" means the number of second receptors before reduction of variables, unless otherwise specified. Examples of the variable reduction method include a forward selection method, a backward selection method, a stepwise forward selection method, and a stepwise backward selection method. In addition, examples of the variable reduction method include regularization, which will be described later.

[0078] In the regression analysis, for example, regularization may be performed. Examples of regularization include L1 regularization, L2 regularization, and L0 regularization. Examples of regularization in particular include L1 regularization. By regularization such as L1 regularization, for example, variables may be reduced. The strength of regularization can be set appropriately depending on various conditions such as the number of second receptors. The strength of regularization can be set using a regularization parameter λ. The larger the regularization parameter λ, the more likely it is that the number of regression coefficients estimated to be zero (or nearly zero) will be, i.e., the smaller the number of second receptors selected as the first receptor. In addition, the larger the regularization parameter λ, the more likely it is that overfitting will occur. Although the risk may be reduced, the risk of underfitting may be increased. Regularization or a regression analysis algorithm including it includes the above-mentioned LASSO regression model, Examples of the regression analysis algorithm include the Ridge regression model and the Elastic Net regression model. Examples of the regression analysis algorithm including regularization include the Lasso regression model. For example, the Lasso regression model or the Elastic Net regression model may be used to perform regression analysis while reducing variables.

[0079] By carrying out the prediction model construction step in this manner, a prediction model candidate is obtained. In the prediction model construction step, one prediction model candidate may be obtained, or two or more prediction model candidates may be obtained. For example, two or more regression analysis may be performed by changing the analysis conditions to obtain two or more prediction model candidates. Specifically, for example, the mode of variable reduction and / or the strength of regularization (specifically, the regularization parameter λ) may be changed to perform two or more regression analysis to obtain two or more prediction model candidates. When two or more prediction model candidates are obtained, a candidate having a desired property (e.g., high prediction accuracy) among the candidates may be adopted as a prediction model. The property of the candidate (e.g., prediction accuracy) can be verified, for example, by cross-validation. That is, in the prediction model construction step (e.g., regression analysis), for example, cross-validation may be performed. Examples of cross-validation include k-fold cross-validation and leave-one-out cross-validation. The number of k in k-fold cross-validation is not particularly limited as long as the model can be verified with the desired accuracy. The number k in the k-fold cross-validation can be, for example, 5 or 10.

[0080] <2> Prediction method of the present invention The prediction method of the present invention is a method for predicting the target function of a test substance. The prediction can be performed using the prediction model of the present invention. Specifically, the prediction can be performed based on the receptor activation profile of the test substance and the prediction model of the present invention. That is, the prediction method of the present invention may include a step of predicting the target function of the test substance based on the receptor activation profile of the test substance and the prediction model of the present invention. This step is also referred to as a "prediction step".

[0081] Furthermore, by predicting the target function of the test substance, a substance having a target function can be screened. That is, a test substance predicted to have a target function can be selected as a candidate substance having a target function, and thus a substance having a target function can be screened. That is, one aspect of the prediction method of the present invention may be a method for screening a substance having a target function. That is, the prediction step may be a step of predicting the target function of the test substance based on the receptor activation profile of the test substance and the prediction model of the present invention, and selecting the test substance predicted to have the target function as a candidate substance having the target function.

[0082] The prediction method of the present invention may further include, prior to the prediction step, a step of producing a prediction model by the prediction model production method of the present invention.

[0083] The receptor activation profile of the test substance can be substituted as a variable into a prediction model (e.g., a regression equation). That is, by substituting the receptor activation profile of the test substance as a variable into a prediction model (e.g., a regression equation), a conclusion serving as an index of prediction (e.g., a predicted value of the target function of the test substance) can be output.

[0084] When the predicted value of the target function of the test substance is high, it may be predicted that the test substance has a target function. For example, one or more test substances among a plurality of test substances may be selected as candidates for substances having a target function in descending order of the predicted value of the target function. It may also be predicted that the higher the predicted value of the target function of the test substance, the more likely the test substance has a target function. It may also be predicted that the higher the predicted value of the target function of the test substance, the stronger the target function of the test substance.

[0085] The prediction method of the present invention may further include a step of evaluating the result of the prediction. That is, by evaluating the target function of the test substance predicted to have the target function, it is possible to confirm whether the test substance actually has the target function. That is, the step of evaluating the result of the prediction may be, for example, a step of confirming the target function of the test substance predicted to have the target function. The result of the prediction can be confirmed, for example, by adding the test substance predicted to have the target function to the target food and confirming an increase or decrease in the target flavor. The increase or decrease in the target flavor can be confirmed, for example, by a known method for identifying the flavor function of a substance. Specifically, the increase or decrease in the target flavor can be confirmed, for example, by sensory evaluation by a specialist panel. EXAMPLES

[0086] The present invention will now be described in more detail with reference to the following non-limiting examples.

[0087] <1> Preparation of cells expressing human olfactory receptors <1-1> Construction of expression vector for human olfactory receptor As for olfactory receptors, 353 types of human olfactory receptors (OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D5, OR1E1, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR2A1, OR2A2, OR2A4, OR2A5, OR2A12, OR2A14, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P , OR2AK2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C16, OR4 C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K2, OR4K5, OR4K 13, OR4K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR5A1, OR5A2, OR5AC2, OR5AK2, OR5AK3P, O R5AN1, OR5AP2, OR5AR1, OR5AS1, OR5AU1, OR5B2, OR5B3, OR5B12, OR5B17, OR5B21, OR5C1, OR5D13, OR5D14, OR5D16, OR5D18, OR5F1, OR5H1, OR5H2, OR5H6,OR5H14、OR5I1、OR5J2、OR5K1、OR5K3、OR5K4、OR5L2、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1、OR5W2、OR6A2、OR6B1、OR6B2、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G2、OR8G5、OR8H3、OR8I2、OR8J1、OR8J3、OR8K1、OR8K3、OR8K5、OR8S1、OR8U1、OR9A4、OR9G1、OR9G4、OR9I1、OR9K2、OR9Q1、OR9Q2、OR10A3、OR10A4、OR10A5、OR10A6、OR10A7、OR10AD1、OR10AG1、OR10C1、OR10D3、OR10D4P、OR10G2、OR10G3、OR10G4、OR10G6、OR10G7、OR10G9、OR10H2、OR10H4、OR10J1、OR10J3、OR10J5、OR10K1、OR10K2、OR10P1、OR10Q1、OR10R2、OR10S1、OR10T2、OR10V1、OR10W1、OR10X1、OR10Z1、OR11A1、OR11G2、OR11H4、OR11H6、OR11H12、OR11L1、OR12D2、OR12D3、OR13A1、OR13C2、OR13C3、OR13C4、OR13C8、OR13D1、OR13F1、OR13G1、OR13H1、OR13J1、OR14A2、OR14A16、OR14C36、OR14I1、OR14J1、OR14K1、OR14L1P、OR51A1P、OR51A4、OR51A7、OR51B2、OR51B4、OR51B5、OR51B6、OR51D1、OR51E1, OR51E2, OR51F1, OR51F2, OR51F5P, OR51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, O R51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52 E4, OR52E5, OR52E8, OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, OR56B4) were adopted.

[0088] 353 human olfactory receptor genes were purchased from the TrueClone cDNA Clone collection (OriGene). Primers were designed based on the sequence information registered in GenBank. Using the PCR method with the human olfactory receptor gene as a template, fragments for subcloning of 353 types of human olfactory receptor genes were amplified. The subcloning fragments of each amplified gene were subcloned downstream of the Rho tag sequence of the Rho-pME18S vector (K. Kajiya et al., Journal of Neuroscience 15 August 2001, 21 (16) 6018-6025) using the EcoRI and XhoI sites to obtain expression vectors for 353 types of human olfactory receptors.

[0089] <1-2> Preparation of olfactory receptor expressing cells HEK293T cells expressing each of the 353 olfactory receptors were prepared as follows: The gene mixture shown in Table 1 and the transfection reagent mixture shown in Table 2 were prepared and incubated at room temperature for 5 minutes. The mixture was mixed with the transfection reagent mixture and dispensed at 12.5 mL per well of a poly-D-lysine coated 384 well plate and left to stand for 15 minutes in a clean bench. HEK293T cells (2.5 × 10 6 1.2 x 10 cells / 10 cm dish 5 The solution was adjusted to 1000 cells / mL, 25 mL of the solution was seeded in each well of a 384-well plate, and cultured overnight in an incubator maintained at 37°C and 5% CO2. In this way, 353 types of HEK293T cell cultures were obtained that were transfected with the expression vectors shown in Table 1 and appropriately expressed the genes encoded by those expression vectors.

[0090] [Table 1]

[0091] [Table 2]

[0092] <2> Creation of a database of human olfactory receptor activities <2-1> Luciferase assay The response of olfactory receptors to test substances was measured using olfactory receptor-expressing cells.

[0093] The 353 types of olfactory receptors expressed in HEK293T cells conjugate with Golf to activate adenylate cyclase, thereby increasing the amount of intracellular cAMP. In this example, the response of the olfactory receptor to the test substance was measured using a luciferase reporter gene assay, which monitors the increase in the amount of intracellular cAMP as an increase in the luminescence value derived from firefly luciferase. The "luciferase reporter gene assay" is also called the "luciferase assay." Firefly luciferase was detected by using the firefly luciferase gene carried in the pGL4.29[luc2P / CRE / Hygro] Vector. Renilla luciferase is expressed from the Renilla luciferase gene in a manner dependent on the amount of intracellular cAMP. The luminescence value derived from Renilla luciferase was used as an internal standard to correct for errors in gene transfer efficiency and cell number in each well. Renilla luciferase is constitutively expressed under the control of the TK promoter from the Renilla luciferase gene carried in the pGL4.74[hRluc / TK] Vector.

[0094] The test materials were The Good Scents Company (http: / / www.thegoodscentscompany.com / ) were selected from the 353 cultures obtained in <1-2> above. The medium was removed, and 15 mL of the 3021 test substance solutions was added to each of the cells, to obtain 353 × 3021 reaction solutions. Each test substance solution was prepared by dissolving each test substance in CD293 (Life Technologies, Inc.). In principle, the test substance concentration in the test substance solution was 300 mM. However, for test substances that showed cytotoxicity at 300 mM, the test substance concentration in the test substance solution was increased to 3 mM, 10 mM, and 20 mM. , 30 mM, or 100 mM. The test substance concentration in the medium was set to 1000 mM. The test substance concentration in the test substance solution was set to 0.1%, 0.3%, 1%, 3%, or 10%. The reaction mixture was placed in an incubator containing 5% CO2 and the cells were cultured for 4 hours. The luciferase gene was sufficiently expressed in the cells. The luminescence value derived from firefly luciferase in the cells was measured and taken as the "Luc value." In addition, the luminescence value derived from Renilla luciferase in the cells was The luminescence value was measured and defined as the "hRLuc value." The luminescence value derived from each luciferase was measured using the Dual-Glo luciferase assay system (Promega) according to the product's operating manual.

[0095] <2-2> Correction and normalization of reporter signals The signals obtained by luciferase assays are biased due to various factors such as experimental conditions, so if they are subjected to comprehensive analysis or statistical modeling as is, the desired results will not be obtained as bias will be detected. The reporter signals were corrected and normalized using the method described below, and a profile consisting of 353 types of olfactory receptor activity was obtained for each test substance.

[0096] <2-2-1> Elimination of reporter signal distribution distortion Generally, data distributions in which the overall data is positive and there is a limit to the number of low values, but no limit to the number of high values, show a skewed shape with a long tail to the right. The Luc and hRLuc values ​​obtained by reporter assay also showed a similar distribution with a long tail to the right. Data with such a skewed distribution is inappropriate for calculations such as the mean, variance, and standard deviation, so the skewed distribution was corrected by logarithmic transformation. In this case, the very rare observation of 0 occurred, so In order to avoid calculation errors, a logarithmic transformation was performed after adding 1 to each value. The "logR value" was calculated from the "logL value" and the hRLuc value.

[0097] <2-2-2> Internal standard correction of reporter signals The olfactory receptor activity signal Luc value (and logL value) obtained by the reporter assay is However, it is affected by the transfection efficiency and expression level of the exogenous gene. To correct for this, the internal standard value logR value was subtracted from the logL value to obtain the "logLR value."

[0098] <2-2-2> Correction of experimental differences in reporter signals The receptor activity information in the olfactory receptor activity database was obtained by accumulating data from a total of 356 experiments. The log LR value in each experimental run without the addition of the test substance (non-stimulation condition) was It was found that all receptors fluctuated up and down in unison for each experiment. In order to eliminate the effect of inter-experiment fluctuations in background values ​​that are not related to receptors or test substances, the median logLR value under non-stimulated conditions for each experiment was calculated, and the signal was corrected by subtracting it from the logLR value to obtain the "bgcLR value."

[0099] <2-2-3> Correction of basal activity of reporter signal Each olfactory receptor exhibits a certain degree of intrinsic activity (basal activity) even in the absence of an activator. In order to eliminate the influence of such test substance-independent and olfactory receptor-dependent bias, the median bgcLR value of each olfactory receptor under non-stimulation conditions in all experimental runs was calculated, and the signal was corrected by subtracting it from the bgcLR value to obtain the "Baselined value."

[0100] <2-2-4> Correction of non-specific activity of reporter signals Some of the test substances caused an increase or decrease in the reporter signal through activation or inhibition of signal transduction systems such as GPCRs endogenously expressed in cultured cells. In order to eliminate the influence of such test substance-dependent and olfactory receptor-independent bias, the median of the baseline value in the olfactory receptor-free condition (condition in which an empty vector was transfected instead of the olfactory receptor) for each test substance was calculated, and the signal was corrected by subtracting it from the baseline value to obtain the "OR-dependent value."

[0101] <2-2-5> Standardization of reporter signals The distribution of ORdependent values ​​in the entire olfactory receptor activity database roughly follows a normal distribution. All OR-dependent values ​​were standardized to a mean of 0 and a variance of 1 by dividing the deviation from the mean of the data by the standard deviation, and these were used as "response values."

[0102] <3> Screening of beef broth flavor enhancers based on olfactory receptor activation profiles <3-1> Selection of key ingredients for meat stock Based on a literature survey of papers and books, as well as an aroma analysis conducted by the inventors, 83 key components were extracted as aroma components related to the flavor of chicken broth, pork broth, and beef broth (chicken broth: 62 components, pork broth: 51 components, beef broth: 59 components).

[0103] <3-2> Sensory evaluation of flavor functions in beef stock As the flavor functions of meat stock, 26 types of flavor functions were set as the evaluation target (16 types of aroma, 5 types of taste, 5 types of mouthfeel). The 26 types of flavor functions include "meaty" and "burnt" which will be described later. The 59 beef broth key components selected in <3-1> were added to the beef broth evaluation solution to prepare 59 evaluation samples. The beef broth evaluation solution was set to an aqueous solution of 2.0% by weight of McCallum BEEF EXTRACT (MCCALUUM INDUSTRIES LIMITED) and 0.3% by weight of Nakuru M (Naika Salt Industry Co., Ltd.). The concentration of each key component added was determined to be a concentration at which the flavor of the added key component could be felt and there was no sense of incongruity. The temperature of the evaluation samples when eaten was 60°C. The flavor functions felt when drinking the evaluation samples and the ranking of their strength were evaluated by a consensus of three experienced evaluators. For the evaluation samples to which each key component was added, the reciprocal of the intensity ranking of the flavor functions felt was taken as the "function strength," and the strength of the flavor functions that were not felt was taken as 0, and the flavor functions were quantified in the range of 0 to 1. Using the above methods, a matrix (59 rows × 26 columns) was created containing the quantitative values ​​of 26 flavor functions for 59 key components of beef stock.

[0104] <3-3> Screening of beef broth flavor enhancers based on olfactory receptor activation profiles <3-3-1> Construction of a prediction model for the flavor enhancement effect of "meaty sensation" Regarding the 59 key ingredients of beef stock, the column of the “meaty feeling” function was taken from the matrix created in <3-2>. The objective variable is <2> From the olfactory receptor activity database created by A LASSO regression analysis was performed by extracting receptor activity as an explanatory variable. We set 100 different parameters λ and performed 5-fold cross-validation. The smallest λ was extracted, and the regression equation at that time was adopted as the model equation. The explanatory variables based on the olfactory receptors were reduced to 14, and the flavor function intensity of "meaty" was calculated based on the 14 odorants. A model was created to predict the activity of the sensory receptors.

[0105] <3-3-2> Construction of a predictive model for the flavor enhancement effect of "charred meat" Using a procedure similar to that in <3-3-1>, we created a model to predict the flavor function intensity of “burnt,” which corresponds to “burnt meat,” for 59 key components of beef broth based on the activity of 12 olfactory receptors.

[0106] <3-3-3> "Meaty" or "burnt" flavor function from olfactory receptor activity database Identification of candidate substances for enhancement The predicted functional values ​​were determined by substituting the olfactory receptor activation profile of each of the 3021 ingredients into the model formula created above. The 3021 ingredients were listed in order of highest predicted functional value. From the top-ranked ingredients, those that were recognized as food grade and easy to obtain were extracted as candidate substances.

[0107] <3-3-4> Extraction of candidate substances for the "meaty" or "burnt" flavor of beef broth Sensory evaluation of Noh The evaluation solution was 2.0 wt% McCallum BEEF EXTRACT (MCCALUUM INDUSTRIES LIMITED). The aqueous solution used was prepared by dissolving Nacle M (Naika Salt Co., Ltd.) in water to a salt concentration of 0.3 wt%. Each substance was added to the evaluation solution at the concentrations shown in Table 3, and the results were evaluated for "meatiness" or "saltiness." The "burnt" flavor function was determined by sensory evaluation. The sensory evaluation was performed by three evaluators deciding the sensory score of the samples to which each substance was added through a consensus. The sensory scores were as follows: 0.0: the "meaty" or "burnt" flavor function was not recognizable, 0.5: the "meaty" or "burnt" flavor function was recognizable but not the main function, and 1.0: the "meaty" or "burnt" flavor function was recognizable as the main function. "Main function" here means the most strongly recognized function (it may be multiple functions). The sensory scores of the evaluation solution before the addition of each substance were both 0.0 for the "meaty" and "burnt" flavor functions.

[0108] The results are shown in Table 3. For each substance evaluated, the function of enhancing the "meaty" or "burnt" flavor was In other words, the model formula created was used to determine whether the meatiness or burntness of the food was " It was confirmed that it is possible to screen for flavor enhancing substances.

[0109] [Table 3]

[0110] <3-3-5> Construction and evaluation of a prediction model for the flavor enhancement effect of "meaty sensation" The objective variables and objective variables were the data of the 59 evaluation samples and the data of the beef broth evaluation solution to which no beef broth key ingredients had been added (i.e., data with both sensory data and OR activity of 0). Except for adding the above, the flavor function of "meaty" was created by following the same procedure as in <3-3-1>. A model was developed to predict the intensity based on olfactory receptor activity.

[0111] The values ​​obtained by substituting the olfactory receptor activation profile of each of the 3021 components into the model formula created above were used as predicted functional values. The 3021 components were listed in order of highest predicted functional value. As a result, the same candidate substances were listed at the top as when the model formula created in <3-3-1> was used. In other words, it became clear that a predictive model could be successfully constructed even if blank data (here, data on beef broth evaluation solution to which no key components of beef broth had been added) were included.

[0112] <4> Screening of oyster sauce flavor enhancers based on olfactory receptor activation profiles <4-1> Sensory evaluation of the flavor function of oyster sauce Sixteen types of oyster sauce were selected as samples. Sixteen flavor functions were set as the evaluation targets for oyster sauce. The 16 flavor functions include "metallic" and "caramelic," which will be described later. This oyster sauce was selected as a control, and the intensity of each flavor function for this oyster sauce was scored in the range of 0 to 3. Using this score as a standard, the intensity of each flavor function for the other 15 types of oyster sauce was also scored in the range of 0 to 3 points.

[0113] <4-2> Extraction of oyster sauce flavor enhancers based on olfactory receptor activation profiles <4-2-1> Construction of a prediction model for the enhancement of metallic flavor function For 16 types of oyster sauce, the metallic function value expressed in points from 0 to 3 was used as the objective variable. <2> A LASSO regression analysis was performed by extracting the activities of 353 olfactory receptors from the olfactory receptor activity database created in and using them as explanatory variables. The parameter λ, which determines the strength of regularization, was set in multiple ways and leave-one-out cross-validation was performed. The λ that minimized the regression error was extracted and the regression equation at that time was adopted as the model equation. In this way, the explanatory variables of the 353 olfactory receptors were reduced to 13, and a model equation was created that predicts the flavor function intensity of functional metallics using the activity of the 13 olfactory receptors. The model equation was created under the condition that it passed through the origin.

[0114] <4-2-2> Construction of a prediction model for the flavor enhancement effect of "Caramelic" Using the same procedure as in <4-2-1>, we created a model to predict the flavor function intensity of "caramelic" for 16 types of oyster sauce based on the activity of 10 olfactory receptors.

[0115] <4-2-3> Extraction of candidate substances for enhancing "metallic" or "caramelic" flavor functions from the olfactory receptor activity database The predicted functional values ​​were determined by substituting the olfactory receptor activation profile of each of the 3021 ingredients into the model formula created above. The 3021 ingredients were listed in order of predicted functional value, and from the top of the list, those that were approved as food grade and owned by the applicant were extracted.

[0116] <4-2-4> Sensory evaluation of the extracted candidate substances for their "metallic" or "caramelic" flavor functions in oyster sauce The evaluation solution was an aqueous solution obtained by diluting the control oyster sauce with hot water (also referred to as the "additive-free oyster sauce sample"). Each substance was added to the evaluation solution (additive-free oyster sauce sample) at the concentrations shown in Table 4, and the "metallic" or "caramelic" flavor function was determined by sensory evaluation. The sensory evaluation was performed by three evaluators deciding the sensory score of the samples to which each substance was added in a range of 0 to 3 points based on the sensory score of the additive-free oyster sauce sample as the standard. The sensory scores of the additive-free oyster sauce sample were 1.0 for metallic and 2.5 for caramelic.

[0117] The results are shown in Table 4. For each substance evaluated, the following was determined: "Metallic" or "Caramelic" The flavor enhancing effect was confirmed. In other words, it was confirmed that the created model formula can be used to screen for "metallic" or "caramelic" flavor enhancing substances.

[0118] [Table 4]

[0119] <4-3> Construction and evaluation of a predictive model for the flavor enhancement effect of "Caramelic" One type of soy sauce was selected as a sample. The strength of the "caramelic" flavor function of the soy sauce was scored in the range of 0 to 3 points, based on the score of the "caramelic" flavor function of the control oyster sauce in <4-1>.

[0120] A model was created to predict the “caramelic” flavor functional intensity based on olfactory receptor activity using the same procedure as in <4-2-2>, except that the data on soy sauce was added to the data on 16 types of oyster sauce as the objective variable and the objective variable.

[0121] The values ​​obtained by substituting the olfactory receptor activation profile of each of the 3021 components into the model formula created above were used as predicted functional values. The 3021 components were listed in order of highest predicted functional value. As a result, candidate substances different from those listed in the case of using the model formula created in <4-2-2> were listed at the top.

[0122] The "caramelic" flavor enhancing effect of the top-ranked candidate substances was measured using the same procedure as in <4-2-4>. As a result, the samples to which the candidate substances were added had a 2.6 The candidates showed a "caramelic" flavor function score of ~2.9, i.e., The results showed that the "tasty" flavor function was enhanced by the "tasty" flavor of the tasty food. In other words, it became clear that a predictive model can be successfully constructed even when data of different foods are included. It also became clear that by using a predictive model constructed including data of different foods, different candidate substances can be screened compared to when a predictive model constructed without data of different foods is used. In other words, it was suggested that by using a predictive model constructed including data of different foods, it may be possible to screen candidate substances that cannot be screened when a predictive model constructed without data of different foods is used.

Claims

1. A method for producing a predictive model, comprising: the prediction model is a model that predicts a target function of a test substance based on a receptor activation profile of the test substance; The target function is a function of enhancing or suppressing the flavor function of the target food, The method includes constructing the predictive model based on the following data (A) and (B): (A) Data reflecting differences in receptor activation profiles among the foods comprising the control food group; (B) data indicating the flavor function of each of the plurality of foods; Including, the control food group includes variations of the target food group; the receptor activation profile of the test substance is a receptor activation profile for a first receptor; the receptor activation profile of (A) is a receptor activation profile for a second receptor, The method, wherein the second receptor comprises the first receptor.

2. The method according to claim 1 , wherein the step is a step of constructing the prediction model by regression analysis using the data (A) as explanatory variables and the data (B) as a response variable.

3. The method of claim 2 , wherein variables are reduced in the regression analysis.

4. The method of claim 3 , wherein the reduction of variables is a reduction of explanatory variables.

5. The method according to claim 3 or 4, wherein the reduction of variables is performed by regularization.

6. The method of claim 5 , wherein the regularization is performed by Lasso regression.

7. The method of claim 1 or 2, wherein cross-validation is performed in the regression analysis.

8. The method of claim 1 or 2, wherein the predictive model comprises a regression equation that outputs a predicted value of the target function based on the receptor activation profile of the test substance.

9. The method of claim 8 , wherein the regression equation is a linear regression equation that passes through the origin.

10. The method according to claim 1 or 2, wherein the group of variations is a group consisting of a plurality of foods of the same type as the food product, but which have different manufacturing ingredients, manufacturing conditions, and / or manufacturers.

11. The method according to claim 1 or 2, wherein the variation group is the following group (1) or (2): (1) A group consisting of two or more variations of any one of the target foods, each of which has increased content of two or more ingredients; (2) A group consisting of two or more products of the target food product from different manufacturers.

12. The method according to claim 1 or 2, wherein the data (A) is the following data (A1) or (A2): (A1) a receptor activation profile for the second receptor of components whose contents differ among foods selected from the control food group; (A2) Receptor activation profiles for the second receptor for each of the multiple foods that make up the control food group.

13. The control food group consists of the variation group of group (1), The method of claim 11 , wherein the data (A) is a receptor activation profile for the second receptor of each of the two or more components.

14. The control food group includes the variation group of group (2), The method according to claim 11, wherein the data (A) is a receptor activation profile for the second receptor for each of a plurality of foods constituting the control food group.

15. The method according to claim 1 or 2, wherein the number of variations constituting the group of variations is 10 or more.

16. 3. The method of claim 1 or 2, wherein the ratio of the target food in the control food group is 80% or more.

17. The method of claim 1 or 2, wherein the number of first receptors is 5 or more.

18. 3. The method of claim 1 or 2, wherein the number of second receptors is 300 or more.

19. 3. The method of claim 1 or 2, wherein the second receptor is selected from the group consisting of an olfactory receptor and a gustatory receptor.

20. 50% or more of the total number of the second receptors are selected from the group consisting of OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D5, OR1E1, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR2A1, OR2A2, OR2A4, OR2A5, OR2A12, OR2A14, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P, OR2AK 2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C16, OR4C46, OR 4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K2, OR4K5, OR4K13, OR4 K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR5A1, OR5A2, OR5AC2, OR5AK2, OR5AK3P, OR5AN1, OR5AP2, OR5AR1, OR5AS1, OR5AU1, OR5B2, OR5B3, OR5B12, OR5B17, OR5B21, OR5C1, OR5D13, OR5D14, OR5D16, OR5D18, OR5F1, OR5H1, OR5H2, OR5H6, OR5H14,OR5I1、OR5J2、OR5K1、OR5K3、OR5K4、OR5L2、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1、OR5W2、OR6A2、OR6B1、OR6B2、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G2、OR8G5、 , OR8H3, OR8I2, OR8J1, OR8J3, OR8K1, OR8K3, OR8K5, OR8S1, OR8U1, OR9A4, OR9G1, OR9G4, OR9I1, OR9K2, OR9Q1, OR9Q2, OR10A3, OR10A4, OR10A5, OR10A6, OR10A7, OR10AD1, OR10AG1, OR10C1, OR10D3, OR10D4P, OR10G2, OR10G3, OR10G4, OR10G6, OR10G7, OR1 0G9, OR10H2, OR10H4, OR10J1, OR10J3, OR10J5, OR10K1, OR10K2, OR10P1, OR10Q1, OR10R2, OR10S1, OR10T2 , OR10V1, OR10W1, OR10X1, OR10Z1, OR11A1, OR11G2, OR11H4, OR11H6, OR11H12, OR11L1, OR12D2, OR12D3, O R13A1, OR13C2, OR13C3, OR13C4, OR13C8, OR13D1, OR13F1, OR13G1, OR13H1, OR13J1, OR14A2, OR14A16, OR1 4C36, OR14I1, OR14J1, OR14K1, OR14L1P, OR51A1P, OR51A4, OR51A7, OR51B2, OR51B4, OR51B5, OR51B6, OR5 1D1, OR51E1, OR51E2, OR51F1, OR51F2, OR51F5P, OR51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M 1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, O From R52E4, OR52E5, OR52E8, OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4 3. The method of claim 1 or 2, wherein the compound is selected from the group consisting of:

21. the second receptor is OR1A1, OR1A2, OR1B1, OR1C1, OR1D2, OR1D5, OR1E1, OR1F1, OR1F12, OR1G1, OR1I1, OR1J1, OR1J2, OR1J4, OR1K1, OR1L1, OR1L3, OR1L4, OR1L8, OR1M1, OR1N1, OR1N2, OR1Q1, OR1R1P, OR1S1, OR2A1, OR 2A2, OR2A4, OR2A5, OR2A12, OR2A14, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P, OR2AK2, OR2AP1, OR2AT 4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H 2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T 5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, O R3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR 4C15, OR4C16, OR4C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6 、OR4F14P、OR4F15、OR4G11P、OR4H12P、OR4K1、OR4K2、OR4K5、OR4K13、OR4K14、OR4K15、OR4K17、OR4L1、OR4M1、OR4N2、OR4N4、OR4N5、OR4P4、OR4Q3、OR4S1、OR4S2、OR4X1、OR4X2、OR5A1、OR5A2、OR5AC2、OR5AK2、OR5AK3P、OR5AN1、OR5AP2、OR5AR1、OR5AS1、OR5AU1、OR5B2、OR5B3、OR5B12、OR5B17、OR5B21、OR5C1、OR5D13、OR5D14、OR5D16、OR5D18、OR5F1、OR5H1、OR5H2、OR5H6、OR5H14、OR5I1、OR5J2、OR5K1、OR5K3、OR5K4、OR5L2 、OR5M3、OR5M8、OR5M9、OR5M10、OR5M11、OR5P3、OR5R1、OR5T1、OR5T2、OR5T3、OR5V1 、OR5W2、OR6A2、OR6B1、OR6B2、OR6C1、OR6C2、OR6C3、OR6C4、OR6C6、OR6C65、OR6C66P、OR6C68、OR6C70、OR6C74、OR6C75、OR6C76、OR6F1、OR6J1、OR6K2、OR6K3、OR6K6、OR6M1、OR6N1、OR6N2、OR6P1、OR6Q1、OR6S1、OR6T1、OR6V1、OR6X1、OR6Y1、OR7A3P、OR7A5、OR7A10、OR7A17、OR7C1、OR7C2、OR7D2、OR7D4、OR7E24、OR7G1、OR7G2、OR7G3、OR8A1、OR8B3、OR8B4、OR8B8、OR8B12、OR8D1、OR8D2、OR8D4、OR8G2、OR8G5、OR8H3、OR8I2、OR8J1、OR8J3、OR8K1、OR8K3、OR8K5、OR8S1、OR8U1、OR9A4、OR9G1、OR9G4、OR9I1、OR9K2、OR9Q1、OR9Q2、OR10A3、OR10A4、OR10A5、OR10A6、OR10A7、OR10AD1、OR10AG1、OR10C1、OR10D3、OR10D4P、OR10G2、OR10G3、OR10G4、OR10G6、OR10G7、OR10G9、OR10H2、OR10H4、OR10J1、OR10J3、OR10J5、OR10K1、OR10K2、OR10P1、OR10Q1、OR10R2、OR10S1、OR10T2、OR10V1、OR10W1、OR10X1、OR10Z1、OR11A1、OR11G2、OR11H4、OR11H6、OR11H12、OR11L1、OR12D2、OR12D3、OR13A1、OR13C2、OR13C3、OR13C4、OR13C8、OR13D1、OR13F1、OR13G1、OR13H1、OR13J1、OR14A2、OR14A16、OR14C36、OR14I1、OR14J1、OR14K1、OR14L1P , OR51A1P, OR51A4, OR51A7, OR51B2, OR51B4, OR51B5, OR51B6, OR51D1, OR51E1, OR51E2, OR51F1, OR51F2, OR51F5P, OR51G1, OR51G 2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, The method of claim 1 or 2, comprising 50% or more of OR52D1, OR52E2, OR52E4, OR52E5, OR52E8, OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4.

22. 3. The method of claim 1 or 2, wherein the second receptor is a human receptor.

23. 3. The method of claim 1 or 2, wherein the target flavor function is a function that exhibits retronasal aroma, taste, or mouthfeel.

24. The method according to claim 1 or 2, comprising the step of acquiring the data (A) and / or the step of acquiring the data (B).

25. A method for predicting a target function of a test substance, comprising: The target function is a function of enhancing or suppressing the flavor function of the target food, The method comprises predicting the target function of a test substance based on the receptor activation profile of the test substance using a predictive model produced by the method of claim 1. Includes The method, wherein the receptor activation profile of the test substance is the receptor activation profile for the first receptor.

26. A method for screening a substance having a target function, comprising: The target function is a function of enhancing or suppressing the flavor function of the target food, The method comprises predicting the target function of a test substance based on the receptor activation profile of the test substance using a predictive model produced by the method of claim 1. Includes the receptor activation profile of the test substance is a receptor activation profile for the first receptor; When the test substance is predicted to have the target function, the test substance is selected as a candidate substance having the target function.

27. 27. The method of claim 25 or 26, further comprising evaluating the results of said prediction.

28. 28. The method of claim 27, wherein the evaluation is performed by sensory evaluation.

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