Method for predicting formulation exhibiting target sensory quality, and method for reconfiguring target sensory quality

Abstract

Provided are: a method for predicting a formulation that exhibits target sensory quality; and a method for reconfiguring target sensory quality by using the method. By identifying two or more substances providing a composite profile that approximates the receptor activity profile corresponding to the target sensory quality and a composite ratio of the receptor activity profiles of those substance, a formulation that exhibits the target sensory quality is predicted, and the target sensory quality is reconfigured based on the predicted formulation.

Description

Methods for predicting formulations that exhibit target sensory qualities and methods for reconstituting target sensory qualities

[0001] The present invention relates to a method for predicting formulations that will exhibit a target sensory quality and a method for utilizing the same to reconstitute the target sensory quality.

[0002] Sensory qualities such as aroma and taste are important factors that influence the palatability of foods, cosmetics, etc. Therefore, techniques for screening components necessary to reproduce specific sensory qualities and techniques for reproducing specific sensory qualities by combining multiple components are industrially important techniques for developing foods, cosmetics, etc.

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

[0004] In recent years, methods have been reported for screening substances that exhibit sensory qualities such as specific aromas or tastes using the responses of receptors such as olfactory receptors and gustatory receptors as indicators (Patent Documents 1 and 2, etc.).

[0005] In addition, a method has been reported for reproducing a target aroma by optimizing the blending of components so that the resulting composite spectrum obtained by combining the MS spectra of multiple components resembles the MS spectrum of a target substance that exhibits the target aroma (e.g., Non-Patent Document 1). However, this method can only mimic the chemical structure contained in the target substance that exhibits the target aroma, and cannot reproduce the target aroma by blending components with different chemical structures.

[0006] In addition, a method for reproducing a target odor by optimizing the blending so that a composite vector obtained by correcting 21 physicochemical feature vectors of multiple components with odor intensity and combining them approximates the 21 physicochemical feature vectors of a target object exhibiting the target odor (Non-Patent Document 2). However, in this method, it is questionable whether the 21 physicochemical feature vectors adequately reflect the odor. Furthermore, since this method requires information on odor intensity, a sensory test is required, and there is room for improvement in throughput and accuracy.

[0007] Patent Publication No. 2019-037197 Patent Publication No. 2018-014999

[0008] Dani Prasetyawan and Nakamoto Takamichi, Sensory Evaluation of Odor Approximation Using NMF with Kullback-Leibler Divergence and Itakura-Saito Divergence in Mass Spectrum Space, Journal of The Electrochemical Society, 2020 J. Electrochem. Soc. 167 167520.Aharon Ravia et al., A measure of smell enables the creation of olfactory metamers, Nature volume 588, pages118-123 (2020)

[0009] The present invention aims to provide a method for predicting a formulation that will exhibit a target sensory quality, and a method for reconstructing the target sensory quality using the same method.

[0010] As a result of intensive research to solve the above-mentioned problems, the inventors have discovered that by identifying two or more substances and the composite ratio of the receptor activity profiles of those substances that give a composite profile that approximates the receptor activity profile corresponding to the target sensory quality, it is possible to predict a formulation that will exhibit the target sensory quality, and further, to reconstitute the target sensory quality based on the predicted formulation, thereby completing the present invention.

[0011] That is, the present invention can be exemplified as follows: [1] A method for predicting a formulation exhibiting a target sensory quality, comprising: a step of identifying two or more target substances and a composite ratio of receptor activity profiles of the target substances that gives a composite profile approximating the target profile, wherein the receptor activity profiles are receptor activity profiles for two or more receptors, respectively; the target profile is a receptor activity profile corresponding to the target sensory quality for the receptor; and a formulation of the target substances at the composite ratio is considered to be a formulation exhibiting the target sensory quality. [2] The method (specifically described in [1]) wherein the target substances consist of 2 to 8 substances. [3] The method (specifically described in [1] or [2]) wherein, in the step, the target substances are selected from a group of test substances. [4] The method (specifically described in [3]) wherein the group of test substances consists of more types of substances than the target substance. [5] The method (specifically described in [3] or [4]) wherein the group of test substances consists of 100 or more substances. [6] The method (specifically described in any of [1] to [5]) wherein the step is performed by non-negative constraint sparse modeling, with the target profile as a response variable and receptor activity profiles for the receptor of each substance constituting the test substance group as explanatory variables. [7] The method (specifically described in [6]) wherein the sparse modeling is performed using a Lasso regression model. [8] The method (specifically described in any of [1] to [7]) wherein the step is performed so that the error between the target profile and the composite profile, converted to a weighted RMSE, is 0.7 or less. [9] The method (specifically described in any of [1] to [8]) wherein the receptor consists of 300 or more types of receptors.

[10] The method (specifically described in any of [1] to [9]) wherein the receptor is selected from the group consisting of G protein-coupled receptors and ionotropic receptors.

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

[10] ) above, wherein the receptors are selected from the group consisting of olfactory receptors and gustatory receptors.

[12] 50% or more of the total number of the receptors areOR1A1、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、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、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, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52 A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52E8, OR52H1, OR52I2, OR

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

[11] ) above, wherein the target gene is selected from the group consisting of OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4. The receptors are 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, OR2A K2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR 2G3, OR2G6, OR2H1, OR2H2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M4, OR2M7, OR 2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C 5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, 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, OR13 C3, OR13C4, OR13C8, OR13D1, OR13F1, OR13G1, OR13H1, OR13J1, OR14A2, OR14A16, OR14C36, OR14I1, OR14J1, O R14K1, OR14L1P, OR51A1P, OR51A4, OR51A7, OR51B2, OR51B4, OR51B5, OR51B6, OR51D1, OR51E1, OR51E2, OR51F1 , OR51F2, OR51F5P, OR51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51 V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52E8, OR52H1, OR52

[14] The method (specifically described in any of [1] to

[13] ) wherein the receptor is a human receptor.

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

[14] ) further comprising a step of evaluating the sensory quality of the predicted blend.

[16] The method (specifically described in

[15] ) wherein the evaluation is performed by sensory evaluation.

[17] For each of a plurality of targets, identifying a composite ratio of the two or more target substances and the receptor activity profiles of those target substances that gives the composite profile that approximates the target profile;The method (specifically described in [1] to

[16] ) includes a minimum set determination step of determining a predetermined number of synthetic elements and synthetic ratios of those synthetic elements that approximate the synthetic profile of the identified multiple targets, as well as one or more target substances that constitute each of the synthetic elements.

[18] The method (specifically described in

[17] ) includes decomposing the synthetic profile of the multiple targets into the predetermined number of synthetic elements and synthetic ratios of those synthetic elements by nonnegative matrix factorization (NMF) in the minimum set determination step.

[19] A method for reconstructing the sensory quality of a target, comprising: a step of predicting a formulation that will exhibit the sensory quality of the target by the method (specifically described in any of [1] to

[16] ); and a step of blending the target substances based on the formulation.

[20] The method (specifically described in

[19] ) wherein the target substances are blended in a blending ratio that is substantially the same as the composite ratio.

[21] The method (specifically described in

[20] ) wherein the blending ratio for each target substance is 0.9 to 1.1 times the synthesis ratio.

[22] A method for reconstructing the sensory quality of a target, comprising: a step of determining a predetermined number of synthesis elements and their synthesis ratios, and one or more target substances constituting each of the synthesis elements, that approximate the synthesis profile of the multiple targets, by the method (specifically described in

[17] or

[18] ); and a step of blending the synthesis elements based on the synthesis ratios of the synthesis elements.

[0012] According to the present invention, it is possible to predict a formulation that will exhibit a target sensory quality, and also to reconstruct the target sensory quality based on the predicted formulation.

[0013] FIG. 1 is a diagram for explaining the reconstruction of a target set. FIG. 2 is a diagram showing a basis matrix A. FIG. 3 is a diagram showing a coefficient matrix H. FIG. 4 is a diagram showing a reconstructed recipe matrix W. FIG. 5 is a diagram showing an approximated matrix AH. FIG. 6 is a diagram showing the relationship between the number of aromatic cartridges in a minimum set and the approximation accuracy using the approximated matrix AH. FIG. 7 is a diagram showing a basis matrix A. FIG. 8 is a diagram showing a coefficient matrix H. FIG. 9 is a diagram showing a target profile matrix W. FIG. 10 is a diagram showing an approximated matrix AH.

[0014] The present invention will be described in detail below.

[0015] <1> Method of the Present Invention One aspect of the method of the present invention is a method for predicting a formulation that exhibits a target sensory quality. This method is also referred to as the "prediction method of the present invention." A formulation that exhibits the target sensory quality is also referred to as the "target formulation."

[0016] One aspect of the method of the present invention is a method for reconstituting a target sensory quality, which method is also referred to as the "reconstitution method of the present invention."

[0017] <1-1> Sensory Quality "Target sensory quality" refers to the sensory quality targeted in the method of the present invention. Specifically, "target sensory quality" refers to the sensory quality exhibited by the formulation that is the target of prediction in the prediction method of the present invention. Furthermore, "target sensory quality" also refers to the sensory quality that is the target of reconstitution in the reconstitution method of the present invention. The type of target sensory quality is not particularly limited. Sensory quality includes sensations felt in the oral cavity and / or nasal cavity when using the target object (e.g., when eating). The target object may be composed of a single component (i.e., a pure substance) or a combination of two or more components (i.e., a mixture). Examples of targets include aroma components, taste components, and foods and cosmetics containing them. Specific examples of sensory quality include aroma, taste, and flavor. The target sensory quality may be one type of sensory quality or a combination of two or more sensory qualities.

[0018] As scents, there are absinthe, acacia, acai, acerola, acetic, acetone, acidic, acorn, acrylate, agarwood, alcoholic, aldehydic, alfalfa, algae, alliaceous, allspice, almond, bitter almond, roasted almond, toasted almond, amber, ambergris, ambrette, ammoniacal, angelica, animal, anise, anisic, apple, cooked apple, dried apple, green apple, red apple, apple skin, apricot, aromatic, arrack, artichoke, asafetida, asparagus, astringent, autumn, avocado, bacon, baked, balsamic, banana, banana peel, ripe banana, unripe banana, roasted barley, basil, bay, green bean, beany, beef juice, beefy, roasted beefy, beer, beeswax, benzoin, bergamot, berry, ripe berry, bitter, blackberry, bloody, blueberry, bois de rose, boronia, bouillon, boysenberry, brandy, baked bread, bread crust, rye bread, bready, broccoli, brothy, brown, bubble gum, buchu, burnt, rancid butter, buttermilk, butterscotch, buttery, cabbage, calamus, camphoreous, cananga, candy, cantaloupe, capers, caramellic, caraway, cardamom, carnation, carrot, carrotseed、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 redcurrant、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、fruit tropical 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、juicyfruit、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、linden flower、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 greenpea、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、plum skin、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 burntsugar、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、tomato leaf、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 (smokedfish), laurel leaves, light, mothballs, mouse, nail polish remover, new rubber, peanut butter, perfumery, putrid, four, decay, 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, wet One example is dog.

[0019] The tastes include sweet, salty, sour, bitter, umami, kokumi, and fatty tastes.

[0020] Flavors include those corresponding to various foods or their ingredients.

[0021] <1-2> Prediction Method of the Present Invention Prediction of a target formulation can be carried out by identifying a synthesis ratio of two or more target substances and the receptor activity profiles of those target substances that gives a synthesis profile similar to the target profile. That is, the method of the present invention is a method for predicting a formulation exhibiting a target sensory quality, and may include a step of identifying a synthesis ratio of two or more target substances and the receptor activity profiles of those target substances that gives a synthesis profile similar to the target profile. This step is also referred to as an "identification step." The synthesis ratio is also referred to as a "synthesis ratio of the target substances" or simply as a "synthesis ratio." The synthesis profile is also referred to as a "synthesis profile of the target substances" or simply as a "synthesis profile." The term "synthesis profile" may be used interchangeably with the term "reconstituted profile." The prediction method of the present invention is expected to enable accurate prediction of a target formulation with high throughput, particularly compared to prediction of a target formulation based on sensory evaluation.

[0022] "The synthetic ratio of two or more target substances and the receptor activity profiles of those target substances gives a synthetic profile that approximates the target profile" means that the synthetic profile obtained by synthesizing the receptor activity profiles of the target substances at that synthetic ratio approximates the target profile.

[0023] The term "target profile" refers to a receptor activity profile corresponding to a target sensory quality. The term "receptor activity profile corresponding to a target sensory quality" may refer to the receptor activity profile of an object exhibiting the target sensory quality. An object exhibiting the target sensory quality may consist of a single component (i.e., a pure substance) or a combination of two or more components (i.e., a mixture). When an object exhibiting the target sensory quality is a mixture, each component constituting the mixture may or may not exhibit the target sensory quality individually, as long as the mixture exhibits the target sensory quality. Examples of objects exhibiting the target sensory quality include aroma components, taste components, and foods and cosmetics containing them. An object exhibiting the target sensory quality may be a real object or a virtual object. In other words, the target profile may be a real receptor activity profile or a virtual receptor activity profile. That is, the target profile may specifically be a receptor activity profile of an actual object exhibiting the target sensory quality, or a virtual receptor activity profile based on the target sensory quality. The target profile may or may not be publicly known. If the target profile is not publicly known, it may be acquired as appropriate before the identification step is performed. That is, the method of the present invention may include a step of acquiring a target profile before the identification step is performed. A target profile of an actual object can be acquired, for example, by a method for acquiring a receptor activity profile of a substance, which will be described later. Furthermore, a virtual receptor activity profile can be constructed, for example, based on the correlation between sensory quality and receptor activity profile.Specifically, a virtual receptor activity profile can be constructed by, for example, (1) constructing a model that explains or predicts target quality (e.g., based on a developer's evaluation) and preference scores (e.g., based on consumer testing) from receptor activity profiles for multiple objects; (2) using the model to reverse-search for a receptor activity profile that provides a desirable target quality or preference score (e.g., maximizes the target quality or preference score) using analytical techniques such as inverse problem analysis or simulation; and (3) setting the reverse-searched receptor activity profile as a target profile.

[0024] "Target substance" refers to each substance identified in the identification step. In the identification step, the target substance may be selected from a group of test substances.

[0025] Each target substance may consist of a single component (i.e., a pure substance) or may consist of a combination of two or more components (i.e., a mixture).

[0026] The number of target substances is two or more. The number of target substances may be set to a number appropriate for reconstituting the target sensory quality, for example. The number of target substances 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, or 20 or more; or 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, or 3 or less, or any compatible combination thereof. Specific examples of the number of target substances include 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, or 20 to 25. Specific examples of the number of target substances include 2 to 15, 2 to 10, 2 to 8, or 2 to 4.

[0027] The term "test substance group" refers to a group of substances used as candidates for target substances. Each substance constituting the test substance group is also referred to as a "test substance."

[0028] The test substance is not particularly limited as long as its receptor activity profile can be utilized. Each test substance may consist of 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 and composition ratio of the components constituting the mixture are not particularly limited. The test substance may be a known substance or a novel substance. The test substance may be a natural product or an artificial substance. The test substance may be, for example, a compound library created using combinatorial chemistry techniques. The test substance may be, for example, a prototype product for reconstituting a target sensory quality. Test substances include aroma components, flavor components, and various other food and cosmetic components, as well as foods and cosmetics containing them. Specific examples of test substances include alcohols, ketones, aldehydes, ethers, esters, hydrocarbons, sugars, organic acids, nucleic acids, amino acids, peptides, and various other organic or inorganic components. Furthermore, test substances particularly include existing food additives. "Existing food additive" means a substance that has already been approved for use as a food additive. The receptor activity profile of the test substance may or may not be publicly known. If the receptor activity profile of the test substance is not publicly known, the receptor activity profile of the test substance may be obtained as appropriate before the identification step is carried out. That is, the method of the present invention may include a step of obtaining the receptor activity profile of the test substance before the identification step is carried out. The receptor activity profile of the test substance can be obtained, for example, by the method for obtaining a receptor activity profile of a substance described below.

[0029] The number of test substances is 2 or more. The number of test substances may be the same as or greater than the number of target substances. The number of test substances may particularly be greater than the number of target substances. The number of test substances may be, for example, 2 or more, 5 or more, 10 or more, 20 or more, 50 or more, 100 or more, 200 or more, 500 or more, 1000 or more, 2000 or more, 500 or more, 1000 or more, 2000 or more, 50000 or more, 10,000 or more, 20,000 or more, 50,000 or more, 100,000 or more, 200,000 or more, or 500,000 or more, or 1,000,000 or less, 500,000 or less, 200,000 or less, 100,000 or less, 50,000 or less, 20,000 or less, 10,000 or less, 5000 or less, 200 or less, 1000 or less, 500 or less, 200 or less, 100 or less, 50 or less, 20 or less, 10 or less, or 5 or less, or any compatible combination thereof. The number of test substances may particularly be 50 or more, 100 or more, 200 or more, 500 or more, 1000 or more, or 2000 or more. The number of test substances may specifically be, for example, 2 to 5, 5 to 10, 10 to 20, 20 to 50, 50 to 100, 100 to 200, 200 to 500, 500 to 1000, 1000 to 2000, 2000 to 5000, 5000 to 10000, 10,000 to 20,000, 20,000 to 50,000, 50,000 to 100,000, 100,000 to 200,000, 200,000 to 500,000, or 500,000 to 1,000,000. The number of test substances may specifically be, for example, 2 to 1,000,000, 2 to 100,000, 2 to 10,000, 2 to 1,000, 2 to 100, 100 to 1,000,000, 100 to 100,000, 100 to 10,000, 100 to 1,000, 1,000 to 1,000,000, 1,000 to 100,000, or 1,000 to 10,000.

[0030] The term "receptor activity profile" refers to data indicating the presence or absence or degree of the property of activating or inactivating a receptor. In other words, the "receptor activity profile" of a substance may refer to data indicating whether the substance activates or inactivates a receptor, or the extent to which the substance activates or inactivates a receptor. Furthermore, the "receptor activity profile" of a substance for two or more types of receptors may refer to the pattern of which receptors the substance activates or inactivates and which receptors it does not activate or inactivate, or the pattern of the extent to which the substance activates or inactivates each receptor. The receptor activity profile for two or more types of receptors can be treated, for example, as an N-dimensional vector (N is the number of receptors). In the description of the receptor activity profile, "substance" may be read as "object" or "component." The receptor activity profile is also referred to as "receptor activation property."

[0031] The receptor activity profile used is a receptor activity profile for two or more receptors. The two or more receptors selected are common to the receptor activity profile of each target substance, the receptor activity profile of each test substance, and the target profile.

[0032] The receptor is not particularly limited. Examples of the receptor include G protein-coupled receptors and ion channel receptors. Examples of the receptor include olfactory receptors and gustatory receptors. An olfactory receptor can be an example of a G protein-coupled receptor. A gustatory receptor can be an example of a G protein-coupled receptor or an ion channel receptor.

[0033] 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, OR2 A14, OR2A25, OR2AE1, OR2AG1, OR2AG2, OR2AJ1P, OR2AK2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2F2, OR 2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J1P, OR2J2, OR2J3, OR2K2, OR2L2, OR2L3, OR2L5, OR2L8, OR2L13, OR2M2, OR2M3, OR2M4, OR2M5, OR2M7, OR2S2, OR2T1 , OR2T2, OR2T3, OR2T4, OR2T5, OR2T6, OR2T7, OR2T8, OR2T10, OR2T11, OR2T12, OR2T27, OR2T29, OR2T33, OR2T34, OR2T35, OR2V1, OR2V2, OR2W1, OR2W3, O R2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A4P, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4 C16, OR4C45, OR4C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F4, OR4F5, OR4F6, OR4F14P, OR4F15, OR4F17, OR4F21, OR4 G11P, OR4H12P, 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, OR1 1H12, OR11L1, OR12D2, OR12D3, OR13A1, OR13C2, OR13C3, OR13C4, OR13C5, OR13C8, OR13C9, OR13D1, OR13F1 , OR13G1, OR13H1, OR13J1, OR14A2, OR14A16, OR14C36, OR14I1, OR14J1, OR14K1, OR14L1P, OR51A1P, OR51A2 , OR51A4, OR51A7, OR51B2, OR51B4, OR51B5, OR51B6, OR51D1, OR51E1, OR51E2, OR51F1, OR51F2, OR51F5P, OR 51G1, OR51G2, OR51H1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A 4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52E6, OR52E8, OR52H1, OR52I1, OR 52I2, 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").

[0034] 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, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, O R2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C 16, OR4C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K2, OR4K 5, OR4K13, OR4K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR5A1, OR5A2, OR5AC2, OR5AK2, OR5 AK3P, 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, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, O R52E5, 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").

[0035] Further examples of G protein-coupled receptors include type I taste receptors (Tas1R), type II taste receptors (Tas2R), glucose transporters (GLUTs), sodium / glucose cotransporters (SGLTs), calcium-sensing receptors (CasRs), metabotropic glutamate receptors (mGluRs), CD36, free fatty acid receptors (FFARs), and other G protein-coupled receptors. Examples of Tas1Rs include Tas1R1, Tas1R2, and Tas1R3. Examples of FFARs include FFAR1, FFAR2, and FFAR3. Other G protein-coupled receptors include GPRC6A, GPR92, GPR120, GPR113, GPR40, GPR43, and GPR41. Any of these G protein-coupled receptors may function, for example, as taste receptors. Tas1R1 and Tas1R3 may, for example, form a heterocomplex and function as an umami receptor. Tas1R2 and Tas1R3 may, for example, form a heterocomplex and function as a sweet receptor. Tas2R may, for example, function as a bitter receptor. GLUTs and SGLTs may both function as sweet receptors. CasR may, for example, function as a kokumi receptor. mGluR, GPRC6A, and GPR92 may all function as an umami receptor. CD36, GPR120, and GPR113 may all function as a fat receptor. FFAR1 and GPR40, FFAR2 and GPR43, and FFAR3 and GPR413 may all function as a fat receptor, for example.

[0036] Ionotropic receptors include epithelial sodium channels (ENaC), acid-sensing ion channels (ASICs), transient receptor potential channels (TRP channels), transmembrane channel-like proteins (TMC proteins), voltage-gated potassium ion channels, 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 taste receptors. Any of ENaC, ASICs, TRP channels, TMC proteins, and voltage-gated potassium ion channels may function, for example, as salty taste receptors. ASICs, PKD1L3, and PKD2L1 may all function, for example, as sour taste receptors.

[0037] The gene encoding the receptor is also called the receptor gene.

[0038] Receptor genes and receptors include receptor genes and receptors from various organisms. Examples of organisms include animals such as mammals. Specific examples of animals such as mammals include Homo sapiens (humans), Mus musculus (mice), Rattus norvegicus (rat), Canis lupus familiaris (dogs), Felis catus (cats), Bos taurus (cattle), Sus scrofa (pigs), Pan troglodytes (chimpanzees), Macaca fascicularis (cyn-eating monkeys), and Equus caballus (horses). Mammals and other animals particularly include humans. The nucleotide sequences of receptor genes and amino acid sequences of receptors from various organisms can be obtained from public databases such as NCBI and Ensembl.

[0039] The receptor may be, for example, a protein having the known or naturally occurring amino acid sequence of the receptor as described above. The receptor may also be, for example, a variant of a protein having the known or naturally occurring amino acid sequence of the receptor as described above. That is, the receptors identified by the above names include, for example, proteins having the known or naturally occurring amino acid sequence of the receptor identified by that name and variants thereof. The expression "a protein having an amino acid sequence" means that the protein contains the amino acid sequence, unless otherwise specified, and also encompasses cases 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 naturally occurring amino acid sequence have been substituted, deleted, inserted, and / or added. Specifically, "one or several" may 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. Variants also include proteins having an amino acid sequence that is, 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 identical to the entire known or naturally occurring amino acid sequence. Note that receptors identified by their originating biological species are not limited to the receptors found in the biological species themselves, but also include proteins having the amino acid sequence of receptors found in the biological species and variants thereof. Variants may or may not be found in the biological species. That is, for example, "human receptors" are not limited to receptors found in humans, but include proteins having the amino acid sequence of receptors found in humans and variants thereof. Receptors may be, for example, chimeric proteins of two or more receptors of different origins. That is, receptors identified by the above names also include, for example, chimeric proteins of two or more receptors identified by those names of different origins.

[0040] The "identity" between amino acid sequences refers to the identity between amino acid sequences calculated using blastp with default scoring parameters (Matrix: BLOSUM62; Gap Costs: Existence = 11, Extension = 1; Compositional Adjustments: Conditional compositional score matrix adjustment).

[0041] The number of receptors is 2 or more. The number of receptors may be, for example, 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, or 5 or less, or any compatible combination thereof. The number of receptors may particularly be 50 or more, 100 or more, 150 or more, 200 or more, 250 or more, or 300 or more. The number of receptors may be, for example, 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 receptors may be, 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 receptors may include, for example, some or all of the receptors exemplified above. The receptors may consist of, for example, some or all of the above-exemplified receptors. For example, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, or 95% or more of the total number of receptors may be selected from the above-exemplified receptors. For example, particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, or 95% or more of the total number of 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, or 95% or more of the total number of receptors may be selected from the above-exemplified olfactory receptor group A. For example, more particularly, 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, or 95% or more of the total number of receptors may be selected from the above-exemplified olfactory receptor group B.The receptor may also comprise, 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 receptors in group B. In the case of receptors that function by forming a heterocomplex, the entire heterocomplex is considered to be one type of receptor.

[0042] The method for obtaining a receptor activity profile of a substance is not particularly limited. The receptor activity profile of a substance can be obtained, for example, by a known method for identifying the presence or absence, or the degree of activation or inactivation, of receptors such as olfactory receptors or gustatory receptors by a substance. Specifically, the receptor activity profile of a substance can be obtained, for example, by contacting a receptor with a substance and measuring the presence or absence, or the degree, of receptor activation or inactivation 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 receptor activation or inactivation resulting from this can be performed with reference to, for example, a method for screening a substance that exhibits a target odor using an olfactory receptor response as an indicator (Patent Publication No. 2019-037197, etc.) or a method for screening a substance that exhibits a target taste using a gustatory receptor response as an indicator (Patent Publication No. 2018-014999, etc.). The receptor may be used by being supported on a cell, such as an animal cell. When a receptor is used by being supported on a cell, "receptor activation or inactivation" may refer to the activation or inactivation of the cell that supports the receptor. Cells bearing 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, cells bearing the receptor can be obtained, for example, by introducing genes encoding each of the subunits constituting the heterocomplex into a cell and co-expressing them.

[0043] Cells bearing receptors may contain components useful or necessary for obtaining a receptor activity profile, such as proteins involved in signal transduction, proteins promoting receptor membrane expression, and components depending on the parameters to be measured. Proteins involved in signal transduction include G proteins (e.g., Golf), G protein activators (e.g., Ric8B), adenylate cyclase, and calcium channels. Golf includes animal Golf such as human Golf (GenBank accession No. NP_892023). Ric8B includes animal Ric8B such as rat Ric8B (GenBank accession No. NP_783188). Proteins promoting receptor membrane expression include RTP1s (Zhuang H and Matsunami H, J Biol Chem 282, 15284-15293 (2007)). Examples of RTP1s include animal RTP1s such as human RTP1s (GenBank accession No. AAT70680), mouse RTP1s (GenBank accession No. ABU23737), and bat RTP1s (the amino acid sequence from the methionine residue at position 37 to the C-terminus of GenBank accession No. XP_006765914). Components appropriate for the parameters to be measured include probes such as calcium indicators and membrane potential-sensitive dyes, and reporter genes such as the luciferase gene.

[0044] Activation of G protein-coupled receptors, such as olfactory receptors, can be measured, for example, using an increase in intracellular cAMP concentration or intracellular calcium concentration as an indicator (see, for example, JP 2019-037197). For example, in HEK293T cells, when olfactory receptors are activated by odorants, they couple with intracellular G proteins (such as 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. Examples of reporter assays include luciferase assays. Reporter assays can measure intracellular cAMP concentration using a reporter gene (such as a luciferase gene) whose expression is configured to depend on the cAMP concentration. Methods for measuring intracellular calcium concentration include calcium imaging using calcium indicators.

[0045] Activation of ionotropic receptors such as TMC proteins can be measured using, for example, ion influx into cells, membrane potential, inward membrane current, or an increase in intracellular ion concentration as indicators (see, for example, Japanese Patent Application Publication No. 2018-014999). Examples of ions include sodium ions, calcium ions, and chloride ions. Examples of methods for measuring membrane potential include the patch clamp method and methods using membrane potential-sensitive dyes. Examples of methods for measuring membrane current include the patch clamp method and voltage clamp. Examples of methods for measuring intracellular sodium concentration include methods using sodium indicators such as CoroNa Green Sodium Indicator (Thermo Fisher Scientific). Examples of methods for measuring intracellular calcium concentration include calcium imaging using calcium indicators.

[0046] Specifically, the contact between a receptor and a substance and the presence or absence or degree of receptor activation or inactivation due to the contact can be measured, for example, by the following procedure.

[0047] 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 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 for contacting the receptor with the substance) as an indicator.

[0048] A system in which contact between a receptor and a substance is carried out is also referred to as a "reaction system." Contact between a receptor and a substance can be carried out in an appropriate liquid. That is, the reaction system may be liquid. A liquid in which contact between a receptor and a substance is carried out (i.e., a liquid reaction system) is also referred to as a "reaction solution." That is, for example, contact between a receptor and a substance can be achieved by coexisting the receptor and the substance in an appropriate reaction solution. Specifically, for example, contact between a receptor and a substance can be achieved by dissolving, suspending, dispersing, or the like the receptor (e.g., a form such as a receptor-bearing cell, as exemplified above) and the substance in an appropriate liquid medium. Examples of liquid media include aqueous media such as water and aqueous buffer solutions. Note that when two or more components are contacted with a receptor simultaneously, contact between the components and the receptor may or may not be initiated simultaneously. That is, for example, contact between a certain component and the receptor may be initiated, followed by the addition of another component to the reaction system. The reaction conditions (conditions under which contact between a receptor and a substance is carried out) are not particularly limited as long as they allow for measurement of the presence or degree of receptor activation or inactivation by the substance. The reaction conditions can be appropriately set depending on various conditions, such as the form of receptor use, the type of substance, and the method for measuring receptor activation or inactivation. For example, known reaction conditions for measuring interactions between substances, such as interactions between proteins and ligands, may be used as is, or modified as appropriate. The substance concentration 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 substance concentration 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 receptor concentration may be, for example, 1 pg / mL to 10 mg / mL. Furthermore, when receptor-bearing cells are used, the concentration of receptor-bearing cells may be, for example, 10 cell / mL to 10,000,000 cell / mL. Contact between the receptor and the substance may or may not be terminated at an appropriate time, but contact between the receptor and the substance is typically continued until receptor activation or inactivation by the substance is measured.The duration of contact between the receptor and the substance may be, for example, 0.1 second 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. Specifically, the duration of contact between the receptor and the substance may be, for example, 1 hour to 6 hours. The reaction system may contain other components in addition to the receptor (e.g., receptor-bearing cells or the like, as exemplified above) and the substance, as long as it is possible to measure the presence or absence or degree of receptor activation or inactivation by the substance. The other components can be appropriately selected depending on various conditions, such as the form of receptor use, the type of substance, and the method for measuring receptor activation or inactivation. Examples of other components include salts such as calcium salts, carbon sources such as glucose, other medium components, and pH buffers.

[0049] Furthermore, when measuring receptor inactivation by a substance, 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 a "receptor activator"). The expressions "contacting the receptor with the 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 interchangeably. The description of contact between the receptor and a substance also applies mutatis mutandis to contact between the receptor and a receptor activator. Contact between the receptor activator and the substance and the receptor may or may not be initiated simultaneously. For example, the substance and the receptor activator may be premixed and then contacted with the receptor. Furthermore, for example, the receptor activator may be added to the reaction system after contact between the substance and the receptor has begun, or the substance may be added to the reaction system after contact between the receptor activator and the receptor has begun. 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 a receptor and a substance is carried out in the presence of a receptor activator, the phrase "duration of contact between the receptor and the substance (here, the substance for which a receptor activity profile is obtained)" may be read as "duration of contact between the receptor and the receptor activator and the substance (here, the substance for which a receptor activity profile is obtained)."

[0050] 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 a combination of two or more components (i.e., a mixture). When the receptor activator is a mixture, the number of components constituting the mixture and their composition ratios are not particularly limited. The receptor activator may be a known substance or a novel substance. The receptor activator may be a natural product or an artificial product. The receptor activator may or may not be known to be able to activate the receptor. Receptor activators can be screened, for example, with reference to a screening method for a substance that exhibits a target odor using an olfactory receptor response as an indicator (e.g., JP 2019-037197) or a screening method for a substance that exhibits a target taste using a taste receptor response as an indicator (e.g., JP 2018-014999).

[0051] 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 degree of receptor activation or inactivation by the substance. The timing for measuring the degree of activation D1 can be set appropriately 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 any appropriate time from the time when contact between the receptor and the substance begins to the time when receptor activation or inactivation 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. Furthermore, 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, or 2 hours or later from the time when contact between the receptor and the substance begins, or up to 24 hours, 12 hours, 6 hours, 2 hours, or 1 hour, or any compatible combination thereof. Specifically, the timing for measuring the degree of activation D1 may be, for example, from 1 hour to 6 hours from the time when contact between the receptor and the substance begins. When contact between the receptor and the substance is carried out in the presence of a receptor activator, the phrase "the time when contact between the receptor and the substance (here, the substance for which a receptor activity profile is obtained) begins" may be interpreted as "the time when contact between the receptor and the receptor activator and the substance (here, the substance for which a receptor activity profile is obtained) begins."

[0052] The presence or absence or degree of receptor activation or inactivation by a substance can be determined specifically by comparing the degree of activation D1 with the degree of receptor activation under control conditions (also referred to as the "degree of activation D2"). Control conditions include conditions under which the receptor is not contacted with the substance. When contact between the receptor and the substance (here, the substance from which the receptor activity profile is obtained) is carried out in the presence of a receptor activator, control conditions include conditions under which the receptor is contacted with the receptor activator but not with the substance (i.e., conditions under which the receptor is contacted with the receptor activator in the absence of the substance).

[0053] The activation levels D1 and D2 can both be obtained and used as data reflecting parameters that serve as indicators of receptor activation or inactivation. Examples of parameters that serve as indicators of receptor activation or inactivation include the amount of intracellular calcium and the amount of intracellular cAMP. In the case of a luciferase assay, examples of data reflecting the amount of intracellular cAMP include luminescence intensity. Data reflecting parameters that serve as indicators of receptor activation or inactivation can be used directly or after appropriate processing. 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.

[0054] When the activation level D1 is high, it may be determined that the receptor has been activated by a substance. For example, when the ratio of the activation level D1 to the activation level D2 (i.e., D1 / D2) is within a predetermined range, it may be determined that the receptor has been activated by a substance. The predetermined range for determining that the receptor has been activated by a substance can be set appropriately depending on various conditions, such as the type of receptor and the type of data indicating the activation levels D1 and D2. The predetermined range for determining that the receptor has been activated by a 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. Examples of the ratio of the activation level D1 to the activation level D2 include the normalized response value calculated using the procedure described in JP 2019-037197 A.

[0055] When the activation level D1 is low, it may be determined that the receptor has been inactivated by a substance. For example, when the ratio of the activation level D1 to the activation level D2 (i.e., D1 / D2) is within a predetermined range, it may be determined that the receptor has been activated by a substance. The predetermined range for determining that the receptor has been inactivated by a substance can be set appropriately depending on various conditions, such as the type of receptor and the type of data indicating the activation levels D1 and D2. The predetermined range for determining that the receptor has been inactivated by a 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. Examples of the ratio of the activation level D1 to the activation level D2 include the normalized response value calculated using the procedure described in JP 2019-037197 A.

[0056] Furthermore, the degree of receptor activation or inactivation by a substance can be determined using the comparison result between the activation level D1 and the activation level D2 as an index. For example, the ratio of the activation level D1 to the activation level D2 (i.e., D1 / D2) can be considered to be the degree of receptor activation or inactivation by a substance. The ratio of the activation level D1 to the activation level D2 can be exemplified by the "normalized response value" calculated by the procedure described in JP 2019-037197 A. Furthermore, for example, the logarithm of the activation level D1 can be subtracted from the logarithm of the activation level D2, and the resulting value, standardized between receptors, can be considered to be the degree of receptor activation or inactivation by a substance. Examples of such standardized values ​​include the "Response value" described in the Examples.

[0057] The receptor activity profile may also be processed by nonlinear transformation or the like. For example, data indicating the degree of receptor activation or inactivation by a substance may be subjected to processing such as nonlinear transformation and then used as the receptor activity profile. The processing method can be appropriately set depending on various conditions, such as the type of data to be processed and the purpose of the processing. For example, data to be emphasized included in the target profile may be relatively expanded. Examples of data to be emphasized include data on receptors to be emphasized. Examples of receptors to be emphasized include receptors that respond strongly to objects exhibiting the target sensory quality and receptors that respond weakly to objects exhibiting the target sensory quality. By relatively expanding data on a certain receptor and subjecting it to the identification step, a target substance and its synthesis ratio that preferentially gives a composite profile similar to the target profile for the receptor activity profile portion of the certain receptor may be identified. That is, for example, by selecting a receptor that responds strongly to an object exhibiting the target sensory quality as a receptor to be emphasized, the influence of the receptor may be more strongly evaluated. Furthermore, for example, by selecting a receptor that responds weakly to an object exhibiting the target sensory quality as a receptor to be emphasized, the influence of that receptor may be evaluated without being overlooked.

[0058] The means for carrying out the identification step is not particularly limited as long as it allows the target substance and its synthesis ratio to be identified with the desired accuracy.

[0059] The identification step can be performed, for example, by an analytical method for finding a solution to an inverse problem (particularly, a solution to an underdetermined inverse problem) using the target profile as the objective variable and the receptor activity profile for the receptor of each substance constituting the test substance group as the explanatory variable. An example of an analytical method for finding a solution to an underdetermined inverse problem is sparse modeling. Since the prediction method of the present invention predicts a substance combination, sparse modeling may be performed so that the coefficients of the explanatory variables are positive (i.e., under non-negative constraint conditions). That is, the sparse modeling may be non-negative constraint sparse modeling.

[0060] In sparse modeling, regularization may be performed. Examples of regularization include L1 regularization, L2 regularization, and L0 regularization. L1 regularization is particularly an example of regularization. Regularization such as L1 regularization may reduce variables, for example. The strength of regularization can be set appropriately depending on various conditions, such as the number of test substances and the number of target substances. The strength of regularization can be set using the regularization parameter λ. A larger regularization parameter λ may increase the number of regression coefficients estimated to be zero (or nearly zero), i.e., reduce the number of target substances selected. A larger regularization parameter λ may reduce the risk of overfitting, but may increase the risk of underfitting. Furthermore, multiple modeling runs may be performed with different regularization strengths (specifically, the regularization parameter λ). The strength of regularization may be set commonly or independently for each explanatory variable (i.e., for each test substance). For example, by using the regularization parameter λ, the overall strength of regularization can be set (i.e., the strength of regularization can be set commonly for all explanatory variables). Furthermore, for example, by setting a penalty factor for each explanatory variable in addition to the regularization parameter λ, the strength of regularization can be set independently for each explanatory variable. By independently setting the strength of regularization for each explanatory variable (i.e., for each test substance), the likelihood of each test substance being selected as a target substance can be adjusted. For example, some or all of the test substances may be pre-selected as target substances. Specifically, for example, by setting the penalty factor for a test substance to zero, the test substance may be pre-selected as a target substance. Furthermore, for example, some of the test substances may be pre-excluded from the target substances. Specifically, for example, by setting the penalty factor for a test substance to infinity, the test substance may be pre-excluded from the target substances.

[0061] Examples of regularization or sparse modeling algorithms that include it include the lasso regression model, the ridge regression model, and the elastic net regression model. In the lasso regression model, L1 regularization may be performed. In the ridge regression model, L2 regularization may be performed. In the elastic net regression model, a combination of L1 and L2 regularization may be performed. In particular, examples of regularization or sparse modeling algorithms that include it include the lasso regression model.

[0062] By performing an analytical technique such as sparse modeling in this manner, candidates for target substances and their composite ratios can be obtained. Among these candidates, a candidate that provides a composite profile that approximates the target profile may be adopted as a solution. For example, among these candidates, a candidate that provides a composite profile that approximates the target profile and has a predetermined number of target substances within a predetermined range may be adopted as a solution. One solution, or two or more solutions, may be adopted as a solution.

[0063] A solution that provides a composite profile that approximates the target profile may be selected based on, for example, the error between the composite profile and the target profile. The error between the composite profile and the target profile can be calculated, for example, using any evaluation function that gives the difference between the two as a non-negative numerical value. Examples of the evaluation function include mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean squared logarithmic error (MSLE), root mean squared logarithmic error (RMSLE), mean absolute percentage error (MAPE), and root mean squared percentage error (RMSPE). Examples of the evaluation function include squared errors such as MSE, RMSE, MSLE, RMSLE, and RMSPE. Examples of the evaluation function include RMSE, in particular. Any of the evaluation functions exemplified above may be used, for example, to calculate a weighted error. The degree of error required to determine whether a composite profile approximates a target profile can be appropriately set depending on various conditions, such as the type of evaluation function and the number of target substances. "The composite profile approximates the target profile" may mean, for example, that the error calculated using the evaluation function exemplified above is equal to or less than a predetermined value. Specifically, "The composite profile approximates the target profile" may mean, for example, that the error between the composite profile and the target profile, converted to a weighted RMSE, is 0.75 or less, 0.74 or less, 0.73 or less, 0.72 or less, 0.71 or less, 0.7 or less, 0.69 or less, 0.68 or less, 0.67 or less, 0.66 or less, 0.65 or less, 0.64 or less, 0.63 or less, 0.62 or less, 0.61 or less, or 0.6 or less. The error between the composite profile and the target profile may be, for example, less than or equal to 0.7, particularly in terms of a weighted RMSE.

[0064] In this way, the target substance and its composition ratio can be identified, and the target composition can be predicted. The composition of the target substance at the composition ratio identified in the identification step can be considered as the target composition, i.e., can be predicted as the target composition. In other words, the target substance and its composition ratio identified in the identification step can be considered as the substance and its composition ratio to be combined in the target composition, i.e., can be predicted as the substance and its composition ratio to be combined in the target composition.

[0065] The method of the present invention may further include a step of evaluating the sensory quality of the predicted target composition. That is, by evaluating the sensory quality of the predicted target composition, it is possible to confirm whether the predicted target composition actually exhibits the target sensory quality. The method for evaluating the sensory quality of the predicted target composition is not particularly limited. The sensory quality of the predicted target composition can be evaluated, for example, by known methods for evaluating the sensory quality of substances, such as their aroma and taste. Such methods include sensory evaluation (evaluation by sensory testing). Specifically, for example, target substances can be blended based on the predicted target composition, and the sensory quality of the resulting composition can be evaluated. The description of the blending of target substances in the reconstitution method of the present invention described below can be applied mutatis mutandis to the blending of target substances for evaluating sensory quality.

[0066] <1-3> Reconstitution method of the present invention A target sensory quality can be reconstituted based on a predicted target blend. That is, the reconstitution method of the present invention may be a method for reconstituting a target sensory quality, and may include a step of predicting a blend that exhibits the target sensory quality using the prediction method of the present invention, and a step of blending the target substance based on the blend.

[0067] The blending ratio of the target substances is not particularly limited as long as it can reconstitute the target sensory quality. The target substances may be blended, for example, at a blending ratio substantially the same as the blending ratio identified in the identification step. "Substantially the same blending ratio as the blending ratio identified in the identification step" means, for each target substance, 0.8 times or more, 0.85 times or more, 0.88 times or more, 0.9 times or more, 0.91 times or more, 0.92 times or more, 0.93 times or more, 0.94 times or more, 0.95 times or more, 0.96 times or more, 0.97 times or more, 0.98 times or more, 0.99 times or more, 1 times or more, 1.01 times or more, 1.02 times or more, 1.03 times or more, 1.04 times or more, 1.05 times or more, 1.06 times or more, 1.07 times or more, 1.08 times or more, 1.09 times or more, 1.1 times or more, or 1.12 times or more of the blending ratio identified in the identification step. , or 1.15 times or more, or 1.2 times or less, 1.15 times or less, 1.12 times or less, 1.1 times or less, 1.09 times or less, 1.08 times or less, 1.07 times or less, 1.06 times or less, 1.05 times or less, 1.04 times or less, 1.03 times or less, 1.02 times or less, 1.01 times or less, 1 times or less, 0.99 times or less, 0.98 times or less, 0.97 times or less, 0.96 times or less, 0.95 times or less, 0.94 times or less, 0.93 times or less, 0.92 times or less, 0.91 times or less, 0.9 times or less, 0.88 times or less, or 0.85 times or less, or any combination thereof that does not contradict. "A blending ratio that is substantially the same as the synthesis ratio identified in the identification step" specifically means, for example, for each target substance, 0.8 to 0.85 times, 0.85 to 0.88 times, 0.88 to 0.9 times, 0.9 to 0.91 times, 0.91 to 0.92 times, 0.92 to 0.93 times, 0.93 to 0.94 times, 0.94 to 0.95 times, 0.95 to 0.96 times, 0.96 to 0.97 times, 0. It may be 97 to 0.98 times, 0.98 to 0.99 times, 0.99 to 1 times, 1 to 1.01 times, 1.01 to 1.02 times, 1.02 to 1.03 times, 1.03 to 1.04 times, 1.04 to 1.05 times, 1.05 to 1.06 times, 1.06 to 1.07 times, 1.07 to 1.08 times, 1.08 to 1.09 times, 1.09 to 1.1 times, 1.1 to 1.12 times, 1.12 to 1.15 times, or 1.15 to 1.2 times.Specifically, the "blending ratio substantially the same as the synthesis ratio identified in the identification step" may be, for example, for each target substance, 0.85 to 1.15 times, 0.9 to 1.1 times, 0.95 to 1.05 times, or 0.98 to 1.02 times the synthesis ratio identified in the identification step.

[0068] <1-4> Method for Reconstructing a Target Set of Objects In order to reconstruct the sensory qualities of various targets (target sets) using a single device, it is generally necessary to load many target substances onto the device. Therefore, a set of synthesis elements may be optimized so that the sensory qualities of multiple targets can be reconstructed using a limited combination of synthesis elements. Note that a synthesis element is one or more test substances (target substances) selected from a group of test substances. In other words, a synthesis element may contain two or more test substances blended at a predetermined ratio. Furthermore, a set of a predetermined number of synthesis elements for reconstructing the sensory qualities of multiple targets is referred to as a minimum set.

[0069] FIG. 1 is a diagram illustrating the reconstitution of a target set. In FIG. 1, an aroma, an example of sensory quality, is reconstituted. Note that aromas do not have a set of basic elements that can be combined to reconstitute any aroma, as in the case of the three primary colors. Therefore, it is desirable to optimize a minimum set according to the target set. The system shown in FIG. 1 includes a prediction device 1 that executes the prediction method of the present invention, an optimization device 2 that creates a minimum set of composite elements, and a reconstitution device 3 that is equipped with multiple cassettes (cartridges) containing composite elements and mixes the composite elements to output a reconstituted aroma.

[0070] The prediction device 1 is a computer that performs the identification step described in <1-2>. The prediction device 1 includes a receptor activity database (DB) 11 and an identification unit 12. The receptor activity DB 11 is a database stored in a storage device provided in the computer, and stores receptor activity profiles associated with each of the test substances that make up the test substance group. The processor provided in the computer executes a program according to the embodiment, thereby functioning as the identification unit 12 that performs the identification step. The identification unit 12 identifies a composite profile using the receptor activity profile of a desired target as a response variable and other test substances as explanatory variables.

[0071] The reconstitution device 3 is equipped with a predetermined number of cassettes 31 and is a device for reconstituting the sensory qualities of multiple targets, and includes a so-called computer. The cassette 31 is a container for storing one synthesis element, and for example, multiple cassettes 31 are detachably mounted on the reconstitution device 3. The number of cassettes 31 (synthesis elements) is not particularly limited, but for example, approximately 2 to 24 cassettes 31 can be used. The minimum set blending ratio 32 is information stored in the computer's storage device. The minimum set blending ratio 32 corresponds to each of the multiple targets and includes a blending ratio of synthesis elements that gives a synthesis profile that approximates the target. The mixer 33 includes a computer processor and extracts synthesis elements from the cassettes 31 in the ratio indicated by the minimum set blending ratio 32, mixes them, and outputs them.

[0072] The optimization device 2 is a so-called computer, and determines the synthesis elements to be contained in the cassette 31 and their respective recipes according to multiple targets. Note that the optimization device 2 may be an apparatus integrated with at least one of the prediction device 1 and the reconstruction device 3.

[0073] In (1) of FIG. 1, the optimization device 2 identifies a reconstitution recipe W for reconstituting a receptor activity profile Y of a target aroma. The receptor activity profile Y of the target aroma can be expressed as an m-row, d-column matrix indicating a d-dimensional receptor activity profile associated with each of the m target aromas. Furthermore, the reconstitution recipe W can be expressed as an m-row, k-column matrix indicating k candidate components associated with each of the m target aromas. The k candidate components are, for example, a union of target substances identified in the identification step. Furthermore, the receptor activity profile X of the candidate components can be expressed as a k-row, d-column matrix indicating a d-dimensional receptor activity profile associated with each of the k candidate components. The reconstitution recipe W can be obtained by the prediction device 1 performing an identification step for each target aroma. In the identification step (1), it is preferable to set a relatively large number of target substances (with a margin) and execute the step. Although the identification step results in a composite profile that approximates the target profile, it will contain errors for each target aroma.

[0074] In (2) of FIG. 1 , the optimization device 2 calculates a minimum set for reconstituting a target aroma set using the reconstitution recipe W. The minimum set blending ratio A (basis matrix A) can be expressed as an m-row, n-column matrix indicating n composite elements associated with each of m target elements. Furthermore, the minimum set recipe H (coefficient matrix H) can be expressed as an n-row, k-column matrix indicating the blending ratio of k candidate components for each of the n composite elements. The reconstitution recipe W can be decomposed into the minimum set recipe H and the minimum set blending ratio A using nonnegative matrix factorization (NMF). Based on the minimum set recipe H determined in this way, the composite elements are placed in each cassette 31. Furthermore, the minimum set blending ratio A is stored as the minimum set blending ratio 32 in the reconstitution device 3. In addition, by carrying out the identification process with a margin in (1), it is possible to increase the number of candidate components that can be used when creating the minimum set recipe H, and to reduce the error in the receptor activity profile between the reconstituted aroma that is ultimately output by the reconstitution device 3 and the target aroma to be reproduced.

[0075] In this way, the cassette 31 (synthesis element) for reconstituting the desired target set can be optimized. Note that the processes (1) and (2) in FIG. 1 may be performed in reverse order. Furthermore, it is desirable that the relationship between the number of candidate components k, the number of targets m, and the number of synthesis elements n be "k > m > n." That is, it is useful for the reconstitution device 3 to be able to reproduce a greater number of targets m using a relatively small number of synthesis elements n (cassettes 31). The value of n can be arbitrarily set by the user when performing NMF. The value of n is appropriately determined based on, for example, the size of the reconstitution device 3 and the error in the receptor activity profile between the reconstituted aroma and the target aroma to be reproduced. Note that the number of candidate components k is generally greater than the number of targets m.

[0076] <2> Program of the Present Invention The present invention provides a program that causes a computer to execute the steps included in the method of the present invention. This program is also referred to as the "program of the present invention."

[0077] That is, the computer may execute the steps included in the method of the present invention. The computer may execute some or all of the steps included in the method of the present invention. The computer may execute, for example, a step of acquiring a target profile, a step of acquiring a receptor activity profile of a test substance, and / or an identification step. The computer may, for example, particularly execute the identification step. Specifically, for example, by inputting a target profile into a computer, the program of the present invention may cause the computer to execute the identification step, and the identification result of the target substance and its synthesis ratio (i.e., the predicted result of the target combination) may be output. The receptor activity profile of the test substance used in the identification step may, for example, be stored in the computer in advance, or may be input into the computer when the identification step is performed.

[0078] The program of the present invention may be recorded on a computer-readable recording medium and provided. A "computer-readable recording medium" refers to a recording medium on which information such as data and programs is stored electrically, magnetically, optically, mechanically, or chemically, and from which the stored information can be read by a computer. Examples of such recording media include floppy disks, magneto-optical disks, CD-ROMs, CD-R / Ws, DVD-ROMs, DVD-R / Ws, DVD-RAMs, DATs, 8mm tapes, memory cards, hard disks, ROMs (read-only memories), and SSDs. Furthermore, the program of the present invention may be recorded such that each step executed by a computer is recorded as a separate program.

[0079] The present invention will now be described in more detail with reference to the following non-limiting examples.

[0080] <1> Preparation of cells expressing human olfactory receptors <1-1> Preparation of expression vectors for human olfactory receptors 352 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, OR2 AG2, OR2AJ1P, OR2AK2, OR2AP1, OR2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, O R2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2L13, OR2M2, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR 2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR 4C13, OR4C15, OR4C16, OR4C46, OR4D1, OR4D2, OR4D5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4 H12P, OR4K1, OR4K2, OR4K5, OR4K13, OR4K14, OR4K15, OR4K17, OR4L1, OR4M1, OR4N2, OR4N4, OR4N5, OR4P4, OR4Q3, OR4S1, OR4S2, OR4X1, OR4X2, OR 5A1, 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, OR51H 1, OR51I1, OR51I2, OR51L1, OR51M1, OR51Q1, OR51S1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52 B6, 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, OR56B4).

[0081] We purchased 352 human olfactory receptor genes from the TrueClone cDNA Clone Collection (OriGene). Using primers designed based on the sequences registered in GenBank, we amplified subcloning fragments for each of the 352 human olfactory receptor genes by PCR using the purchased human olfactory receptor genes as templates. The amplified subcloning fragments for each gene were subcloned downstream of the Rho tag sequence in the Rho-pME18S vector (K. Kajiya et al., Journal of Neuroscience, 15 August 2001, 21 (16) 6018-6025) using the EcoRI and XhoI sites, yielding 352 expression vectors for human olfactory receptors.

[0082] <1-2> Preparation of Olfactory Receptor-Expressing Cells HEK293T cells expressing each of the 352 olfactory receptors were prepared using the following procedure. The gene mixture shown in Table 1 and the transfection reagent mixture shown in Table 2 were prepared and left to stand at room temperature for 5 minutes. pcDNA3.1-microbat RTP1s is an expression vector for bat RTP1s, pcDNA3.1-human Golf is an expression vector for human Golf, and pcDNA3.1-rat Ric8B is an expression vector for rat Ric8B (JP Patent Publication No. 2019-037197). The gene mixture and transfection reagent mixture were mixed and dispensed in 12.5 μL aliquots into each well of a poly-D-lysine-coated 384-well plate. The mixture was left to stand for 15 minutes in a clean bench. HEK293T cells (2.5 × 10 cells) were seeded in 10 cm dishes the day before. 6 1.2 x 10 cells / 10 cm dish 5 The solution was adjusted to 1000 cells / mL, and 25 μL of each was seeded into each well of a 384-well plate and cultured overnight in an incubator maintained at 37°C and 5% CO. In this way, 352 cultures of HEK293T cells transfected with the expression vectors shown in Table 1 and expressing the genes encoded by those expression vectors were obtained.

[0083]

[0084]

[0085] <2> Creation of a Human Olfactory Receptor Activity Database <2-1> Luciferase Assay Olfactory receptor-expressing cells were used to measure the response of olfactory receptors to test substances.

[0086] The 352 olfactory receptors expressed in HEK293T cells conjugate with Golf to activate adenylate cyclase, thereby increasing intracellular cAMP levels. In this example, a luciferase reporter gene assay was used to measure the response of olfactory receptors to test substances. This assay monitors the increase in intracellular cAMP levels as an increase in luminescence intensity derived from firefly luciferase. The "luciferase reporter gene assay" is also referred to as the "luciferase assay." Firefly luciferase is expressed from the firefly luciferase gene carried by the pGL4.29[luc2P / CRE / Hygro] Vector in a manner dependent on the amount of intracellular cAMP. Additionally, the luminescence intensity 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 from the Renilla luciferase gene carried in the pGL4.74[hRluc / TK] Vector under the control of the TK promoter.

[0087] A total of 3103 test substances were selected, including those listed in The Good Scents Company (http: / / www.thegoodscentscompany.com / ). The medium was removed from the 352 cultures obtained in <1-2> above, and 15 μL of the 3103 test substance solutions was added to each, yielding 352 × 3103 reaction solutions. Each test substance solution was prepared by dissolving each test substance in CD293 (Life Technologies, Inc.). The test substance concentration in the test substance solution was generally 300 μM. However, for test substances that showed cytotoxicity at 300 μM, the test substance concentration in the test substance solution was set to 3 μM, 10 μM, 30 μM, or 100 μM. For a small number of test substances, the test substance concentration in the test substance solution was set to 1000 μM. When the test substance was a mixture, the concentrations were set to 0.1%, 0.3%, 0.5%, 1%, 3%, 5%, 10%, 50%, or 100%. The reaction mixture was placed in an incubator maintained at 37°C and 5% CO2, and the cells were cultured for 4 hours to allow sufficient intracellular expression of the firefly luciferase gene. The luminescence value derived from intracellular firefly luciferase was measured and recorded as the "Luc value." The luminescence value derived from intracellular Renilla luciferase was also measured and recorded 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 instructions.

[0088] <2-2> Correction and normalization of reporter signals. Signals obtained in luciferase assays can contain bias due to various factors, such as experimental conditions. Therefore, subjecting these signals directly to comprehensive analysis or statistical modeling may simply result in the detection of bias and potentially prevent the desired results. Therefore, we performed reporter signal correction and normalization using the following methods to obtain a profile of 352 olfactory receptor activities for each test substance. Specifically, we performed logarithmic transformation to eliminate distortion in the reporter signal distribution, corrected for gene transfer efficiency using an internal standard, corrected for inter-experimental differences in reporter signals using unstimulated conditions, corrected for the basal activity specific to each olfactory receptor, corrected for olfactory receptor-independent activity, and normalized activity values ​​using the mean and standard deviation of all data.

[0089] <3> Reconstitution of target odors using olfactory receptor activity profiles as indicators <3-1> Odor reconstitution targeting a single odor component Two substances shown in Table 3 were selected from a group of 3,103 test substances as target odors (i.e., odors to be reconstituted). The target olfactory receptor activity profile in the olfactory receptor activity database was set as the target profile.

[0090]

[0091] For each target, 3102 substances, excluding the target from the olfactory receptor activity database, were selected as a test substance group, and the olfactory receptor activity profile of these substances was set as the test substance group profile.

[0092] Using the target profile as the objective variable (352 points × 1 variable) and the test substance group profiles as explanatory variables (352 points × 3102 variables), we performed non-negative constrained linear sparse modeling to obtain a composite profile approximating the target profile and the coefficients of each variable (the composite ratio of the receptor activity profiles of two or more target substances that give the composite profile). Specifically, we used a LASSO (least absolute shrinkage and selection operator) regression model. The model intercept was set to 0. The parameter λ, which determines the strength of regularization, was optimized so that the number of variables with coefficients greater than 0 (the number of two or more target substances that give the composite profile) was four or less. The target substance mixture ratio was determined according to the ratio of the model coefficients.

[0093] The target substances were mixed according to the calculated mixing ratio to prepare the reconstituted products. For the sensory evaluation, the target and reconstituted products were each diluted to a concentration at which the aroma could be easily smelled. Table 4 shows the composition of the evaluation samples used in the sensory evaluation.

[0094]

[0095] The sensory evaluation was conducted blindly (n = 4). The evaluators were presented with the target aroma as a reference sample. Then, the target aroma, the reconstituted aroma, or their components were presented once each as evaluation samples in random order. The evaluators rated the similarity of the aroma quality of the evaluation sample to that of the reference sample on a 5-point scale (1: not at all similar, 2: somewhat similar, 3: neither similar nor similar, 4: somewhat similar, 5: very similar). For each evaluation sample, the median of all evaluators' ratings was calculated and used as the "sensory similarity" between the target aroma and each evaluation sample. The sensory similarity between the target aroma and each evaluation sample is shown in Table 5.

[0096]

[0097] By using the olfactory receptor activity profile as an index to reconstruct the target odor, we achieved a reconstruction accuracy of approximately 70-90% for targets presented blindly.

[0098] <3-2> Odor reconstruction targeting a single odor component Four substances shown in Table 6 were selected as target odors. The target olfactory receptor activity profile in the olfactory receptor activity database was set as the target profile.

[0099]

[0100] For each target, the target was removed from the olfactory receptor activity database, leaving 196 test substance groups, and the olfactory receptor activity profiles of these were set as test substance group profiles.

[0101] Using the target profile as the objective variable (352 points × 1 variable) and the test substance group profiles as explanatory variables (352 points × 196 variables), we performed non-negative constrained linear sparse modeling to obtain a composite profile approximating the target profile and the coefficients of each variable (the composite ratio of the receptor activity profiles of two or more target substances that give the composite profile). Specifically, we used a LASSO (least absolute shrinkage and selection operator) regression model. The model intercept was set to 0. The parameter λ, which determines the strength of regularization, was optimized so that the number of variables with coefficients greater than 0 (the number of two or more target substances that give the composite profile) was four or less. The target substance mixture ratio was determined according to the ratio of the model coefficients.

[0102] The target substances were mixed according to the calculated mixing ratio to prepare the reconstituted products. For the sensory evaluation, the target and reconstituted products were each diluted to a concentration at which the aroma could be easily smelled. Table 7 shows the composition of the evaluation samples used in the sensory evaluation.

[0103]

[0104] The sensory evaluation was conducted blindly (n = 4 × 3 times). The evaluators were presented with the target aroma as a reference sample. Then, the target aroma or the reconstituted aroma was presented blindly as an evaluation sample. The evaluators rated the similarity of the aroma quality of the evaluation sample to that of the reference sample on a 5-point scale (0: not similar at all, 1: slightly similar, 2: moderately similar, 3: very similar, 4: extremely similar). The subjects repeated each test three times on separate days. For each evaluation sample, the median score across all evaluations by all evaluators was calculated and used as the "sensory similarity" to the target aroma. The sensory similarity between the target aroma and each evaluation sample is shown in Table 8.

[0105]

[0106] Even when the test substance group was set to 196 types, as in the case of the test substance group of 3,103 types, the reconstruction accuracy of approximately 70 to 100% of the target presented blindly was achieved by reconstructing the target odor using the olfactory receptor activity profile as an index.

[0107] <3-3> Odor reconstruction targeting composite aromas Three aroma oils shown in Table 9 were selected as target aromas. The target olfactory receptor activity profile in the olfactory receptor activity database was set as the target profile.

[0108]

[0109] For each target, 332 substances, excluding the target from the olfactory receptor activity database, were selected as a test substance group, and the olfactory receptor activity profile of these substances was set as the test substance group profile.

[0110] Using the target profile as the objective variable (352 points × 1 variable) and the test substance group profiles as explanatory variables (352 points × 332 variables), non-negative constrained linear sparse modeling was performed to obtain a composite profile approximating the target profile and the coefficients of each variable (the composite ratio of the receptor activity profiles of two or more target substances that give the composite profile). Specifically, a LASSO (least absolute shrinkage and selection operator) regression model was used. The model intercept was set to 0. The parameter λ, which determines the strength of regularization, was optimized so that the number of variables with coefficients greater than 0 (the number of two or more target substances that give the composite profile) was four or less. The target substance mixture ratio was determined according to the ratio of the model coefficients.

[0111] The target substances were mixed according to the calculated mixing ratio to prepare the reconstituted products. For the sensory evaluation, the target and reconstituted products were each diluted to a concentration at which the aroma could be easily smelled. Table 10 shows the composition of the evaluation samples used in the sensory evaluation.

[0112]

[0113] The sensory evaluation was conducted with n = 4 participants. The evaluators were presented with the target aroma as a reference sample. Then, the reconstituted product aroma was presented as an evaluation sample. The evaluators rated the similarity of the aroma quality of the evaluation sample to that of the reference sample on a 5-point scale (0: not similar at all, 1: slightly similar, 2: moderately similar, 3: very similar, 4: extremely similar). For each evaluation sample, the median of the ratings from all evaluators was calculated and used as the "sensory similarity" to the target aroma. The sensory similarity between the target aroma and each evaluation sample is shown in Table 11.

[0114]

[0115] Even when targeting a mixture of odors, odor reconstruction using olfactory receptor activity profiles resulted in moderate to very similar reconstruction accuracy.

[0116] <4> Reconstitution of target odor using reporter signal as index. Instead of the corrected olfactory receptor activity profile, the reporter signal corrected with the internal standard described in the manufacturer's protocol was used as an index to reconstitute the target odor and perform a sensory evaluation in the same manner as in <3-1>.

[0117] The two substances shown in Table 3 were selected as target odors. The target reporter signals in the olfactory receptor activity database were set as the target profile.

[0118] For each target, 3102 substances, excluding the target from the olfactory receptor activity database, were selected as test substance groups, and their reporter signals were set as test substance group profiles.

[0119] Using the target profile as the objective variable (352 points × 1 variable) and the test substance group profiles as explanatory variables (352 points × 3102 variables), we performed non-negative constrained linear sparse modeling to obtain a composite profile approximating the target profile and the coefficients of each variable (the composite ratio of reporter signals from two or more target substances that give the composite profile). Specifically, we used a LASSO (least absolute shrinkage and selection operator) regression model. The model intercept was set to 0. The parameter λ, which determines the strength of regularization, was optimized so that the number of variables with coefficients greater than 0 (the number of two or more target substances that give the composite profile) was four or less. The target substance mixture ratio was determined according to the ratio of the model coefficients.

[0120] The target substances were mixed according to the calculated mixing ratio to prepare Reconstituted Product 2. As a control, Reconstituted Product 1 was prepared, calculated using the olfactory receptor activity profile as an index using the method described in <3-1>. For sensory evaluation, the target, Reconstituted Product 1, and Reconstituted Product 2 were each diluted to a concentration at which the aroma could be easily smelled. Table 12 shows the composition of the evaluation samples used in the sensory evaluation.

[0121]

[0122] The sensory evaluation was conducted blindly (n = 8). The evaluators were presented with the target aroma as a reference sample. Then, the target aroma, the aroma of reconstituted product 1, and the aroma of reconstituted product 2 were presented blindly in random order as evaluation samples. The evaluators rated the similarity of the aroma quality of the evaluation sample to that of the reference sample on a 5-point scale (1: not similar at all; 2: somewhat similar; 3: neither similar nor similar; 4: somewhat similar; 5: very similar). For each evaluation sample, the median of the ratings from all evaluators was calculated and used as the "sensory similarity" between the target aroma and each evaluation sample. The sensory similarity between the target aroma and each evaluation sample is shown in Table 13.

[0123]

[0124] It was shown that the formulations calculated using the olfactory receptor activity profile as an index had a higher degree of sensory similarity than the formulations calculated using the raw measured values ​​of the reporter signal as an index.

[0125] <5> Calculation of a minimum set that reproduces a variety of fragrances. With the aim of reproducing a variety of fragrances using a limited number of fragrance cartridges, a minimum set was calculated using the present invention. Specifically, the composition of each cartridge and its mixing ratio were calculated to reproduce the target fragrance set of 72 items shown in Table 14 using a mixture of 12 fragrance cartridges.

[0126]

[0127] <5-1> Reconstitution of the target aroma set Using the same method as described in <3-3> above, we calculated the blending ratios for reconstituting each target profile of the target aroma set using 40 or fewer aroma components. As a result, we obtained a matrix (72 rows x 315 columns) representing the blending ratios of aroma components required to reconstitute the 72 items of the target aroma set. Hereafter, this matrix will be referred to as the reconstitution recipe matrix W. The average correlation coefficient between the target profile and the composite profile of the reconstituted product for the 72 items of the target aroma set was r = 0.647 (standard deviation 0.091, minimum 0.421, maximum 0.807).

[0128] <5-2> Approximate decomposition of the reconstitution recipe matrix Non-negative matrix factorization (NMF) was performed on the reconstitution recipe matrix W with a dimension of 12, and the matrix was decomposed into the format W ≒ AH. As a result, a basis matrix A (72 rows x 12 columns) and a coefficient matrix H (12 rows x 315 columns) were obtained. Here, the coefficient matrix H represents the blending ratio of aroma components for each cartridge in the minimum set, and the basis matrix A represents the blending ratio of each cartridge for reconstituting the target aroma set. The correlation coefficient between the reconstitution recipe matrix W and its approximate matrix AH was r = 0.901, demonstrating good approximation accuracy.

[0129] Basis matrix A is shown in Figure 2. Figure 2 shows the 72 target aroma items arranged vertically and the 12 aroma cartridges arranged horizontally, and the blending amounts of the aroma cartridges used to reconstitute each target aroma are displayed as a heat map. The darker the color, the greater the blending amount. The rows and columns are arranged according to the results of the hierarchical clustering analysis.

[0130] The coefficient matrix H is shown in Figure 3. The 12 fragrance cartridges are arranged vertically and the 315 fragrance components are arranged horizontally, and the blended amounts of fragrance components used to reconstitute each fragrance cartridge are displayed as a heat map. The darker the color, the greater the blended amount.

[0131] Figure 4 shows the reconstitution recipe matrix W. Figure 5 shows the approximation matrix AH. In Figures 4 and 5, the 72 target aroma items are arranged vertically and the 315 aroma components are arranged horizontally, and the blending amounts of the aroma components used to reconstitute each target aroma are shown in a heat map. The darker the color, the greater the blending amount.

[0132] Table 15 shows the number of fragrance components that make up each of the 12 fragrance cartridges.

[0133]

[0134] <6> Optimization of the number of fragrance cartridges Figure 6 shows the relationship between the number of fragrance cartridges and the approximation accuracy (residual sum of squares, RSS) using the approximation matrix AH when the number of fragrance cartridges in the minimum set is set to 2, 4, 6, 8, 10, 12, 14, 16, 20, and 24.

[0135] Figure 6 is a graph showing the relationship between the number of fragrance cartridges in the minimum set and the approximation accuracy of the approximation matrix AH. In Figure 6, the horizontal axis plots the number of fragrance cartridges (the number of dimensions of the NMF) and the vertical axis plots the residual sum of squares RSS, which is an index of the approximation accuracy of the reconstructed recipe matrix W and the approximation matrix AH. The open dots represent inflection points in the decrease of RSS.

[0136] As shown in Figure 6, increasing the number of fragrance cartridges decreased the RSS and improved the approximation accuracy. The inflection point where the slope of the decrease in RSS with increasing number of fragrance cartridges became gentler was when there were 12 fragrance cartridges. This means that the improvement in approximation accuracy obtained by increasing the number of fragrance cartridges beyond 12 is minimal. In other words, considering the trade-off between the cost of adding additional output ports to the odor presentation device and additional fragrance cartridges, it can be said that 12 fragrance cartridges is optimal for reconstructing the target fragrance set shown in this example.

[0137] <7> Calculating a Minimum Set Reproducing a Variety of Aromas (Alternative Method) In <5> above, the mixing ratios of aroma components (reconstruction recipe matrix) for reconstructing the 72 items of the target aroma set were first calculated <5-1>, and then the configuration of fragrance cartridges for reconstructing the target aroma set was obtained using nonnegative matrix factorization NMF <5-2>. Theoretically, this procedure can be reversed. That is, the target profile matrix of the 72 items of the target aroma set is first subjected to nonnegative matrix factorization NMF to obtain the profile matrix of the fragrance cartridge (fragrance profile set). Then, each profile in the fragrance profile set is used as a target profile, and the mixing ratios of aroma components for reconstructing each are calculated.

[0138] <7-1> Approximate Decomposition of the Target Profile Matrix of the Target Odor Set Because the activity values ​​obtained in <2-2> above contain negative values ​​and cannot be subjected to nonnegative matrix factorization NMF, the activity values ​​were made nonnegative using an exponential function. Subsequent analyses were performed using this nonnegative activity profile. Note that nonnegative value conversion is not necessary if the activity values ​​are nonnegative, depending on the measurement method and its standardization. Furthermore, conversion to an exponential function is not required as long as they can be converted to nonnegative values. Nonnegative matrix factorization NMF was performed on the target profile matrix W (72 rows x 355 columns) of the target odor set with a dimension of 12, and the matrix was decomposed into the form W ≒ AH. As a result, a basis matrix A (72 rows x 12 columns) and a coefficient matrix H (12 rows x 355 columns) were obtained.

[0139] Figure 7 shows the basis matrix A. In Figure 7, the 72 target aroma items are arranged vertically and the 12 aroma cartridges are arranged horizontally, and the blending amounts of the aroma cartridges used to reconstitute each target aroma are shown as a heat map. The darker the color, the greater the blending amount. The rows and columns are arranged according to the results of the hierarchical clustering analysis.

[0140] The coefficient matrix H is shown in Figure 8. In Figure 8, 12 fragrance cartridges are arranged vertically and 355 types of olfactory receptors are arranged horizontally, and the receptor (OR) activity profile of each fragrance cartridge is displayed as a heat map. Darker colors indicate stronger activity.

[0141] Figure 9 shows the target profile matrix W, and Figure 10 shows the approximate matrix AH. In Figures 9 and 10, the 72 target odor items are arranged vertically and the 355 olfactory receptors are arranged horizontally, and the target profile and approximate profile for each target odor are displayed as a heat map. Darker colors indicate stronger activity.

[0142] In this case, the coefficient matrix H represents the fragrance profile set, and the basis matrix A represents the mixing ratio of the fragrance cartridges for reconstructing the target fragrance set. The correlation coefficient between the target profile matrix W and its approximate matrix AH was r = 0.883, indicating good approximation accuracy.

[0143] <7-2> Reconstitution of Fragrance Cartridges For the fragrance profile set (coefficient matrix H) obtained in <7-1> above, the blending ratios for reconstitution with 40 or fewer aroma components were calculated. As a result, a matrix (12 rows x 44 columns) representing the blending ratios of aroma components for reconstituting 12 fragrance cartridges was obtained. The average correlation coefficient between the target profile and the composite profile of the reconstituted product for the 12 fragrance cartridges was r = 0.824 (standard deviation 0.147, minimum 0.578, maximum 0.994).

[0144] Table 16 shows the number of aroma components that make up each of the 12 fragrance cartridges.

[0145]

Claims

1. A method for predicting a formulation that exhibits a target sensory quality, comprising: a step of identifying a composite ratio of two or more target substances and receptor activity profiles of those target substances that gives a composite profile that approximates a target profile, wherein the receptor activity profiles are receptor activity profiles for two or more receptors, respectively, the target profile is a receptor activity profile that corresponds to a target sensory quality for the receptors, and the formulation of the target substances at the composite ratio is considered to be a formulation that exhibits the target sensory quality.

2. The method of claim 1, wherein the target substances consist of 2 to 8 types of substances.

3. The method of claim 1, wherein in said step, the target substance is selected from a group of test substances.

4. The method of claim 3, wherein the group of test substances consists of a greater number of substances than the target substance.

5. The method of claim 3, wherein the group of test substances consists of 100 or more substances.

6. The method according to claim 1, wherein the process is carried out by non-negative constraint sparse modeling, using the target profile as an objective variable and the receptor activity profile for the receptor of each substance constituting the test substance group as an explanatory variable.

7. The method of claim 6, wherein the sparse modeling is performed utilizing a Lasso regression model.

8. The method of claim 1, wherein said step is performed so that the error between said target profile and said synthetic profile, in terms of weighted RMSE, is 0.7 or less.

9. The method of claim 1, wherein the receptor comprises more than 300 types of receptors.

10. The method of claim 1, wherein the receptor is selected from the group consisting of G protein-coupled receptors and ionotropic receptors.

11. The method of claim 1, wherein the receptor is selected from the group consisting of olfactory receptors and gustatory receptors.

12. 50% or more of the total number of the receptors are 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, 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, 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, OR 5AP2, 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, OR51S 1, OR51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, OR52B6, OR52D1, OR52E2, OR52E4, OR52E5, OR52 2. The method of claim 1, wherein the IL-16A1, IL-16B2, IL-16C1, IL-16D2, IL-16D3, IL-16D4, IL-16D5, IL-16D6, IL-16D7, IL-16D8, OR52H1, OR52I2, OR52J3, OR52K2, OR52L2P, OR52M1, OR52N1, OR52N2, OR52N4, OR52N5, OR52P2P, OR52R1, OR52W1, OR52Z1P, OR56A1, OR56A3, OR56A4, OR56A5, OR56B1, OR56B2P, and OR56B4.

13. The 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, OR2AP1, OR2AJ2, OR2AK3, OR2AK4, OR2AK5, OR2AK6, OR2AK7, OR2AK8, OR2AK9, OR2AK10, OR2AK11, OR2AK12, OR2AK13, OR2AK14, OR2AK15, OR2AK16, OR2AK17, OR2AK18, OR2AK19, OR2AK210, OR2AK22, OR2AK19, OR2AK23, OR2AK16, OR2AK18, OR2AK24, OR2AK19, OR2AK19, OR2AK25 ... R2AT4, OR2B2, OR2B3, OR2B6, OR2B11, OR2C1, OR2C3, OR2D2, OR2D3, OR2F1, OR2G2, OR2G3, OR2G6, OR2H1, OR2H2, OR2J2, OR2J3, OR2K2, OR2L2, OR2L8, OR2 L13, OR2M2, OR2M4, OR2M7, OR2S2, OR2T1, OR2T2, OR2T5, OR2T6, OR2T8, OR2T10, OR2T11, OR2T27, OR2T34, OR2V2, OR2W1, OR2W3, OR2Y1, OR2Z1, OR3A1, OR3 A2, OR3A3, OR3A4, OR4A5, OR4A15, OR4A16, OR4A47, OR4B1, OR4C3, OR4C5, OR4C6, OR4C11, OR4C12, OR4C13, OR4C15, OR4C16, OR4C46, OR4D1, OR4D2, OR4D 5, OR4D6, OR4D9, OR4D10, OR4D11, OR4E2, OR4F3, OR4F5, OR4F6, OR4F14P, OR4F15, OR4G11P, OR4H12P, OR4K1, OR4K2, OR4K5, OR4K13, OR4K14, OR4K15, OR4 K17, 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, OR 51T1, OR51V1, OR52A1, OR52A4, OR52A5, OR52B2, OR52B4, 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.

14. The method of claim 1, wherein the receptor is a human receptor.

15. The method of claim 1, further comprising the step of evaluating the sensory qualities of the predicted formulation.

16. The method of claim 15, wherein the evaluation is performed by sensory evaluation.

17. The method of claim 1, comprising: a step of identifying, for each of a plurality of targets, a composite ratio of the two or more target substances and the receptor activity profiles of those target substances that gives a composite profile that approximates the target profile; and a minimum set determination step of determining a predetermined number of composite elements and composite ratios of those composite elements, as well as one or more target substances that constitute each of the composite elements, that approximate the composite profile of the identified plurality of targets.

18. The method according to claim 17, wherein in the minimum set determination step, the composite profile of the multiple targets is decomposed into the predetermined number of composite elements and composite ratios of those composite elements by nonnegative matrix factorization (NMF).

19. A method for reconstructing a target sensory quality, comprising: a step of predicting a formulation that exhibits the target sensory quality by a method according to any one of claims 1 to 16; and a step of formulating the target material based on the formulation.

20. The method of claim 19, wherein the target material is mixed in a ratio substantially the same as the synthesis ratio.

21. The method of claim 20, wherein for each target substance, the blending ratio is 0.9 to 1.1 times the synthesis ratio.

22. A method for reconstituting a target sensory quality, comprising: determining a predetermined number of synthetic elements and their synthetic ratios, which approximate the synthetic profile of the multiple targets, by the method of claim 17 or 18, and one or more target substances constituting each of the synthetic elements; and blending the synthetic elements based on the synthetic ratios of the synthetic elements.

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