Systems and methods for modeling and displaying sweetener synergy

Abstract

Disclosed herein are systems and computer-implemented methods of generating a model and using the model to determine synergy between a plurality of compounds for application to a mixture of human taste receptors. The plurality of compounds in the mixture may be sweet compounds. The model may be generated based on a function, where the sigmoid function approximates a set of experimental data, and where the experimental data maps a combination of concentrations of compounds to a measurement response of the taste receptor. Once generated, the model may then be used to determine the synergy between the plurality of compounds.

Description

Technical Field

[0001] Various embodiments of the present disclosure generally relate to methods for generating models of edible compound mixtures that activate taste receptors, and more particularly, to methods for generating models of sweet compound mixtures and analyzing synergies using such models, and methods for preparing edible products using synergistic compound mixtures. Background Art

[0002] Synergistic edible compound mixtures are commonly used to impart various properties to edible products. Experiments have shown that such mixtures can significantly increase the response levels of taste receptors. Examples of these mixtures include synergistic blends of sweeteners, which have been used for purposes such as enhancing the sweetness of foods and beverages, masking bitter or off-flavors, and reducing production costs. Therefore, it is desirable to understand how various sweet compounds interact with each other at the taste receptor level in mixtures. Existing methods for studying the interactions between compounds (e.g., sweeteners) in mixtures and the effects of these compounds on taste receptors typically rely on data from physical measurements and may include varying the concentration of each compound. However, these techniques may pose challenges in determining taste receptor responses and synergies at data points that have not been physically measured.

[0003] Accordingly, there is a need for a predictive model that can be used to determine taste receptor responses and synergies for various edible compound mixtures, rather than relying solely on specific experimental results where taste receptor responses are physically measured.

[0004] The background description provided herein is for the purpose of presenting the background of the present disclosure in general. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art or suggestions of prior art by virtue of their inclusion in this section. Summary of the Invention

[0005] According to aspects of the present disclosure, systems and computer-implemented methods are disclosed for generating a model and using the model to determine synergies between multiple compounds in a mixture to be applied to human taste receptors. The systems and methods disclosed herein can also be used to prepare synergistic sweetener compositions based on the model.

[0006] In one aspect, an exemplary computer-implemented method can be executed by a processor and can include receiving a first data set including a plurality of data points, each data point including a concentration level of each compound in a mixture of multiple compounds. The method can further include receiving a second data set including a response level of a human taste receptor determined for each data point from the plurality of data points.

[0007] The method may further include generating a model of the response levels of human taste receptors based on a first data set and a second data set. The response level model can be generated by determining a mapping between each data point from the plurality of data points and the response level of a human taste receptor determined for each data point from the plurality of data points. The method may further include determining a first function of the concentration level of each compound in the mixture, the function having one or more unknown coefficients. Further, the method may include: determining the one or more unknown coefficients by fitting the first function to the mapping between each data point from the plurality of data points and the response level of a human taste receptor determined for each data point from the plurality of data points, thereby determining a second function of the concentration level of each compound in the mixture, the second function having the same form as the first function.

[0008] In some embodiments, the first function is a sigmoid function. In some embodiments, the sigmoid function is based on a logistic function, a trigonometric function, or the Hill equation.

[0009] In some embodiments of the foregoing aspects (which may be combined with any of the foregoing embodiments), data associated with the model is displayed to the user on a user device.

[0010] In some embodiments of the foregoing aspects (which may be combined with any of the foregoing embodiments), the human taste receptor is the sweet taste receptor T1R2 / T1R3, and each compound in the mixture is a sweet compound.

[0011] In some embodiments of the foregoing aspects (which may be combined with any of the foregoing embodiments), each compound in the mixture is selected from the group consisting of sugars, mogrosides, sweet amino acids, polyols, artificial sweeteners, natural sweeteners, and sweet proteins.

[0012] In some embodiments of the foregoing aspects (which may be combined with any of the foregoing embodiments), the mixture comprises a first sweet compound, a second sweet compound, and a third sweet compound.

[0013] In some embodiments of the foregoing aspects (which may be combined with any of the foregoing embodiments), each of the first sweet compound, the second sweet compound, and the third sweet compound is selected from aspartame, sucrose, sucralose, rebaudioside A, rebaudioside D, rebaudioside M, thaumatin, neohesperidin, and S819 [1-((1H-pyrrol-2-yl)methyl)-3-(4-isopropoxyphenyl)thiourea].

[0014] In another aspect, a second exemplary method may be performed by a processor and may include receiving a plurality of data points associated with a plurality of sweeteners, each data point indicating a concentration level of a corresponding sweetener among the plurality of sweeteners. The method may further include determining a synergism between the plurality of sweeteners using a sweet receptor response model based on the received plurality of data points, wherein the synergism is measured by an increase in the response level of the sweet receptor to the plurality of sweeteners. The method further includes prompting a user via a user interface to prepare a synergistic sweetener composition that includes the plurality of sweeteners at the respective concentration levels indicated in the plurality of data points such that the synergistic sweetener composition produces the synergism determined using the sweet receptor response model.

[0015] In some embodiments of the foregoing aspect (which may be combined with any of the foregoing embodiments), the increase in the response level of the sweet receptor is equal to or greater than 25%.

[0016] In some embodiments of the foregoing aspect (which may be combined with any of the foregoing embodiments), the plurality of sweeteners includes at least two sweeteners.

[0017] In some embodiments of the foregoing aspect (which may be combined with any of the foregoing embodiments), the plurality of sweeteners includes at least three sweeteners.

[0018] In some embodiments of the foregoing aspect (which may be combined with any of the foregoing embodiments), the response level of the sweet receptor is displayed by the processor via the user interface.

[0019] In some embodiments of the foregoing aspect (which may be combined with any of the foregoing embodiments), the response level of the sweet receptor is displayed in luminosity.

[0020] In some embodiments of the foregoing aspect (which may be combined with any of the foregoing embodiments), the response level of the sweet receptor is displayed in a graph.

[0021] In some embodiments of the foregoing aspects (which may be combined with any of the foregoing embodiments), a sweet receptor response model is trained by determining, by a processor, a mapping between sample data points of a sample sweetener and corresponding response levels of a sweet receptor, each sample data point including a concentration level of a corresponding sample sweetener; determining, by the processor, a first function of the concentration level of the sample sweetener, the function having one or more unknown coefficients; and determining the one or more unknown coefficients by fitting the first function to the mapping between the sample data points and the corresponding response levels of the sweet receptor, and determining, by the processor, a second function of the concentration level of the sample sweetener, the second function having the same form as the first function.

[0022] In yet another aspect of the present disclosure, a third exemplary method includes preparing a sweetener composition having a synergy of two or more sweeteners at corresponding concentrations recommended by a sweet receptor model. The model may be generated based on a plurality of data points associated with a sweetener mixture, each data point including a concentration level of each sweetener in each sweetener mixture, and the sweet receptor data includes each response level of sweet receptor T1R2 / T1R3 determined for each data point from the plurality of data points. The model may be analyzed to determine the synergy between the sweeteners in the sweetener mixture and the corresponding concentration levels of the sweeteners exhibiting the synergy, so as to provide recommendations for the synergistic composition.

[0023] In some aspects, provided herein are sweetener compositions prepared according to any of the embodiments of the foregoing methods.

[0024] In some aspects, provided herein are confectionery products comprising any of the sweetener compositions described herein. In some embodiments, the confectionery products are selected from the group consisting of hard candies, gummies, mints, chewing gums, gelatin, chocolates, fudges, rock candies, fondants, licorices, hard-coated candies, caramels, and toffees. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings incorporated in and constituting a part of this specification illustrate various exemplary embodiments and, together with the specification, serve to explain the principles of the disclosed embodiments.

[0026] Figure 1 A flowchart depicting an exemplary method of generating a model according to one or more embodiments is shown.

[0027] Figure 2 An exemplary method of preparing a synergistic sweetener composition using a model according to one or more embodiments is described.

[0028] Figure 3A flowchart depicting an exemplary method of training a model according to one or more embodiments is shown.

[0029] Figure 4 An embodiment of a computer system that can execute the techniques presented herein is illustrated.

[0030] Figures 5A to 5C A graph depicting a comparison of experimental results of applying two sweeteners at different concentrations to a sweet taste receptor with results generated by the model, as discussed in Example 1, is shown.

[0031] Figure 6 A graph depicting a comparison of experimental results of applying three sweeteners at different concentrations to a sweet taste receptor with results generated by the model, as discussed in Example 2, is shown.

[0032] Figures 7A to 7C Each depicts a graph showing synergy in a mixture of two sweeteners determined using the model, as discussed in Example 3.

[0033] Figure 8 A graph depicting synergy in a mixture of three sweeteners determined using the model, as discussed in Example 4, is shown.

[0034] Figure 9A A graph depicting synergy between three different mixtures of two sweeteners, as discussed in Example 6, is shown.

[0035] Figure 9B A graph depicting the generation of data from the graph shown in Figure 9A by using the model to reconstruct data to show synergy between three sweeteners, as discussed in Example 6, is shown. Detailed Description

[0036] Various embodiments of the present disclosure generally relate to methods of generating models of edible compound mixtures that activate taste receptors. More specifically, various embodiments of the present disclosure relate to methods of generating models of sweet compound mixtures that determine the response of the sweet taste receptor T1R2 / T1R3, computer-implemented methods of using such models to analyze synergy between sweet compound mixtures, and methods of preparing edible products using compound mixtures with synergy determined using such models.

[0037] As discussed above, certain edible compounds (e.g., sweeteners) can be combined in a mixture at concentrations (i.e., concentration levels) that have a synergistic effect on taste receptors. The synergistic response of taste receptors can be determined by varying the concentrations of the compounds in the mixture. In some cases, when the synergism between two of three compounds has been determined, it may be desirable to determine the effect of synergism on taste receptors between combinations of three different compounds. However, this may require a large amount of testing and access to a large amount of data that may not be available. Further, linear interpolation may be considered suboptimal when predicting the response of taste receptors to three or more compounds at arbitrary concentrations.

[0038] Accordingly, embodiments of the present disclosure relate to solving, alleviating, or correcting the above problems by generating a model that can be used to predict the expected response of taste receptors (such as the sweet taste receptor T1R2 / T1R3) to compounds at different concentrations from a mixture of compounds, and capturing the synergism between the compounds. The model of the present disclosure can provide interpolation and extrapolation of the expected response of the human sweet taste receptor T1R2 / T1R3 outside and between specific data points of physical measurements. The model can have multiple uses, including using the model to predict the taste receptor response of many different compound mixtures and determining the synergism of different compound mixtures.

[0039] The present disclosure provides systems and methods for receiving data related to compounds in a compound mixture to be applied to human taste receptors and developing a model based on the data. The data can include the concentration of each compound and the corresponding response level of the human taste receptor. The model can be generated by selecting a function type for the model and generating a closed-form equation with multiple variables, each variable corresponding to the concentration of a compound in the mixture applied to the taste receptor.

[0040] In some examples herein, a computer-based model can be used to determine the T1R2 / T1R3 sweet taste receptor response based on different concentrations of at least two sweet compounds. The model can be derived from a functional assay of the sweet taste receptor, where the response of the T1R2 / T1R3 sweet taste receptor is measured in response to different concentrations of the at least two sweet compounds. The functional assay generates multiple data points. In some examples, the data points correspond to the concentration of a first sweet compound, the concentration of a second sweet compound, and the concentration of a third sweet compound. Further, the data used to derive the model of the present disclosure can include an indication of the response level of the sweet taste receptor when these concentrations are applied to the sweet taste receptor.

[0041] Once generated, the model can be used to determine the synergistic effects between compounds in a mixture. Synergistic effects can be determined when the response level of a taste receptor to a mixture of compounds is higher than the sum of the response levels of the taste receptor to each individual compound. In some embodiments, a computer-based model can be used to prepare a synergistic sweetener composition. Such sweetener compositions can be used to enhance the sweetness of food products including confectionery products.

[0042] Although the models described herein relate to the activation of human sweet taste receptors with sweeteners, the models of the present disclosure are applicable to other taste receptors (e.g., umami receptors) that can be activated by one, two, three, or more receptor-activating compounds.

[0043] The terms used hereinafter can be interpreted in the broadest reasonable manner, even if it is used in conjunction with the detailed description of certain specific examples of the present disclosure. In fact, certain terms may even be emphasized hereinafter; however, any term that is intended to be interpreted in a limiting manner will be disclosed and specifically defined in the detailed description section. Both the foregoing general description and the following detailed description are merely exemplary and explanatory and do not limit the claimed features.

[0044] In the detailed description herein, references to "an embodiment", "one embodiment", "a non-limiting embodiment", "in various embodiments", etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but each embodiment does not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is considered within the knowledge of those skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. After reading this specification, it will be clear to those skilled in the relevant art how to implement the present disclosure in alternative embodiments.

[0045] Generally speaking, terms can be understood, at least in part, from their use in context. For example, terms such as "and", "or", or "and / or" as used herein can include multiple meanings, which can depend, at least in part, on the context in which such terms are used. Typically, "or" when used in connection with a list such as A, B, or C is intended to mean A, B, and C, used in an inclusive sense herein, as well as A, B, or C, used in an exclusive sense herein. Additionally, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or property in a singular sense, or can be used to describe a combination of features, structures, or properties in a plural sense. Similarly, terms such as "a", "an", or "the" can also be understood to convey a singular usage or to convey a plural usage, at least in part, depending on the context. Additionally, the term "based on" can be understood to not necessarily be intended to convey a set of exclusive factors, but can allow for the existence of additional factors that are not necessarily explicitly described, again at least in part, depending on the context.

[0046] As used herein, the term "comprises", "comprising", or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, composition, article, or apparatus that comprises a list of elements does not include only those elements, but may include other elements not expressly listed or inherent to such process, method, composition, article, or apparatus. The term "exemplary" is used in the sense of "an example" rather than "an ideal". As used herein, unless the context otherwise indicates, the singular forms "a / an" and "the" include plural referents. Relative terms such as "about", "substantially", and "approximately" refer to being almost the same as a reference number or value, and should be understood to cover variations of plus or minus 5% of the specified amount or value.

[0047] As used herein, "taste" refers to the sensation caused by the activation or inhibition of receptor cells in the mouth of a subject. Different types of taste can include sweet, sour, salty, bitter, kokumi, umami, and any combination thereof.

[0048] The terms "sweetener" and "sweetening compound" can refer to a ligand or compound that is capable of binding to T1R2 / T1R3 and conferring or enhancing sweetness. For example, "sweetening compound" can include, but is not limited to, sugars, mogrosides, polyols, D-amino acids, sweet proteins, artificial sweeteners, sulfamates, and steviol glycosides.

[0049] As used herein, "synergy" or "synergistic response" can refer to an effect produced by two or more individual components, where when used in combination, the total effect produced by these components is greater than the sum of the individual effects of each component acting alone.

[0050] As used herein, a "model" or "machine learning model" generally includes instructions, data, and / or a model configured to receive an input and apply one or more of weights, biases, classification, or analysis to the input to generate an output. The output can include, for example, a classification of the input, an analysis based on the input, a design, process, prediction, or recommendation associated with the input, or any other suitable type of output. Machine learning models are typically trained using training data (e.g., empirical data and / or input data samples) that are fed into the model to establish, adjust, or modify one or more aspects of the model, such as weights, biases, criteria for forming classifications or clusters, etc. Aspects of a machine learning model can operate on the input linearly, in parallel, via a network (e.g., a neural network), or via any suitable configuration.

[0051] The execution of a machine learning model can include deploying one or more machine learning techniques such as linear regression, logistic regression, random forests, gradient boosting machines (GBMs), deep learning, and / or deep neural networks. Supervised and / or unsupervised training can be employed. For example, supervised learning can include providing training data and labels corresponding to the training data (e.g.) as ground truth. Unsupervised methods can include clustering, classification, etc. Any suitable type of training can be used, e.g., random, gradient boosted, random seeded, recursive, epoch- or batch-based, and so on.

[0052] Certain non-limiting embodiments will now be described with reference to block diagrams and operational illustrations of methods, processes, apparatuses, and devices. It should be understood that each block in the block diagrams or operational illustrations, and combinations of blocks in the block diagrams or operational illustrations, can be implemented by analog hardware or digital hardware and computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an ASIC, or other programmable data processing device to alter its functionality as described in detail herein, such that the instructions executed by the processor of the computer or other programmable data processing device implement the functions / actions specified in the block diagram or one or more of the operational blocks. In some alternative specific implementations, the functions / actions noted in the blocks may not occur in the order noted in the operational illustration. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order, depending on the functions / actions involved.

[0053] Now referring to the drawings, Figure 1FIG. 0 depicts a flowchart of an exemplary method 100 for generating a model according to the present disclosure. The model can be configured to provide data for synergistic effects between multiple compounds in a mixture applied to a human taste receptor, such as the sweet taste receptor T1R2 / T1R3. According to an embodiment, the model is capable of being implemented in computer software on a desktop computer, laptop computer, notebook computer, handheld device, or server-class computing device. Further, method 100 can be implemented in computer software and is capable of being executed on a similar computing device.

[0054] Method 100 begins at step 110. At step 110, a processor receives a first data set. The first data set includes a plurality of data points. Each data point includes the concentration level of each compound in a mixture of multiple compounds. In some instances, the first data set is received from a database or a file. The plurality of data points can be represented as a data structure such as an array, matrix, vector, lookup table, or a combination thereof, which can be stored in the memory of a computer, or stored on a peripheral device or a network-connected storage device.

[0055] At step 120, the processor receives a second data set. The second data set includes the response level of a human taste receptor determined for each data point from the plurality of data points received at step 110. The first data set and the second data set can be based on experimental data obtained from a functional assay of a human taste receptor, where the response of the receptor is measured in response to different concentrations of each compound in a mixture of multiple compounds. In some instances, the first data set and the second data set can be received together as a single data set. The data points can define a specific response of the receptor when different compounds at a specific concentration are applied to it. For example, each data point can be represented as an n-tuple, where the first n - 1 elements represent the concentrations of different compounds that have been applied to the sweet taste receptor T1R2 / T1R3. The nth element then represents the response level of the receptor to the given concentration. Such a response level can be visually represented as a luminescence level.

[0056] A variety of data filling methods can be used to fill a database or file storing the first dataset of experimental data and the second data. For example, an end user (such as an experimenter) can access a computer interface (such as a graphical user interface (GUI) or a command line interface (CLI)) and manually enter data points (corresponding to the concentration of a compound) and the corresponding measured luminescence of the receptor. Further, in other embodiments, the data points and luminescence can be transmitted as a file or data stream to a receiving process on a computer, where the receiving process automatically fills the database. In other embodiments, a device configured to measure the luminescence of a sweet taste receptor based on the detected compound concentration is further configured to automatically transmit the concentration data and luminescence data to a receiving process for subsequent input into a database or file. Additionally, the process of measuring the luminescence of a sweet taste receptor itself can be configured to directly access and fill a database or file.

[0057] In step 130, a model of the response level of a human taste receptor (e.g., a sweet taste receptor) is generated based on the first dataset and the second dataset. After receiving the data in the previous steps, steps 140 to 160 are performed to generate the model.

[0058] In step 140, the experimental data received from the functional assay is processed. Step 140 includes determining the mapping between each data point from the plurality of data points and the response level of the human taste receptor determined for each data point from the plurality of data points. In step 150, a first function of the concentration level of each compound in the mixture is determined. The function can have one or more unknown coefficients. In aspects of the present disclosure, determining the first function can include selecting a sigmoid function from a plurality of sigmoid functions. For example, a sigmoid function can be selected from a plurality of sigmoid functions to approximate the experimental data. The type of function used to model the receptor response is a function type having an S-shaped (or "S") shape, and many biological processes can be modeled using this shape. Several sigmoid functions are used in biological modeling. Among the functions used are the logistic function, the Gompetz sigmoid function, the trigonometric function, and the Hill equation.

[0059] The logistic function can be expressed as: The Gompetz sigmoid function can be expressed as where a, b, and c can vary to change the asymptote, displacement, and growth rate, respectively. The trigonometric function representing the sigmoid shape is the hyperbolic tangent function or tanh(x). Finally, the Hill equation can be expressed as Further, other sigmoid functions can be obtained using linear combinations of other known sigmoid functions and by multiplication and / or superposition of known sigmoid functions.

[0060] Thus, according to an embodiment, any one of the above functions (or indeed, another sigmoid function) can be selected in step 150. Such a function can be stored in a database as an equation, for example, where the parameter values (e.g., the value of "n" in the Hill equation) can vary.

[0061] In step 160, a second function that determines the concentration level of each compound in the mixture is determined. The second function has the same form as the first function. To perform step 160, the expression representing the combination of compound concentrations is replaced by the independent variable of the selected sigmoid function. In one or more embodiments, the expression for the compound concentration is defined as the sum of the products of the compound concentrations and the binding constants, as shown in the following expression:

[0062] a x x + a y y + a z z + a xy xy + a xz xz + a yz yz + a xyz xyz

[0063] In this expression, x, y, and z are the concentrations of each compound (e.g., the first sweetening compound, the second sweetening compound, and the third sweetening compound, respectively), and a n coefficient is the unknown binding constant. Thus, assuming the selected sigmoid function is the Hill equation, after substituting the above expression, the sigmoid function can be expressed as:

[0064]

[0065] Similarly, if the selected sigmoid function is the logistic function, the sigmoid function can be expressed as:

[0066] Once the expression for the compound concentration is substituted into the selected sigmoid function, the value of the unknown coefficient in the sigmoid function can be determined. The one or more unknown coefficients are determined by fitting the first function (e.g., the sigmoid function) to the mapping between each data point from the plurality of data points and the response level of the human taste receptor determined for each data point from the plurality of data points. For example, in the case of the Hill equation, the unknown coefficients are the binding constants a x 、a y 、a z 、a xz 、a yz and a xyzand the exponent n. Similarly, for a logistic function, the unknown coefficients are the aforementioned association constants. For a Gompetz sigmoid function, the unknown coefficients are the association constants, as well as the asymptote, displacement, and growth rate.

[0067] According to an embodiment, non-linear regression is used to determine the unknown coefficients of the updated sigmoid function generated at step 160. That is, the coefficients of the function f(x, y, z) are selected to better approximate (or "fit") the experimental data received in steps 110 and 120. In this way, the difference between the simulated luminescence values generated by the sigmoid function and the actual luminescence levels specified in the experimental data is reduced.

[0068] In at least one instance, method 100 may include a further step of determining whether there are any more sigmoid functions to be evaluated to approximate the experimental data. For example, assuming that the first sigmoid function selected is the Hill equation and the method is adapted to further evaluate sigmoid functions having a hyperbolic tangent, logistic, or Gompetz form, method 100 may determine that these additional sigmoid functions are still to be evaluated. If there are more sigmoid functions to evaluate, method 100 returns to step 150, where the next sigmoid function is selected. The method may then proceed to step 160 for the next selected sigmoid function. In instances where multiple sigmoid functions are evaluated, the sigmoid function having the lowest approximation error among all evaluated sigmoid functions is selected as the model.

[0069] After generating the model in method 100, data associated with the model may be displayed to the user on the user device. For example, the data associated with the model may be in the form of a luminescence display graph. The luminescence graph may display the response (luminescence level) of human taste receptors to compounds in a mixture of compounds. In one aspect of the present disclosure, a sweetener or sweet compound may be used as the compound in a mixture of multiple compounds, and the experimental data used to generate the model is based on this compound. For example, prior to the step of receiving data in method 100, a functional assay of the sweet receptor T1R2 / T1R3 may be performed, where the response of the receptor is measured in response to different concentrations of two or more sweet compounds.

[0070] Sweetening compounds that can be used in the embodiments of the present disclosure may include sugars, sweet amino acids, polyols, artificial sweeteners, natural sweeteners, and sweet proteins. Exemplary sweetening compounds may include, but are not limited to: sucrose, fructose, glucose, high fructose corn syrup, tagatose, galactose, ribose, xylose, arabinose, rhamnose, erythritol, xylitol, mannitol, sorbitol, inositol, saccharin, methyl eugenol, saponarin E1, advantame, acesulfame potassium, alitame, aspartame, CH 401, dulcin, neotame, sodium cyclamate, sucralose, super aspartame, cynarin, glycyrrhizin, rebaudioside C, abrusoside A, abrusoside B, abrusoside C, abrusoside D, abrusoside E, apio-glycyrrhizin, arabino-glycyrrhizin, baiyunoside, brazzein, rubusoside, sarcostin V, sarcostin VI, D. cumminsii, cyclocarin A, cyclocarin I, dukeside A, glycyrrhizic acid, honokiol, 4β-hydroxyhesperetin-7-glucoside dihydrochalcone, astragaloside E, astragaloside E, 3-hydroxyphloridzin, 2,3-dihydro-6-methoxy-3-acetate, momordin maltosyl-α-(1,6)-neohesperidin dihydrochalcone, miraculin, mogroside IIE, mogroside III, mogroside IIIE, mogroside IV, mogroside V, 11-oxomogroside V, monatin, thaumatin, ammonium glycyrrhizinate (Mag), mulberroside Iib, naringin dihydrochalcone, neohesperidin (NHDC), neohesperidin dihydrochalcone (NHDHC), neomogroside, austroinulin, pantadin, perillartine, perillaldehyde I, perillaldehyde II, perillaldehyde III, perillaldehyde IV, perillaldehyde V, phlomiside I, phloridzin, phyllodulcin, polypodiodin A, potassium magnesium calcium glycyrrhizinate, pterocaryoside A, pterocaryoside B, rebaudioside A, rebaudioside B, rebaudioside D, rebaudioside M, rubusoside, scandoside R6, and SE-1 having the following structure (Formula I):

[0071]

[0072] SE-2 having the following structure (Formula II):

[0073]

[0074] SE-3 having the following structure (Formula III):

[0075]

[0076] SE-4 having the following structure (Formula IV):

[0077]

[0078] Prunasin I, sodium glycyrrhizinate, steviolbioside, stevioside, α-glucosyl phyllodulcin A, styracoside B, styracoside G, styracoside H, styracoside I, styracoside J, sweroside, thaumatin, ammonium glycyrrhizinate (TAG), trifolin, curculin, strogin 1, strogin 2, strogin 4, miraculin, hodulcin, jujuboside II, jujuboside III, abruside E, piantic acid I monoglucuronide, piantic acid II monoglucuronide, chlorogenic acid, β-(1,3-dihydroxy-4-methoxybenzyl)-hesperetin dihydrochalcone, 3′-carboxy-hesperetin dihydrochalcone, 3′-stevioside analog, and S819 [1-((1H-pyrrol-2-yl)methyl)-3-(4-isopropoxyphenyl)thiourea] having the following structure (Formula V):

[0079] In some instances, the model can be based on a first sweetening compound and a second sweetening compound selected from the group disclosed above. In other instances, the model can be based on a first sweetening compound, a second sweetening compound, and a third sweetening compound can be selected from the group of sweetening compounds disclosed above. In at least one instance, the plurality of sweetening compounds can include a combination of thaumatin, neohesperidin dihydrochalcone (NHDC), and aspartame. Other instances can include various combinations of aspartame, sucrose, sucralose, rebaudioside A, rebaudioside D, thaumatin, NHDC, and S819.

[0080] In some instances, the plurality of sweetening compounds can include two or more compounds that bind to different sites of the T1R2 / T1R3 sweet receptor. In at least one instance, at least one compound in the mixture can bind to T1R2, and at least one compound in the mixture can bind to T1R3. For example, at least one compound can bind to the seven transmembrane domain (7TM), Venus flytrap domain, or cysteine-rich domain of T1R2, and at least one compound can bind to the seven transmembrane domain (7TM), Venus flytrap domain, or cysteine-rich domain of T1R3. In certain instances, each compound in the mixture can bind to a different domain of T1R2 or a different domain of T1R3. In other instances, each compound in the mixture can bind to the same domain of T1R2 or the same domain of T1R3.

[0081] Once a model is calculated based on experimental data according to Method 100, the model can be used in various applications. For example, the model can be used to determine a predicted receptor response for a given concentration of a compound. Alternatively, the model can be used to determine an unknown concentration of a compound and a specific desired receptor response given a specific concentration of other compounds. The model can also be used to determine a set of compound combinations that at least elicit a threshold receptor response. Further, the model can be used to determine a synergistic effect between two or more different compounds (e.g., sweetening compounds) in a mixture of compounds.

[0082] Figure 2 A flowchart depicting an exemplary Method 200 of using a model according to the present disclosure is shown. Specifically, Method 200 can be performed to prepare a synergistic sweetener composition. Prior to preparing the synergistic sweetener composition, Method 200 details using a model to determine a synergistic effect between multiple sweeteners in a mixture of sweeteners.

[0083] In step 202, a plurality of data points associated with multiple sweeteners are received. Each data point indicates a concentration level of a corresponding sweetener among the multiple sweeteners. Each sweetener among the multiple sweeteners can be a sweetener from the group of sweetening compounds disclosed above.

[0084] Next, in step 204, based on the received plurality of data points, a sweet receptor response model is used to determine a synergistic effect between the multiple sweeteners. For example, after generating a model derived from a functional assay of human sweet receptors according to Method 100 as described above, the model can be used in step 204 to determine a synergistic effect between the multiple sweeteners. In step 204, the synergistic effect is measured by an increase in the response level of the sweet receptor to the multiple sweeteners. In one embodiment, when the response level of the human taste receptor to the mixture is higher than the sum of the response levels of the human taste receptor to each individual compound, the model can determine a synergistic effect between two or more compounds in the mixture on which the model is based.

[0085] As described above with respect to Method 100, the response level of the human taste receptor (e.g., sweet receptor) can be displayed in luminescence in the form of a graph. For example, a processor can display the response level of the sweet receptor via a user interface. Using the model to determine the synergistic effect can include determining a synergistic function, which is obtained by subtracting the response level of the sweet receptor to each individual sweetener from the response level of the sweet receptor to a mixture of two or more sweeteners. According to the present disclosure, the synergistic function for determining the synergistic effect can be expressed as:

[0086] Max(L(X&Y&Z)-L(X)-L(Y)-L(Z), 0)

[0087] In the above synergy function, L (luminescence) is the normalized receptor response of a mathematical model, and X, Y, and Z are three components of the mixture (e.g., sweeteners). In some instances, the synergy between sweeteners in a mixture of two sweeteners can be determined. In these instances, the synergy function can be expressed as:

[0088] Max(L(X&Y)vL(X)-L(Y), 0)

[0089] Once the synergy response is determined, a plot of the synergy response as a function of the concentration of each sweetener in the mixture can be made. For example, a plot of the synergy function Syn(X, Y, Z) or Syn(X,Y) versus the concentration of the sweetener can be made. This plot can be displayed as a graph on the user device via the user interface. The graph can display luminescence values ranging from 0 to 1. In some instances, the synergy can be depicted as an increase in the (sweetness) signal. For example, the increase in the response level of the sweet taste receptor can be at least 25%. In some instances, the increase in the response level of the mixture can be in the range of 25% to 60%.

[0090] After determining the synergy, method 200 proceeds to step 206. In step 206, the user is prompted via the user interface to prepare a synergy sweetener composition having the various sweeteners at the corresponding concentration levels indicated in the plurality of data points such that the synergy sweetener composition produces the synergy determined using the sweet taste receptor response model in step 204.

[0091] Before the model is used to determine the synergy between multiple sweet compounds, the model is trained using a plurality of sample data points, as Figure 3 shown. Figure 3 Exemplary method 300 illustrates training the sweet taste receptor response model used in method 200.

[0092] In step 302, a mapping between the sample data points of the sample sweeteners and the corresponding response levels of the sweet taste receptor is determined. Each sample data point includes the concentration level of the corresponding sample sweetener.

[0093] In step 304, a first function of the concentration levels of the sample sweeteners is determined. The function has one or more unknown coefficients. For example, the first function can be the sigmoid function as described above with respect to method 100.

[0094] In step 306, a second function that determines the concentration level of the sample sweetener is determined. The second function has the same form as the first function. The second function is determined by fitting the first function to the mapping between the sample data points and the corresponding response levels of the sweet taste receptors to determine one or more unknown coefficients.

[0095] In some embodiments, the synergistic sweetener compositions produced in accordance with the present disclosure can be used in any food or beverage composition. Such compositions can include, but are not limited to, baked goods, dairy products, carbonated and non-carbonated beverages, confectionery products, and the like. Specifically, the synergistic sweetener compositions of the present disclosure can be used in confectionery products. The confectionery products can be in the form of hard candies, soft candies, mints, chewing gums, gelatin, chocolates, fudges, lollipops, fondants, licorices, hard-coated candies, toffees, or toffees. The synergistic sweetener compositions can be used to enhance sweetness.

[0096] In addition to being configured to determine the synergy between multiple compounds (e.g., sweeteners) in a mixture of multiple compounds, the model of the present disclosure can also provide additional benefits. For example, the model can be capable of reconstructing a three-compound (3D) data set from experiments based on one compound (1D) or two compounds (2D) without having to conduct experiments with the three compounds. For example, 2D experiments can be reconstructed from 1D diagonals, from 1D lines, and / or from any scattered set of 2D points. In some embodiments, 3D data can be reconstructed from diagonals, from any 2D projection, from 1D lines, and / or from any scattered set of 3D points.

[0097] Figure 4 Illustrates a specific implementation of a computer system that can execute the techniques given herein. The computer system 400 can include a set of instructions that can be executed to cause the computer system 400 to perform any one or more of the methods or computer-based functions disclosed herein. The computer system 400 can operate as a standalone device or can be connected, for example, using a network to other computer systems or peripheral devices.

[0098] In a networked deployment, computer system 400 may operate as a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. Computer system 400 may also be implemented as or incorporated into a variety of devices, such as a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile device, palmtop computer, laptop computer, desktop computer, communication device, wireless telephone, landline telephone, control system, camera, scanner, fax machine, printer, pager, personal trusted device, web appliance, network router, switch or bridge, or any other machine capable of executing a set of (sequential or otherwise) instructions that specify actions to be taken by that machine. In a particular implementation, computer system 400 may be implemented using an electronic device that provides voice, video, or data communication. Further, although a single computer system 400 is illustrated, the term "system" should also be understood to include any collection of systems or subsystems that individually or jointly execute one or more sets of instructions to perform one or more computer functions.

[0099] As Figure 4 illustrated, computer system 400 may include a processor 402, such as a central processing unit (CPU), a graphics processing unit (GPU), or both. Processor 402 may be a component in a variety of systems. For example, processor 402 may be part of a standard personal computer or workstation. Processor 402 may be one or more general-purpose processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other devices now known or later developed for analyzing and processing data. Processor 402 may implement software programs such as manually generated (i.e., programmed) code.

[0100] The computer system 400 may include a memory 404 that can communicate via a bus 408. The memory 404 may be a main memory, a static memory, or a dynamic memory. The memory 404 may include, but is not limited to, computer-readable storage media, such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tapes or disks, optical media, etc. In a specific implementation, the memory 404 includes a cache or random access memory for the processor 402. In an alternative specific implementation, the memory 404 is separate from the processor 402, such as a cache memory of the processor, a system memory, or other memory. The memory 404 may be an external storage device or a database for storing data. Examples include hard disk drives, compact discs ("CDs"), digital video discs ("DVDs"), memory cards, memory sticks, floppy disks, universal serial bus ("USB") memory devices, or any other device operable to store data. The memory 404 is operable to store instructions executable by the processor 402. The functions, actions, or tasks illustrated in the figures or described herein may be performed by a programmed processor 402 that executes instructions stored in the memory 404. The functions, actions, or tasks are independent of a particular type of instruction set, storage medium, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, microcode, etc., operating alone or in combination. Similarly, the processing strategy may include multiprocessing, multitasking, parallel processing, etc.

[0101] As shown, the computer system 400 may also include a display unit 410, such as a liquid crystal display (LCD), an organic light-emitting diode (OLED), a flat panel display, a solid state display, a cathode ray tube (CRT), a projector, a printer, or other display devices now known or later developed for outputting determined information. The display 410 may serve as an interface for a user to view the functions of the processor 402, or specifically as an interface to software stored in the memory 404 or the drive unit 406.

[0102] Additionally or alternatively, the computer system 400 may include an input device 412 configured to allow a user to interact with any component of the system 400. The input device 412 may be a numeric keypad, a keyboard, or a cursor control device (such as a mouse or a joystick), a touch screen display, a remote control device, or any other device operable to interact with the computer system 400.

[0103] The computer system 400 may also or alternatively include a disk drive unit or an optical drive unit 406. The disk drive unit 406 may include a computer-readable medium 422, in which one or more sets of instructions 424, such as software, may be embedded. Further, the instructions 424 may embody one or more of the methods or logics described herein. During execution by the computer system 400, the instructions 424 may reside, wholly or in part, within the memory 404 and / or the processor 402. The memory 404 and the processor 402 may also include the computer-readable medium as described above.

[0104] In some systems, the computer-readable medium 422 includes the instructions 424, or receives and executes the instructions 424 in response to a propagated signal, such that a device connected to the network 450 can transmit voice, video, audio, images, or any other data through the network 450. Further, the instructions 424 may be transmitted or received through the network 450 via the communication port or interface 420 and / or using the bus 408. The communication port or interface 420 may be part of the processor 402 or may be a separate component. The communication port 420 may be created in software or may be a physical connection in hardware. The communication port 420 may be configured to connect to the network 450, an external medium, the display 410, or any other component in the system 400 or a combination thereof. The connection to the network 450 may be a physical connection such as a wired Ethernet connection or may be established wirelessly as described below. Similarly, additional connections to other components of the system 400 may be physical connections or may be established wirelessly. The network 450 may alternatively be directly connected to the bus 408.

[0105] Although the computer-readable medium 422 is shown as a single medium, the term "computer-readable medium" may include a single medium or multiple media, such as a centralized or distributed database, and / or associated caches and servers storing one or more sets of instructions. The term "computer-readable medium" may also include any medium that can store, encode, or carry a set of instructions for execution by a processor or that causes a computer system to perform any one or more of the methods or operations disclosed herein. The computer-readable medium 422 may be non-transitory and may be tangible.

[0106] The computer-readable medium 422 may include solid-state memory such as a memory card or other package that houses one or more non-volatile read-only memories. The computer-readable medium 422 may be random access memory or other volatile, rewritable memory. Additionally or alternatively, the computer-readable medium 422 may include magneto-optical or optical media such as a disk or tape or other storage device to capture carrier signals such as signals transmitted through a transmission medium. Digital file attachments of e-mails or other self-contained information archives or sets of archives may be considered a distribution medium of a tangible storage medium. Accordingly, the present disclosure is considered to include any one or more of a computer-readable medium or distribution medium in which data or instructions may be stored, as well as other equivalents and successor media.

[0107] In an alternative embodiment, dedicated hardware implementations such as application specific integrated circuits, programmable logic arrays, and other hardware devices may be constructed to implement any one or more of the methods described herein. Applications that may include devices and systems of various embodiments may broadly include a variety of electronic and computer systems. One or more embodiments described herein may be implemented by functionality: using two or more particular, interconnected hardware modules or devices, where related control and data signals may be transferred between and through the modules; or as part of an application specific integrated circuit. Accordingly, the system includes software, firmware, and hardware implementations.

[0108] The computer system 400 can be connected to one or more networks 450. The network 450 can define one or more networks, including wired networks or wireless networks. The wireless network can be a cellular phone network, 802.11, 802.16, 802.20, or WiMax network. Further, such networks can include public networks such as the Internet, private networks such as intranets, or combinations thereof, and can utilize various network protocols available now or developed later, including but not limited to TCP / IP-based network protocols. The network 450 can include a wide area network (WAN) such as the Internet, a local area network (LAN), a campus network, a metropolitan area network, a direct connection such as through a universal serial bus (USB) port, or any other network that allows data communication. The network 450 can be configured to couple one computing device to another computing device to enable data transfer between the devices. Generally, the network 450 can be enabled to use any form of machine-readable medium to transfer information from one device to another. The network 450 can include communication methods through which information can propagate between computing devices. The network 450 can be divided into sub-networks. The sub-networks can allow access to all other components connected thereto, or the sub-networks can restrict access between components. The network 450 can be regarded as a public network connection or a private network connection, and can include, for example, virtual private networks or encryption or other security mechanisms employed over the public Internet.

[0109] In accordance with various specific embodiments of the present disclosure, the methods described herein can be implemented by software programs executable by a computer system. Further, in exemplary non-limiting specific embodiments, the specific embodiments can include distributed processing, component / object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be configured to implement one or more of the methods or functions described herein.

[0110] Although this specification describes components and functions that can be implemented in specific specific embodiments with reference to specific standards and protocols, the present disclosure is not limited to such standards and protocols. For example, standards for Internet and other packet-switching network transmissions (e.g., TCP / IP, UDP / IP, HTML, HTTP) represent examples of the prior art. Such standards are periodically replaced by faster or more efficient equivalent standards having substantially the same functions. Therefore, alternative standards and protocols having the same or similar functions as those disclosed herein are considered to be their equivalents.

[0111] It should be understood that in one embodiment, the steps of the method discussed are performed by an appropriate processor (or multiple processors) of a processing (i.e., computer) system executing instructions (computer readable code) stored in a storage device. It should also be understood that the disclosed embodiments are not limited to any particular implementation or programming technique, and the disclosed embodiments can be implemented using any suitable technique for implementing the functionality described herein. The disclosed embodiments are not limited to any particular programming language or operating system.

[0112] It should be understood that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped in a single embodiment, figure, or description thereof in order to simplify the disclosure and aid in understanding one or more of the various inventive aspects. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than those expressly recited in each claim. More precisely, as reflected in the appended claims, the inventive aspects lie in less than all the features of a single preceding disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim independently serving as a separate embodiment of the invention.

[0113] In addition, although some embodiments described herein include some but not other features included in other embodiments, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments, as will be understood by those skilled in the art. For example, in the appended claims, any of the embodiments claimed may be used in any combination.

[0114] Therefore, although certain embodiments have been described, those skilled in the art will recognize that other and further modifications may be made to the embodiments without departing from the spirit of the invention, and it is intended to claim all such changes and modifications that fall within the scope of the invention. For example, functionality may be added or deleted from the block diagrams, and operations may be interchanged in functional blocks. Steps may be added or deleted to the described methods within the scope of the invention.

[0115] The above-disclosed subject matter should be considered illustrative and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of the present disclosure. Accordingly, to the maximum extent permitted by law, the scope of the present disclosure will be determined by the broadest permissible interpretation of the appended claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description. Although various embodiments of the present disclosure have been described, it will be apparent to those of ordinary skill in the art that there can be more embodiments within the scope of the present disclosure. Therefore, the present disclosure is not limited except as defined by the appended claims and their equivalents.

[0116] However, the following examples are intended to illustrate the present disclosure and are not restrictive in nature. It should be understood that the present disclosure encompasses additional embodiments consistent with the foregoing description and the following examples.

[0117] Example

[0118] Example 1 :(2D) Modeling of Binary Combinations of NHDC, Thaumatin, and Aspartame

[0119] Activation of the Human Sweet Taste Receptor

[0120] This example describes the modeling of human sweet taste receptor activation, showing the effects of the following combinations respectively: the combination of NHDC and thaumatin (as Figure 5A shown); the combination of NHDC and aspartame (as Figure 5B shown); and the combination of aspartame and thaumatin (as Figure 5C shown).

[0121] Method: According to the above technology, the generation of models for combining different concentrations of NHDC and thaumatin as Figure 5A shown, the generation of models for combining different concentrations of NHDC and aspartame as Figure 5B shown, and the generation of models for combining different concentrations of aspartame and thaumatin as Figure 5C shown were described. For each embodiment, a predictive model based on the Hill equation was generated. These models were generated based on experimental data including 1152 data points, which represent 12 concentrations of aspartame, 8 concentrations of NHDC, and 12 concentrations of thaumatin. The concentration of aspartame varied between 0 mM and 10 mM, the concentration of NHDC varied between 0 mM and 1 mM, and the concentration of thaumatin varied between 0 mM and 100 mM. Regarding the mixture of NHDC and thaumatin ( Figure 5A ), the Hill equation that most closely approximated the experimental data had coefficients a x = 0.5, a y = 0.1, a xy= 4.8 and n = 0.8. For the mixture of NHDC and aspartame ( Figure 5B ), the Hill equation that most closely approximates the experimental data has coefficients a x = 0.15, a y = 0.5, a xy = 9.9 and n = 0.8. For the mixture of aspartame and thaumatin ( Figure 5C ), the Hill equation that most closely approximates the experimental data has coefficients a x = 0.05, a y = 0.1, a xy = 0.2 and n = 0.9.

[0122] Results: Figures 5A to 5C are visual comparisons of the modeling results of the sigmoid function (Hill equation) with the experimental results of applying two sweeteners at different concentrations to the sweet taste receptors of humans, where the same concentration of sweetener is input into the model. In Figure 5A , the thaumatin scale represents concentrations of 0 (μm), 0.06 (μm), 0.1 (μm), 0.3 (μm), 0.6 (μm), 1 (μm), 3 (μm), 6 (μm), 10 (μm), 30 (μm), 60 (μm), 100 (μm), and the NHDC scale represents concentrations of 0 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM). In Figure 5B , the aspartame scale represents concentrations of 0 (mM), 0.006 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM), 3 (mM), 6 (mM), 10 (mM), and the NHDC scale represents concentrations of 0 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM). In Figure 5C , the thaumatin scale represents concentrations of 0 (uM), 0.06 (uM), 0.1 (uM), 0.3 (uM), 0.6 (uM), 1 (uM), 3 (uM), 6 (uM), 10 (uM), 30 (uM), 60 (uM), 100 (uM), and the aspartame scale represents concentrations of 0 (mM), 0.006 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM), 3 (mM), 6 (mM), 10 (mM).

[0123] Example 2:(3D) Modeling of NHDC, Thaumatin, and Aspartame Activation of the Human Sweet Taste Receptor

[0124] This example describes modeling the activation of the human sweet taste receptor by a combination of the sweetener compounds NHDC, thaumatin, and aspartame.

[0125] Method: Based on the above techniques, a model based on the Hill equation was generated to model the combination of aspartame, NHDC, and thaumatin. The experimental data included 1152 data points representing 12 concentrations of aspartame, 8 concentrations of NHDC, and 12 concentrations of thaumatin. In this example, the Hill equation most closely approximated the experimental data, as described below with reference to Figure 6 stated.

[0126] Results: Figure 6 Comparison of experimental and model results for 12 concentrations of thaumatin is shown, where for each concentration of thaumatin, the concentration of NHDC varied from 0 mM to 1 mM and the concentration of aspartame varied from 0 mM to 10 mM. In this case, the Hill equation approximated the experimental data with the lowest error rate. The coefficients were as follows: a x = 0, a y = 0, a z = 0.06, a xy = 3, a yz = 0.7, a xz = 1, a xyz = 0 and n = 0.55, which generated a sigmoid function represented by the following equation

[0127]

[0128] Example 3 :(2D) Plots of Compound Synergism of Binary Combinations of NHDC, Thaumatin, and Aspartame

[0129] This example describes: plotting the compound synergism between NHDC and thaumatin (as Figure 7A shown) using the model generated according to the above techniques; plotting the compound synergism between aspartame and thaumatin (as Figure 7B shown) using the model generated according to the above techniques, and plotting the compound synergism between aspartame and NHDC (as Figure 7C shown) using the model generated according to the above techniques.

[0130] Figures 7A to 7CEach of them shows a plot of the fitted response L(X&Y) of a mixture of two compounds minus the fitted individual responses of each component of the mixture (i.e., L(X) and L(Y) and L(Z)) (where X and Y are binary combinations of aspartame, NHDC, and thaumatin). The 2D synergy by subtraction is represented by the formula: Max(L(X&Y)-L(X)-L(Y), 0).

[0131] As in the previous plot above, this difference is plotted as a function of the concentrations of aspartame, NHDC, and thaumatin. The aspartame tick marks labeled 2 to 12 represent concentrations of 0 (mM), 0.006 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM), 3 (mM), 6 (mM), 10 (mM) respectively. The NHDC tick marks labeled 10 to 90 represent concentrations of 0 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM) respectively. The thaumatin tick marks labeled 2 to 12 represent concentrations of 0 (μM), 0.06 (μM), 0.1 (μM), 0.3 (μM), 0.6 (μM), 1 (μM), 3 (μM), 6 (μM), 10 (μM), 30 (μM), 60 (μM), 100 (μM). The range of the luminescence (L) values is from 0 to 1.

[0132] Results: Figure 7A Showed little synergy between NHDC and thaumatin. Figure 7B and Figure 7C Showed high synergy between aspartame and thaumatin and between aspartame and NHDC respectively.

[0133] Example 4 :(3D) plot of compound synergy of a mixture of NHDC, thaumatin, and aspartame

[0134] This example describes using the model generated according to the above technique to plot the compound synergy between NHDC, thaumatin, and aspartame as Figure 8 shown.

[0135] Figure 8 Shows a plot of the fitted response L(X&Y&Z) of a mixture of three compounds minus the fitted individual responses of each component of the mixture (i.e., L(X) and L(Y) and L(Z)) (where X is aspartame, Y is NHDC, and Z is thaumatin). The 3D synergy by subtraction is represented by the formula: Max(L(X&Y&Z)-L(X)-L(Y)-L(Z), 0).

[0136] As in the previous figures above, in each panel representing a fixed amount of thaumatin, the difference was plotted as a function of aspartame concentration and NHDC concentration. The aspartame tick marks labeled 2 to 12 represent concentrations of 0 (mM), 0.006 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM), 3 (mM), 6 (mM), 10 (mM), respectively. The NHDC tick marks labeled 10 to 90 represent concentrations of 0 (mM), 0.01 (mM), 0.03 (mM), 0.06 (mM), 0.1 (mM), 0.3 (mM), 0.6 (mM), 1 (mM), respectively. The thaumatin panels 0 to 90 represent concentrations of 0 (μM), 0.06 (μM), 0.1 (μM), 0.3 (μM), 0.6 (μM), 1 (μM), 3 (μM), 6 (μM), 10 (μM), 30 (μM), 60 (μM), 100 (μM). The luminescence (L) values range from 0 to 1.

[0137] Results: Figure 8 Values where the synergy reaches 0.5 are shown.

[0138] Example 5 : Synergistic effects observed in various combinations of sweeteners

[0139] This example describes the increase in sweet taste receptor signals calculated for the following sweeteners: aspartame, sucrose, sucralose, rebaudioside A, rebaudioside D, thaumatin, NHDC, and S819, which are present in binary mixtures in various combinations as shown in Table 1.

[0140] Table 1. Synergistic effects observed in binary compound mixtures

[0141]

[0142] The synergy between binary mixtures of sweeteners was calculated as follows: L(X&Y) / (L(X)+L(Y)). When both compounds are present in the binary mixtures as shown in Table 1, the increase in the sweetness signal ranges from 25% to 50%.

[0143] Example 6 : 3D reconstruction based on 2D projections of the activation of the human sweet taste receptor by NHDC, rebaudioside A, and S819

[0144] This example describes modeling the activation of human sweet taste receptors through a combination of NHDC, REB A, and S819 based on initial data including three XY planes, XZ planes, and YZ planes, where X is NHDC, Y is REB A, and Z is S819.

[0145] Method: Figure 9A The original 2D models of the mixtures of NHDC and S819, NHDC and REB A, and S819 and REB A, respectively, and the concentration of each sweetener in the sweetener are shown. Figure 9B Shows from Figure 9A The 3D model reconstructed from the 2D model in. Based on Figure 9A The initial data in generates a model using Figure 9B The Hill equation and independent variables listed in. The Hill sigmoid function fitting gives the following parameters: a = 5.36, b = 9.55, c = 6.38, d = 39.43, e = 107.35, f = -79.31, g = 81206, h = 0.57.

[0146] The subject matter disclosed above should be considered illustrative and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of the present disclosure. Accordingly, to the maximum extent permitted by law, the scope of the present disclosure will be determined by the broadest permissible interpretation of the appended claims and their equivalents, and should not be limited or restricted by the foregoing detailed description. Although various embodiments of the present disclosure have been described, it will be apparent to those of ordinary skill in the art that there can be more embodiments within the scope of the present disclosure. Accordingly, the present disclosure is not limited except as defined by the appended claims and their equivalents.

Claims

1. A computer-implemented method of a generative model, the model being configured to provide data for determining synergism among multiple compounds in a mixture applied to a human taste receptor, the method comprising: Receiving, by a processor, a first data set including a plurality of data points, each data point including a concentration level of each compound in a mixture of multiple compounds; Receiving, by the processor, a second data set, the second data set including a response level of a human taste receptor determined for each data point from the plurality of data points; And Generating, by the processor, a model of the response level of the human taste receptor based on the first data set and the second data set by the following steps: Determining, by the processor, a mapping between each data point from the plurality of data points and the response level of the human taste receptor determined for each data point from the plurality of data points; Determining, by the processor, a first function of the concentration level of each compound in the mixture, the function having one or more unknown coefficients; And Determining the one or more unknown coefficients by fitting the first function to the mapping between each data point from the plurality of data points and the response level of the human taste receptor determined for each data point from the plurality of data points, whereby the processor determines a second function of the concentration level of each compound in the mixture, the second function having the same form as the first function.

2. The method according to claim 1, the method further comprising: Displaying, on a user device, data associated with the model to a user.

3. The method according to claim 1, wherein the human taste receptor is the sweet taste receptor T1R2 / T1R3, and wherein each compound in the mixture is a sweet compound.

4. The method according to claim 1, wherein the first function is a sigmoid function.

5. The method according to claim 4, wherein the sigmoid function is based on a logistic function, a trigonometric function, or a Hill equation.

6. The method according to claim 1, wherein each compound in the mixture is selected from the group consisting of sugars, mogrosides, sweet amino acids, polyols, artificial sweeteners, natural sweeteners, and sweet proteins.

7. The method according to claim 1, wherein the mixture contains a first sweet compound, a second sweet compound, and a third sweet compound.

8. The method according to claim 7, wherein each of the first sweet compound, the second sweet compound, and the third sweet compound is selected from aspartame, sucrose, sucralose, rebaudioside A, rebaudioside D, rebaudioside M, thaumatin, neohesperidin, and S819 [1-((1H-pyrrol-2-yl)methyl)-3-(4-isopropoxyphenyl)thiourea].

9. A computer-implemented method of preparing a synergistic sweetener composition using a sweet taste receptor response model, the method comprising: Receiving, by a processor, a plurality of data points associated with multiple sweeteners, each data point indicating a concentration level of a corresponding sweetener among the multiple sweeteners; Determining, by the processor and using the sweet taste receptor response model, a synergy between the multiple sweeteners based on a plurality of received data points, wherein the synergy is measured by an increase in the response level of the sweet taste receptor to the multiple sweeteners; and Prompting, by the processor and via a user interface, a user to prepare a synergistic sweetener composition, the synergistic sweetener composition including the multiple sweeteners at respective concentration levels indicated in the plurality of data points, such that the synergistic sweetener composition produces the synergy determined using the sweet taste receptor response model.

10. The method of claim 9, wherein the increase in the response level of the sweet taste receptor is equal to or greater than 25%.

11. The method of claim 9, wherein the multiple sweeteners include at least two sweeteners.

12. The method of claim 9, wherein the multiple sweeteners include at least three sweeteners.

13. The method of claim 9, the method further comprising: Displaying, by the processor and via the user interface, the response level of the sweet taste receptor.

14. The method of claim 13, wherein the response level of the sweet taste receptor is displayed in luminescence.

15. The method of claim 14, wherein the response level of the sweet taste receptor is displayed in a graph.

16. The method of claim 9, wherein the sweet taste receptor response model is trained by the steps of: Determining, by the processor, a mapping between sample data points of sample sweeteners and corresponding response levels of the sweet taste receptor, each sample data point including a concentration level of a corresponding sample sweetener; Determining, by the processor, a first function of the concentration levels of the sample sweeteners, the function having one or more unknown coefficients; and Determining the one or more unknown coefficients by fitting the first function to the mapping between the sample data points and the corresponding response levels of the sweet taste receptor, thereby determining, by the processor, a second function of the concentration levels of the sample sweeteners, the second function having the same form as the first function.

17. A method of preparing a synergistic sweetener composition based on a sweet taste receptor response model, the method comprising: Preparing a synergistic sweetener composition having two or more sweeteners at respective concentrations recommended by a sweet taste receptor model; wherein the model is generated based on a plurality of data points associated with a sweetener mixture, each data point including a concentration level of each sweetener in each sweetener mixture, and the sweet taste receptor data includes each response level of the sweet taste receptor T1R2 / T1R3 determined for each data point from the plurality of data points; and wherein the model is analyzed to determine a synergy between sweeteners in the sweetener mixture and the respective concentration levels of the sweeteners exhibiting the synergy in order to provide a recommendation for a synergistic composition.

18. A sweetener composition prepared by the method of claim 17.

19. A confectionery product, said confectionery product comprising the sweetener composition as claimed in claim 18.

20. The confectionery product as claimed in claim 19, wherein the confectionery product is selected from the group consisting of hard candy, gummy candy, mints, chewing gum, gelatin, chocolate, fudge, licorice, hard-coated candy, butterscotch, and toffee.

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