Taste Receptor Phenotyping for Personalized Wine Selection
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Solution Overview
Problem
There is a need for improved methods to predict and characterize individual wine tasting preferences based on the phenotypic expression of T2R and/or T1R chemosensory receptors, and to select wines that align with these preferences.
Innovation Solution
A method involving determining the phenotypic expression of T2Rs and/or T1Rs through tests that stimulate these receptors with agonists and detect responses, followed by calculating wine bin scores to predict preferred wine types, using a system that includes agonists like caffeine, denatonium, and quinine, and reagents like Griess reagents for interaction detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phenotypic expression of T2R and/or T1R receptors is determined through stimulation tests, then wine tasting preferences can be predicted, but the complexity of the testing system increases
Solution Approach 1:
The testing system is segmented into separate functional modules: agonist stimulation components, receptor interaction detection systems, and data analysis algorithms. This allows the complex prediction process to be broken down into manageable, independent test components that can be administered separately and combined for final prediction.
Solution Approach 2:
The patent uses intermediary substances (agonists like caffeine, quinine, and denatonium) that mediate between the T2R/T1R receptors and the detection system. These intermediaries bind to specific receptors and produce measurable responses, enabling indirect but accurate measurement of receptor phenotypic expression without requiring direct observation of receptor activity.
2Reliability
If multiple agonists and reagents are used to stimulate and detect receptor responses, then the reliability of wine preference prediction improves, but the quantity of substances required increases
Solution Approach 1:
The testing system employs multiple agonists (caffeine for T2R38, quinine for T2R4, denatonium for T2R16) that can collectively assess different T2R receptor subtypes. This multi-functional approach allows a single test battery to evaluate various bitter taste receptors and sweet taste receptors (T1R), providing comprehensive wine preference prediction without requiring separate specialized tests for each receptor type.
Solution Approach 2:
The system varies parameters such as agonist concentration, exposure time, and detection thresholds to optimize the response signal while minimizing the quantity of substances needed. By adjusting these parameters, the system achieves reliable detection of receptor phenotypic expression using small, controlled amounts of each agonist and reagent.
3Adaptability or versatility
If wine bin scores are calculated based on receptor expression, then personalized wine recommendations can be provided, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary calculations by pre-establishing relationships between T2R/T1R receptor phenotypes and wine bin score ranges. During actual testing, the measured receptor expression levels are quickly mapped to pre-determined score ranges, avoiding complex real-time calculations. This allows rapid generation of personalized wine recommendations while maintaining accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where wine bin scores are calculated based on receptor expression data, and these scores provide immediate feedback for personalized wine selection. The feedback loop allows the system to refine predictions and provide tailored recommendations efficiently, reducing the overall time required for personalized wine matching.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively predicts wine preferences by correlating receptor expression with taste perceptions, enabling personalized wine recommendations and aiding in wine selection based on individual taste profiles.
Implementation Method 1
tests that stimulate these receptors with agonists and detect responses
Implementation Method 2
reagents like Griess reagents for interaction detection
Data Source
AI summary
A method of measuring phenotypic expression of T2Rs and/or TIRs for a subject to predict taste preferences for wines may include stimulating T2Rs and/or TIRs of a subject with agonists and detecting products released as a result of stimulation of the T2Rs and/or T1Rs. A method may include recording discerned levels of taste perception after stimulation with agonists and correlating the discerned level to phenotypic expression, which may be used to predict taste preferences for wine profiles. A method may include calculating wine bin scores for predicted wine preferences, from phenotypic expression of T2Rs and/or T1Rs, obtaining taste preference information, and determining the subject's taste preference scores for wines administered to the subject.