Nutrigenomics of Taste – Impact on Food Preferences and Food Production
Bibliographic record
Abstract
Food preferences are influenced by a number of factors such as personal experiences, cultural adaptations and perceived health benefits. Taste, however, is the most important determinant of how much a food is liked or disliked. Based on the response to bitter-tasting compounds such as phenylthiocarbamide (PTC) or 6-n-propylthiouracil (PROP), individuals can be classified as supertasters, tasters or nontasters. Sensitivity to bitter-tasting compounds is a genetic trait that has been recognized for more than 70 years. Genetic differences in bitter taste perception may account for individual differences in food preferences. Other factors such as age, sex and ethnicity may also modify the response to bitter-tasting compounds. There are several members of the TAS2R receptor gene family that encode taste receptors on the tongue, and genetic polymorphisms of TAS2R38 have been associated with marked differences in the perception of PTC and PROP. However, the association between TAS2R38 genotypes and aversion to bitter-tasting foods is not clear. Single nucleotide polymorphisms in other taste receptor genes have recently been identified, but their role in bitter taste perception is not known. Establishing a genetic basis for food likes/dislikes may explain, in part, some of the inconsistencies among epidemiologic studies relating diet to risk of chronic diseases. Identifying populations with preferences for particular flavors or foods may lead to the development of novel food products targeted to specific genotypes or ethnic populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".