Variation in orosensation and liking of sampled foods with thermal tasting phenotype
Bibliographic record
Abstract
Flavour is a key driver of liking, purchase behaviour and consumption of food and beverages. Determining how individuals differ in their perception of flavour is important to fully understanding dietary choices and habitual diet-related health outcomes. Thermal tasting—the capacity to experience a phantom taste when small areas of the tongue are rapidly heated or cooled—associates with greater orosensory acuity for tastants in aqueous solutions. This study sought to extend this finding and establish whether thermal-taster status also associates with the perceived intensities of oral sensations elicited by sampled food. Twenty-five thermal tasters (TTs) and 19 thermal non-tasters (TnTs) scored liking (generalized degree of liking scale) and the intensity (generalized visual analogue scale) of the dominant orosensations elicited by 20 food and beverage items in duplicate using a randomized complete block design. Multiple analysis of variance (MANOVA) showed that overall, TTs rated the intensity of orosensory food groups higher than did TnTs, although this was significant only for foods that were predominantly bitter-eliciting (ANOVA). Overall liking scores approached significance (MANOVA) but differed between TTs and TnTs only for the grainy orosensory food grouping (ANOVA). These findings are discussed in the context of diet-related health outcomes and directions for further research concerned with taste phenotypes, flavour perception and consumption behaviours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".