Application of TCATA to examine variation in beer perception due to thermal taste status
Bibliographic record
Abstract
Thermal taste status (TTS) describes a phenotype whereby some individuals experience a thermally-induced taste on thermal stimulation of the tongue (thermal tasters; TTs) and some do not (thermal non-tasters; TnTs). TTs experience a range of orosensations elicited by aqueous solutions and some beverages more intensely than TnTs. Whether this extends throughout ingestion duration is unknown, despite the fact that the evolution of flavour on the palate is a key component of consumer acceptance of food/beverages. We sought to use temporal-check-all-that-apply (TCATA) to determine how beer perception varies with TTS. A secondary aim was to investigate the effects of serving temperature and a concurrent auditory cue on TCATA responses and how these may interact with TTS. Forty-one female participants (21 TTs, 20 TnTs) were trained to identify seven dominant sensations elicited by a de-alcoholized beer (astringent, bitter, carbonation, fruity/hops, malty, sour, sweet). Beer samples were served in duplicate at either 6 °C or 21 °C with or without a concurrent auditory cue consisting of a sound clip of effervescence. TTs cited astringent and bitter more frequently than TnTs (p(F) ≤ 0.01), and the area under the curve (AUC) was greater for TTs for several sensations (p(t) < 0.05). Samples served at 6 °C had higher carbonation citation frequencies (p(F) < 0.001) and AUC at 0–30 s (p(t) < 0.001) and 30.1–60 s (p(t) < 0.05) than the warmer samples, with responses for astringent following a same pattern. AUC for carbonation and astringent varied within the sound conditions at 0–30 s (p(t) < 0.05). Overall, these results show that the ‘taste’ advantage of TTs extends to beer, and that temporal methods are needed to more fully describe consumer variation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".