Perception of Aqueous Ethanol Binary Mixtures Containing Alcohol-Relevant Taste and Chemesthetic Stimuli
Bibliographic record
Abstract
Ethanol is a complex stimulus that elicits multiple gustatory and chemesthetic sensations. Alcoholic beverages also contain other tastants that impact flavour. Here, we sought to characterize the binary interactions between ethanol and four stimuli representing the dominant orosensations elicited in alcoholic beverages: fructose (sweet), quinine (bitter), tartaric acid (sour) and aluminium sulphate (astringent). Female participants were screened for thermal taste status to determine whether the heightened orosensory responsiveness of thermal tasters (n = 21–22) compared to thermal non-tasters (n = 13–15) extends to these binary mixtures. Participants rated the intensity of five orosensations in binary solutions of ethanol (5%, 13%, 23%) and a tastant (low, medium, high). For each tastant, 3-way ANOVAs determined which factors impacted orosensory ratings. Burning/tingling increased as ethanol concentration increased in all four binary mixture types and was not impacted by the concentration of other stimuli. In contrast, bitterness increased with ethanol concentration, and decreased with increasing fructose concentration. Sourness tended to be reduced as ethanol concentration increased, although astringency intensity decreased with increasing concentration of fructose. Overall, thermal tasters tended to be more responsive than thermal non-tasters. These results provide insights into how the taste and chemesthetic profiles of alcoholic beverages across a wide range of ethanol concentrations can be manipulated by changing their composition.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".