Taste Sensitivity and Taste Preference Measures Are Correlated in Healthy Young Adults
Bibliographic record
Abstract
Taste is fundamentally important for food selection. Although measures of taste sensitivity and preference have been refined over several decades, it remains largely unknown how these measures relate to each other and to food preferences. The objectives of this study were to examine, in healthy adults (age 24.6 ± 0.6 years, n = 49), 1) correlations among measures of taste sensitivity, including detection threshold (DT) and suprathreshold sensitivity (ST), and taste preference (PR) within sweet, salt, sour, umami, and [...] fat tastes; and 2) underlying associations [...] among DT, ST, and PR measurements using principal component analysis. DTs and STs were negatively correlated within each taste modality. Salt, sweet, and umami DTs and STs were positively and negatively correlated with PRs, respectively. No correlations were observed between sour and fat DTs, STs, and PRs. Two principal components accounted for 41.9% of the variance and produced 3 clear clusters consisting of DTs, STs, or PRs from each taste modality. Sweet PR and fat ST deviated from the clusters and may, therefore, be driven by different factors. No associations were observed between measured PR and ST with self-reported food PRs. Overall, this study provides evidence that higher sensitivities only to salt, sweet, or umami taste are associated with a decrease in the PR for these tastes. These findings demonstrate the importance of investigating taste sensitivity together with PR to gain a more complete understanding of the determinants of food selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".