Sensory characterization during repeated ingestion of small‐molecular‐weight phenolic acids
Bibliographic record
Abstract
BACKGROUND: Characterization of the sensory properties of small-molecular-weight phenolic acids such as ferulic and vanillic acids has been limited. The objectives of this study were to characterize the sensory perceptions of these acids and the effects of their repeated consumption on sourness, bitterness and astringency. This knowledge will further the understanding of the impact of these acids on the sensory characteristics of foods in which they are typically consumed. RESULTS: Two time-intensity sensory evaluation experiments were conducted with nine trained panelists: a single-sip study and a sequential-sip study. Concentrations of phenolic acids typically found in whole grain bread were tested. For both experiments, vanillic acid was perceived to be significantly more sour than ferulic acid, and ferulic acid was perceived to be significantly more bitter than vanillic acid. Maximum sourness, bitterness and astringency intensities significantly increased with increasing molarity for both acids. During sequential sipping, astringency and bitterness intensity increased with each sip. Sourness, however, increased to sip 3 but did not significantly increase after that point. CONCLUSION: This research demonstrates that even small quantities of phenolic acids can be perceived as increasingly bitter and astringent with repeated exposures.
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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 | 0.001 |
| 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".