Optimizing the Orosensory Properties of Model Functional Beverages: The Influence of Novel Sweeteners, Odorants, Bitter Blockers, and Their Mixtures on (+)‐Catechin
Bibliographic record
Abstract
UNLABELLED: The use of flavor-modifying strategies are important to improving the sensory profile of some excessively bitter and astringent functional ingredients, such as (+)-catechin (CAT). Two bitter blockers (ß-cyclodextrin [CYCLO], homoeriodictyol sodium salt [HED]), two sweeteners (sucrose [SUC], rebaudioside A [REB]), and two odorants (vanillin [VAN], black tea aroma [TEA]) were assessed for their efficacy at modifying the bitterness and astringency of CAT in model aqueous solutions. The intensity of oral sensations elicited by CAT was determined in duplicate in binary, ternary, and quaternary mixtures of these stimuli by a trained panel (n = 15) using a 15 cm visual analogue scale. Overall, bitterness and astringency were most effectively reduced by ternary solutions containing CYCLO + REB or CYCLO + SUC (68%, 60%, and 45%, 43% for bitterness and astringency, respectively). Odorants were not effective at modifying the bitterness or astringency of CAT. We conclude that the use of select bitter blockers and sweeteners may be of value in optimizing the flavor and acceptance of functional food and beverages fortified with phenolic compounds. PRACTICAL APPLICATION: (+)-Catechin is a bitter-tasting plant-derived health-promoting phenolic compound of interest to functional food and beverage manufacturers. We investigated the efficacy of bitter blockers, plant-based sweeteners, and odorants in decreasing the bitterness and astringency elicited by (+)-catechin. Some of these additives, both alone and in combination, reduced bitterness and astringency, and may therefore assist in optimizing the flavor and consumer acceptance of some phenolic-based functional foods and beverages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".