Effect of protein‐flavour binding on flavour delivery and protein functional properties: A special emphasis on plant‐based proteins
Bibliographic record
Abstract
Abstract As the loss of free volatile compounds in aqueous protein systems is known to greatly influence the quality, and therefore the consumer acceptability, of protein‐containing foods, examination of the ability of different proteins to react with volatile flavour compounds as well as the nature of the interaction are of great interest to flavour chemists. It is generally believed that the affinity of flavour to proteins is a multi‐factor function related to protein source, protein conformation and stereochemistry of the flavour compound. However, the nature of protein‐flavour interactions has not been explicit. Less focus has been put on plant‐based proteins in comparison to animal proteins. With an increase in the use of plant‐based proteins in food systems, this article provides a fundamental review of the impact protein‐flavour interactions on flavour retention and release with a special emphasis on plant protein materials. Flavour‐food matrix interactions have been examined with a focus on how they influence flavour perception. Current knowledge on methodologies involved in protein‐flavour binding studies, binding mechanisms and factors affecting the interaction have been discussed. The implication of protein‐flavour interaction on protein functionality specifically protein thermal gelation properties has also been considered. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 |
| 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".