Methods for Removing Bitterness in Functional Foods and Nutraceuticals
Bibliographic record
Abstract
Bitterness is the most complex and the least understood of the five basic tastes. Numerous compounds naturally present in food are responsible for the generation of bitter taste. Although a small amount of bitterness is considered desirable in some instances, for the majority of food products it remains unacceptable to consumers. This creates a challenge to promote health benefits of certain foods, especially functional foods where the added active ingredients are mostly bitter. Two options exist to ameliorate this problem; 1) the removal or reduction of the bitter compound(s), or 2) masking the bitterness through the addition of other ingredients. Unfortunately, both of these strategies often result in a less healthful product, therefore, care must be taken to ensure a complete functionality of the targeted compounds within functional foods. This chapter reviews recent studies that focus on reducing or masking bitterness in foods including functional foods as well as recent research related to bitter blockers.
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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 | 0.000 | 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 | 0.000 |
| 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".