Bibliographic record
Abstract
Certain amino acid sequences encoded within the primary structure of food proteins impart no bitter taste when present as structural components of native proteins. However, upon release from the native (mother) protein during chemical or enzymatic hydrolysis, the free peptide has a bitter taste when ingested orally. Bitterness intensity of the peptide is related to ability to interact with bitter taste receptors (T2Rs) in the oral cavity. Ability of a peptide to elicit bitter taste is dependent on amino acid composition, specifically an abundance of hydrophobic residues. Arrangements of the amino acids also influence bitterness intensity because basic residues at the N-terminal and hydrophobic or bulky residues at the C-terminal are preferred. Most influential amino acids for increased bitterness intensity are proline, valine, leucine, phenylalanine and tryptophan. However, spatial structure is also important, especially short distances between N- or C-terminal C=O group and hydrophobic groups promote stronger bitterness intensity. In this chapter, other structural properties required for bitterness intensity as well as methods for decreasing or removing peptide bitterness are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".