Sodium Chloride Suppresses the Bitterness of Protein Hydrolysates by Decreasing Hydrophobic Interactions
Bibliographic record
Abstract
The formation of bitter off-flavor is a long-existing issue during food protein hydrolysis. The aim of this study is to determine the mechanism of sodium chloride (NaCl) suppressing the bitterness of protein hydrolysates. In this study, the bitterness of egg white hydrolysate (EWH) and hen meat hydrolysate (HPH) was determined using an electronic tongue. The results showed that the bitterness intensity of quinine hydrochloride, EWH, and HPH was suppressed significantly by NaCl in a concentration-dependent manner (P < 0.05). The particle sizes, turbidity, zeta potentials, and surface hydrophobicity of EWH and HPH were also significantly decreased by NaCl at concentrations of 0.05, 0.1, 0.3, and 0.5 M (P < 0.05). These results indicated that adding NaCl at certain concentrations led to a salting-in effect, burying hydrophobic groups and decreasing the surface hydrophobicity of peptides, resulting in the decrease of bitterness. Using NaCl is an alternative, effective, and cheap strategy to suppress protein hydrolysate bitterness by decreasing hydrophobic interactions in food industry. PRACTICAL APPLICATION: NaCl can be used as an effective bitterness masker for food protein hydrolysates by decreasing hydrophobic interactions of peptides.
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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 | 0.000 |
| 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".