Functional and Biological Properties of Peptides Obtained by Enzymatic Hydrolysis of Whey Proteins
Bibliographic record
Abstract
The study of peptides released by enzymatic hydrolysis of whey proteins has been initially focusing on improving their functional properties in food model systems. Our first study showed that peptides 41 to 60 and 21 to 40 from β-lactoglobulin (β-LG) were responsible for improved emulsifying properties of a tryptic hydrolysate of whey protein concentrate (WPC). Further work showed that adding negatively charged peptides from tryptic hydrolysates of WPC could prevent phase separation of dairy-based concentrated liquid infant formula, as a replacement for carrageenan. Hydrolysis of whey proteins using a bacterial enzyme was also successful in improving heat stability of whey proteins in an acidic beverage. Some tryptic peptides demonstrated improvement in the heat stability and in modifying thermal aggregation of whey proteins. Recent research has shown that whey peptides could trigger some physiological functions. Within the scope of this research our work has led to the development of a whey protein enzymatic hydrolysate that has demonstrated antihypertensive properties when orally administered to spontaneously hypertensive rats and human subjects. Our work then focused on the fractionation of hydrolysates by nanofiltration to prepare specific peptidic fractions; however, peptide/peptide and peptide/protein interactions impaired membrane selectivity. The study of those interactions has lead to the demonstration of the occurrence of interactions between β-LG and its hydrophobic fragment 102–105 (opioid peptide), which probably binds in the central cavity of the protein. This latest result suggests that β-LG could be used as a carrier for the protection of bioactive peptides from gastric digestion. Our work therefore has shown that the enzymatic hydrolysis of whey proteins is not only improving their functional properties, but it is also providing powerful technology in the exploitation of their biological properties for functional foods and nutraceutical applications.
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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".