Enhancing the Biological Activities of Food Protein-Derived Peptides Using Non-Thermal Technologies: A Review
Bibliographic record
Abstract
Bioactive peptides (BPs) derived from animal and plant proteins are important food functional ingredients with many promising health-promoting properties. In the food industry, enzymatic hydrolysis is the most common technique employed for the liberation of BPs from proteins in which conventional heat treatment is used as pre-treatment to enhance hydrolytic action. In recent years, application of non-thermal food processing technologies such as ultrasound (US), high-pressure processing (HPP), and pulsed electric field (PEF) as pre-treatment methods has gained considerable research attention owing to the enhancement in yield and bioactivity of resulting peptides. This review provides an overview of bioactivities of peptides obtained from animal and plant proteins and an insight into the impact of US, HPP, and PEF as non-thermal treatment prior to enzymolysis on the generation of food-derived BPs and resulting bioactivities. US, HPP, and PEF were reported to improve antioxidant, angiotensin-converting enzyme (ACE)-inhibitory, antimicrobial, and antidiabetic properties of the food-derived BPs. The primary modes of action are due to conformational changes of food proteins caused by US, HPP, and PEF, improving the susceptibility of proteins to protease cleavage and subsequent proteolysis. However, the use of other non-thermal techniques such as cold plasma, radiofrequency electric field, dense phase carbon dioxide, and oscillating magnetic fields has not been examined in the generation of BPs from food proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".