Enhancing the Biological Activities of Food Protein-Derived Peptides Using Non-Thermal Technologies: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it