Antioxidant proteins and peptides to enhance the oxidative stability of meat and meat products: A comprehensive review
Bibliographic record
Abstract
Lipid oxidation is among the major flaw-grounding processes in meat and meat-based products that can affect interactions among lipids and proteins, leading to critically undesirable changes. Therefore, it is imperative to control lipid oxidation in meat allied products to enhance consumer acceptability. Moreover, lipid oxidation is somber dilemma visage by the meat processing industry, affects food constituents, leading to detrimental alterations that can impart the deleterious effects on human health upon consumption. Various synthetic (butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), and tertiary butylhydroquinone (TBHQ)) and natural antioxidants (vitamin C, vitamin A, tocopherols, especially vitamin E, flavonoids particularly quercetin, proteins, and peptides) as well as preservatives are employed to extend the storability of meat and resultants products; however, great consideration is paid to the utilization of natural antioxidants due to the harmful side effects imparted by synthetic counterparts. Recently, bioactive peptides are claimed to thwart lipid oxidation in meat and other products; in addition, these antioxidant peptides have also been reported to possess substantial health-promoting potential besides controlling oxidation. Therefore, the present review is intended to emphasize the sources, production methods, and applications of antioxidant proteins and peptides to control oxidative degradation in meat products and the potential health benefits of bioactive peptides. Furthermore, the techniques available for the extraction, characterization, and assessment of the antioxidant capability of bioactive peptides are discussed critically in this review.
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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 | 0.003 | 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) | 0.003 | 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".