Protein Oxidation in Processed Meat: Mechanisms and Potential Implications on Human Health
Bibliographic record
Abstract
Processed meats represent a large percentage of muscle foods consumed in the western world. Various processing steps affect the physicochemical properties of the meat, compromise its nutritional components, or produce some compounds that are of health concern. Hence, the impact of oxidation on human health and the aging process and the influence of diet on these harmful reactions are of growing interest. Past decades have seen more focus on lipid oxidation, microbial deterioration, and pathogenicity, as well as production of carcinogenic compounds during meat processing. The oxidation of protein, which is a major component in meat systems, has received less attention. Protein oxidation has been defined as a covalent modification of protein induced either directly by reactive species or indirectly by reaction with secondary by-products of oxidative stress. Not only are these modifications critical for technological and sensory properties of muscle foods, they may have implications on human health and safety when consumed. Cooking, for example, has been observed to increase free radical generation while it also decreases the antioxidant protection systems in meat, both of which contribute to protein oxidation. Many other meat processing techniques, as well as other emerging technologies, may significantly affect protein oxidation and protein overall quality. This paper explores the current understanding of meat processing techniques and their possible effects on the status of protein oxidation and nutritional value, as well as their possible implications on human health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".