Recent Advances in Protein Extraction Techniques for Meat Secondary Streams
Bibliographic record
Abstract
Meat secondary streams command low value along the meat value chain, with a significant portion of these exiting the food value chain and contributing to global food loss and waste. Valorizing these substantial secondary streams through the efficient extraction of high-biological-value proteins could translate into significant social, economic, and environmental benefits. Protein extraction from meat secondary streams offers a promising approach to enhance their nutritional and commercial value while supporting global food security initiatives. This approach could also help to distant these by-products from their original source, making them more appealing to consumers. The current review evaluates the protein content and valorization potential of meat secondary streams from various animal sources. It further provides a critical assessment of both traditional and emerging protein extraction techniques, highlighting their advantages, limitations, and applications. Existing knowledge gaps are also identified to guide future research. This review aligns the role of protein recovery technologies with the UN’s Sustainable Development Goal target 12.3, which seeks to halve global food waste by 2030.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".