Techniques for postmortem tenderisation in meat processing: effectiveness, application and possible mechanisms
Bibliographic record
Abstract
Abstract Developing efficient and promising tenderising techniques for postmortem meat is a heavily researched topic among meat scientists as consumers are willing to pay more for guaranteed tender meat. However, emerging tenderising techniques are not broadly used in the meat industry and, to some degree, are controversial due to lack of theoretical support. Thus, understanding the mechanisms involved in postmortem tenderisation is essential. This article first provides an overview of the relationship of ageing tenderisation and calpain system, as well as proteomics applied to identify protein biomarkers characterizing tenderness. In general, the ageing tenderisation is mediated by multiple biochemical activities, and it can exhibit better palatability and commercial benefit by combining other interventions. The calpain system plays a key role in ageing tenderisation functions by rupturing myofibrils and regulating proteolysis, glycolysis, apoptosis and metabolic modification. Additionally, tenderising techniques from different aspects including exogenous enzymes, chemistry, physics and the combined methods are discussed in depth. Particularly, innovation of home cooking could be recommended to prepare relatively tender meat due to its convenience and ease of operation by consumers. Furthermore, the combined interventions provide better performance in controlled tenderness. Finally, future trends in developing new tenderising techniques, and applied consideration in the meat processing industry are proposed in order to improve meat quality with higher economical value. Graphical abstract
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".