Contribution of intramuscular connective tissue and its structural components on meat tenderness-revisited: a review
Bibliographic record
Abstract
The tenderness of meat influences consumers' perceptions of its quality. Meat tenderness is a key quality characteristic that influences consumer satisfaction, repeat purchases, and willingness to pay higher prices for meat. Muscle fibers, connective tissues, and adipocytes are the main structural components of meat that contribute to its tenderness and texture. In the present review, we have focused on the role of connective tissue and its components in meat tenderness, specifically perimysial intramuscular connective tissue (IMCT) and its concept as an immutable "background toughness." The collagen contribution to cooked meat toughness can be altered by animal diet, compensatory growth, slaughter age, aging, and cooking. As well, progressive thickening of the perimysium leads to a progressive increase in shear force values in beef, pork, chicken, and this may occur prior to adipocyte formation as cattle finish in feedlots. Conversely, adipocyte accumulation in the perimysium can decrease cooked meat shear force, suggesting that the contribution of IMCT to meat toughness is complex and driven by both collagen structure and content. This review provides a theoretical foundation of information to modify IMCT components to improve meat tenderness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.004 | 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".