Changes in tenderness and cathepsins activity during post mortem ageing of yak meat
Bibliographic record
Abstract
Tian, J.-C., Han, L., Yu, Q.-L., Shi, X.-X. and Wang, W.-T. 2013. Changes in tenderness and cathepsins activity during post mortem ageing of yak meat. Can. J. Anim. Sci. 93: 321–328. Very little research has been conducted on yak meat tenderization. In this study we investigated the changes in physical characteristics (e.g., pH, water-holding capacity, texture profile analysis, shear force) and cathepsins L, B and H activities in the tenderization process. These traits were quantified in longissimus dorsi muscle from 10 yaks during 192 h post mortem. Samples were aged at 4°C for 0, 12, 24, 36, 48, 72, 120, 168 and 192 h. pH decreased (P<0.05) from 6.84 to 5.54 in the first 72 h and did not change significantly during the next 120 h. Water-holding capacity showed an overall decreasing trend (P<0.05). Shear force decreased? (P<0.05) and myofibrillar fragmentation index increased? (P<0.05), and it was concluded that ageing can improve yak meat tenderness. Our results on texture profile analysis showed a decrease in hardness (P<0.05), springiness (P<0.05) and chewiness (P<0.05), reflected in a progressive softening during ageing (P<0.05). Cathepsins L, B and H activity showed an increased trend (P<0.05). In conclusion, our results show potential roles for cathepsins L, B and H in the tenderization process. This study provides further insights into the tenderization process of yak meat, which may ultimately be used for the advantageous manipulation of the process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".