The Influence of Vacuum Packaging of Hot-Boned Lamb at Early Postmortem Time on Meat Quality during Postmortem Chilled Storage
Bibliographic record
Abstract
To evaluate the effects of early postmortem vacuum packaging (VP) on meat quality during postmortem chilled storage, hot-boned lamb was vacuum-packaged at 1, 6, 12, 24, and 48 h postmortem and stored around 2°C until 168 h postmortem, with lamb packaged in plastic wrap as the control (aerobic packaging). Intramuscular pH decline was delayed when lamb was vacuum packaged at 1, 6, and 12 h postmortem (p<0.05). The lamb vacuum-packaged at 1 h postmortem (VP-1h group) had significantly lower shear force values and purge losses accompanied by lower free thiol group values than other treatments during postmortem storage and was also higher in extractable calpain-1 activity by 6 h postmortem (p<0.05). Free thiol group concentrations were significantly higher after VP at 6 and 12 h postmortem (p<0.05). Packaging lamb under vacuum very early postmortem produced the lowest shear force and purge loss, likely by slowing heat loss and muscle temperature decline, implying that lamb quality is improved by VP when applied very early postmortem. This was at the expense of protein oxidation, which was unrelated to other meat quality measurements, most likely because potential contracture during hot boning confounded its impact. Further research is required to understand the implications of the interaction between protein oxidation, VP, and hot boning on the acceptability of lamb.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".