Use of electrical stimulation and chilling to enhance meat tenderness of heavy lambs
Bibliographic record
Abstract
Pouliot, E., Gariépy, C., Thériault, M. and Castonguay, F. W. 2014. Use of electrical stimulation and chilling to enhance meat tenderness of heavy lambs. Can. J. Anim. Sci. 94: 627-637. The aim of this study was to determine if electrical stimulation and chilling can modulate pre-rigor pH-temperature dynamics in order to enhance meat tenderness of heavy lambs produced in Quebec and determine if there is an optimum carcass temperature window to reach pH 6.0. A total of 128 heavy lambs (fasted body weight between 38 and 52 kg) were selected at the abattoir over 8 slaughter days (16 lambs d-1) and assigned to four processing treatments in a 2×2 factorial design: electrical stimulation (ES) or not (NES) and normal (NC) or slow (SC) chilling. Slow-chilled carcasses stayed warmer during the first 12 h postmortem (P<0.001), but reached the same temperature as NC ones thereafter. They also had a lower pH between 2 and 12 h (P<0.05). Stimulated carcasses had a lower pH than NES throughout the first 24 h postmortem (P<0.001), while ultimate pH was similar (P=0.738). Shear force values were improved (P<0.001) by both ES and aging as expected, although chilling had no effect (P=0.400). Stimulation×aging interactions for shear force values (P=0.019) and myofibrillar fragmentation index (P=0.097) indicate that aging began earlier following ES. Sarcomeres were longer for ES compared with NES carcasses (P<0.001) indicating that the latter were subject to cold shortening. Meat from NES carcasses was more prone to be tough, although an important part of NES carcasses provided tender meat. This illustrates the importance of individual variations on tenderness and the multiplicity of factors involved in its development. No optimal window was observed for temperature at pH 6.0.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".