Measuring pH of Pork at Specific Temperatures Postmortem to Predict Quality Traits
Bibliographic record
Abstract
The objective of this study was to explore if pH measurements collected at specific temperatures (39–31°C) during the early postmortem period can predict pork quality with greater accuracy than pH assessments collected at fixed-time intervals (45 min and 24 h postmortem). To achieve this, pH, temperature, and meat quality data were collected from the longissimus thoracis from the left sides of 558 commercially sourced pork carcasses, including 296 barrows and 262 gilts. The results showed that pH values at 45 min and 24 h postmortem were not significantly correlated (P > .05). Furthermore, pH values at 45 min and 24 h postmortem were weakly correlated with pH at 39°C to 31°C (r ≤ 0.27; P < .05). There was a strong positive correlation (0.73 ≤ r ≤ 0.99; P < .05) among pH measurements at 39°C to 31°C, indicating consistency in pH across specific temperatures. Stepwise regression analysis identified multiple significant predictors for each quality trait examined. Specifically, pH at 35°C explained 11.5% of the variability in L* , pH at 36°C explained 27.5% of the variability in purge loss, and pH at 32°C explained 12.7% of the variability in slice shear force. Our findings show that pH collected at specific temperatures may be a good predictor of important pork quality attributes and could be used for research purposes and incorporated as selection objectives for genetic selection programs.
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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) | 0.000 | 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) | 0.000 | 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 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".