Measuring pH of Pork at Specific Temperatures Postmortem to Predict Quality Traits
Why this work is in the frame
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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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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 | 0.001 | 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 it