371 The effect of feeding a high-fiber and -fat diet on pig meat quality
Bibliographic record
Abstract
The application of strategic finishing diets to reduce the muscle glycogen content at slaughter is considered a method to improve pork meat quality. The objective of this study was to assess the effects of feeding pigs with high-fat and low-digestible-carbohydrate diets in the late finishing period (last 29 d) on meat quality. Four dietary treatments were applied: 1) a control diet (CON; 67.2% corn, 15.5% soybean meal, and 15.0% hard wheat), 2) a high-fat and -fiber diet (HFF; 38.0% corn, 17.9% soybean meal, 15.0% hard wheat, 20.0% oat hulls, and 6.9% fat), 3) a blend of 50:50% CON and HFF diets, and 4) the CON diet for 11 d and then the HFF diet for 11 d after a 7-d diet transition. A total of 160 pigs (119.2 ± 6.2 kg), balanced by gender (barrows and gilts), were raised in 20 pens of 8 pigs each (5 pens per treatment). At the end of the finishing period, pigs were weighed and fasted between 16 and 19 h before slaughter. The whole-blood lactate level was assessed at exsanguination using a hand-held Lactate Scout Analyzer (EKF Diagnostic GmbH, Magdeburg, Germany). On the slaughter day, HCW was recorded and pH was measured at 1 h postmortem in the LM and semimembranous (SM) muscles. Meat quality was also assessed at 24 h postmortem by measuring pHu in the LM, SM, and adductor muscles; color (Minolta L*, a*, and b*) in the LM and SM muscle; and EZ-drip loss in the LM. Analyses were performed using the MIXED procedure of SAS, with the pen as the experimental unit, using treatment, sex, and their interactions in the model. Results indicated neither differences between treatments nor an interactive effect (P > 0.05) between sex and treatment for final live weight and any of the postmortem measured variables. These results may indicate either the inefficiency of the applied dietary treatments for meat quality improvement or, based on the low exsanguination blood lactate values (mean and confidence interval of 5.2 mmol/L and 5.8–6.7 mmol/L, respectively), that the stress imposed on pigs before slaughter in this study might not have been sufficiently high to result in meat quality variation.
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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) | 0.001 | 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 | 0.001 |
| 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".