Changes in Meat Quality and Genetic Parameter Estimation between Fresh and Frozen-Thawed Samples in Crossbred Commercial Pigs
Bibliographic record
Abstract
ObjectivesThe objectives were: (1) to estimate heritability of important meat quality traits in fresh and frozen-thawed pork; (2) to estimate phenotypic, genetic and environmental correlations of meat quality measurements between and within fresh and frozen-thawed pork; and (3) to analyze the effect of crude fat content on meat quality changes from fresh to frozen-thawed pork in commercial crossbred pigs.Materials and MethodsData from 2,027 crossbred commercial pigs including pork color (L*, a*, and b*), intramuscular pH and drip loss measurements performed on m. longissimus dorsi when fresh and when thawed after frozen storage were used to estimate the genetic parameters for these meat quality characteristics using univariate and bivariate animal models in ASReml. The differences (∆) in the meat quality measurements between fresh and frozen-thawed samples were tested by paired t test (dependent t test) using SAS 9.3 (SAS Inst. Inc., Cary, NC) with a significance level of P < 0.0001.ResultsAll meat quality traits changed significantly (P < 0.0001) from fresh to frozen-thawed status and intramuscular crude fat content exerted a heteroscedastic effect (P < 0.001) on the magnitude of this change. Meat quality measurements of fresh pork were all moderately to highly heritable (h2 = 0.212 to 0.436), with heritability estimates for L* (h2 = 0.434 fresh samples, versus 0.244 frozen-thawed), pH (h2 = 0.221 fresh, 0.183 frozen-thawed) and drip loss (h2 = 0.333 fresh, 0.139 frozen-thawed) were higher when estimated using fresh rather than frozen-thawed data, while heritability estimates of a* (h2 = 0.326 fresh, 0.427 frozen-thawed) and b* (h2 = 0.212 fresh, 0.242 frozen-thawed) were comparable for fresh and frozen-thawed data when their standard errors were considered. Genetic correlations for L*, a*, b* and pH between fresh and frozen-thawed meat were high (rA = 0.665, 0.816, 0.442, and 0.853 for L*, a*, b* and pH, respectively) while genetic correlation for drip loss was moderate (rA = 0.236). Genetic correlations estimated within fresh and frozen-thawed measurements specifically between L* and a* (rA = –0.240 in fresh and –0.440 in frozen-thawed), L* and b* (rA = 0.760 in fresh and 0.483 in frozen-thawed), a* and b* (rA = 0.357 in fresh and 0.450 in frozen-thawed) were all moderate to high, but genetic correlations between a* and pH (rA = 0.082), b* and drip loss (rA = 0.128) in frozen-thawed samples were low. Genetic correlation between pH and drip loss estimated in frozen-thawed samples (rA = –0.096) was smaller than that in fresh samples (rA = –0.187).ConclusionWe concluded that while either fresh or frozen-thawed pork samples can be used for L*, a*, and b* measurements, pH and drip loss should be measured using fresh rather than frozen-thawed pork for genetic selection.
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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 | 0.002 | 0.002 |
| 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 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".