Biochemical Properties of Natural Actomyosin Extracted from Normal and Pale, Soft, and Exudative Pork Loin After Frozen Storage
Bibliographic record
Abstract
ABSTRACT: The studies of natural actomyosin (NAM) extracted from normal and pale, soft, and exudative (PSE) pork longissimus muscle stored at ‐20°C for up to 6 mo, revealed that the surface hydrophobicity (S 0 ‐ANS) of NAM from PSE pork was significantly ( P < 0.05) higher than that from normal pork indicating greater conformational changes in proteins from PSE meat that resulted in the exposure of hydrophobic aromatic amino acid residues on the surface. Also, the S 0 ‐ANS of NAM was a function of storage time. The equations were as follows: S 0 ‐ANS = 16.9 × storage mo + 123 for normal and S 0 ‐ANS = 17.5 × storage mo + 164 for PSE. NAM from frozen normal pork had lower α‐helical content than comparable fresh pork. With extended frozen storage, viscosity of NAM from PSE meat was lower than that from normal pork. The sulfhydryl and disulfide contents were unchanged. Electrophoresis revealed an extra 95 to 100 kDa band from PSE meat NAM, possibly from α‐actinin or myosin degradation. Water‐binding capacity (WBC) of normal and PSE meat decreased with increasing storage time; however, there were only minor changes in thaw loss. The decrease of WBC of pork meat partially can be explained by the increase of S 0 ‐ANS observed for the NAM. These results suggest that proteins from PSE pork are more susceptible to denaturation and degradation in fresh meat and following frozen storage.
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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) | 0.000 | 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) | 0.001 | 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".