Effect of Different Storage-Temperature Combinations on Longissimus dorsi Quality upon Sous-vide Processing of Frozen/Thawed Pork
Bibliographic record
Abstract
This study investigated the effect of storage state (chilled state on sous-vide, CS; frozen state without thawing on sous-vide, FS; and frozen/thawed states on sous-vide, TS) and sous-vide cooking temperature (65°C and 72°C) on the longissimus dorsi muscle quality of pork. FS showed a higher moisture content than that of CS and TS (p<0.001), whereas both FS and CS showed higher expressible moisture loss than that of TS (p<0.001). FS showed a lower cooking loss (p<0.001) than that of CS and TS. FS and TS exhibited significantly higher lipid oxidation than that of CS. Carbonyl and sulfhydryl content were not significantly affected by the storage treatment. FS and TS exhibited lower shear force than that of CS (p<0.001). FS and TS showed higher springiness than that of CS (p<0.001), FS exhibited lower gumminess than that of CS and TS (p<0.01). Sous-vide treatment at 65°C exhibited significantly higher moisture content and lower expressible moisture loss, cooking loss, and total and sarcoplasmic protein than those at 72°C. Shear force and springiness of 65°C-treated groups were lower than those of 72°C-treated groups (p<0.01). Cooking temperature significantly influenced overall acceptability, whereas the storage state did not affect the overall acceptability. These results indicated that meat quality might be improved upon cooking from the frozen or frozen/thawed state using sous-vide when compared with traditional processing.
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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 | 0.001 |
| 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".