Analysis of quality-related proteins in golden pompano ( <i>Trachinotus ovatus</i>) fillets with modified atmosphere packaging under superchilling storage
Bibliographic record
Abstract
Here, we aimed to study the changes in proteome of golden pompano fillets during post-mortem storage. TMT-labeled quantitative proteomic strategy was applied to investigate the relationships between protein changes and quality characteristics of modified atmosphere packaging (MAP) fillets during superchilling (-3 ℃) storage. Scanning electron microscopy was used to show that the muscle histology microstructure of fillets was damaged to varying degrees, and low-field nuclear magnetic resonance was used to find that the immobile water and free water in the muscle of fillets changed significantly. Total sulfhydryl content, TCA-soluble peptides and Ca2+-ATPase activity also showed that the fillet protein had a deterioration by oxidation and denaturation. The Fresh (FS), MAP, and air packaging (AP) groups were set. Total of 150 proteins were identified as differential abundant proteins (DAPs) in MAP/FS, while 209 DAPs were in AP/FS group. The KEGG pathway analysis indicated that most DAPs were involved in binding proteins and protein turnover. Correlation analysis found that 52 DAPs were correlated with quality traits. Among them, 8 highly correlated DAPs are expected to be used as potential quality markers for protein oxidation and water-holding capacity. These results provide a further understanding of the muscle deterioration mechanism of packaging golden pompano fillets during superchilling.
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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) | 0.000 | 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".