Effect of chitosan coatings incorporated with antioxidants of bamboo leaves and potassium sorbate on lipid oxidation and hydrolysis of scallop ( <i>Chlamys farreri</i>) during refrigerated storage
Bibliographic record
Abstract
Nutrient loss, caused by lipid oxidation and hydrolysis, occurs in the scallop adductor muscle during refrigerated storage. To retard this process, chitosan coating with inclusion of antioxidant of bamboo leaves (AOB) and potassium sorbate (PS) were used. Results indicated the coatings employed could significantly inhibit the increases in total number of colonies, total volatile basic nitrogen, peroxide value, thiobarbituric acid-reactive substances, lipase activity and free fatty acid content as well as the decrease in contents of triacylglycerols, phosphatidylcholine, phosphatidylethanolamine and polyunsaturated fatty acids (PUFA) in the scallop adductor muscle during refrigerated storage. After 8 days, the contents of PUFA, eicosapentaenoic acid and docosahexenoic acid in the control adductor muscle decreased by 46.91, 47.46 and 45.50%, respectively, while the corresponding values were 35.93-36.68, 34.01-36.45 and 32.73-37.50%, respectively, for AOB- and PS-coated adductor muscles. Therefore, chitosan coatings incorporated with AOB and PS could retard nutrient loss by inhibiting lipid oxidation and hydrolysis.
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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".