The effects of double gelatin containing chitosan nanoparticles–calcium alginate coatings on the stability of chicken breast meat
Bibliographic record
Abstract
Abstract The effects of gelatin coatings (2% and 4%) containing chitosan nanoparticles (ChNPs; 1% and 2%), in combination with calcium‐alginate coatings (CA; 2%), on quality attributes and shelf life of chicken breast meat were evaluated at 4°C for 12 days. The results indicated that double‐active gelatin–calcium alginate coatings had significant ( p < .05) effects on moisture and protein content. Incorporation of ChNPs into double gelatin‐CA coatings led to significant reduction ( p < .05) in TBARS, pH, and TVB‐N values at the end of storage. The counts of total viable count (TVC), coliforms, yeasts, and molds were significantly ( p < .05) lower in all coated samples, particularly in treated samples by 4% gelatin containing 2% ChNPs + 2% CA coatings (6.85, 6.78, and 5.91 log CFU/g, respectively, compared with 8.35, 8.76, and 7.71 log CFU/g in control) at the end of keeping time. The results of sensory attributes showed that the coated samples had higher overall acceptability scores compared with the untreated samples. A synergistic relationship between the concentrations of gelatin and ChNPs was observed in maintaining the quality characteristics of meat samples during storage. Therefore, this study aims to evaluate the performance of double gelatin coating containing ChNPs in combination with CA coating in the storage quality improvement of chicken breast meat stored for 12 days at 4 °C to develop novel and practical coatings for meat and meat products.
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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".