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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".