Caseinate–Carboxymethyl Chitosan Composite Edible Coating with Soybean Oil for Extending the Shelf Life of Blueberry Fruit
Bibliographic record
Abstract
Utilizing edible films/coatings promises to extend the shelf life of fruits by controlling various physiological parameters (e.g., respiration and transpiration rates), maintaining firmness, and delaying fruit senescence. The influence of composite-based edible coatings made from sodium or calcium caseinate: carboxymethyl chitosan (75:25) on the postharvest quality of fresh blueberries was assessed over a 28-day storage period, on the basis of weight loss and changes in pH, firmness, color, titratable acidity, soluble solids content, mold and yeast count, and respiration rate. The pH of the blueberries increased over the period of storage, with significant differences observed between uncoated and coated (e.g., pH was 3.89, 3.17, and 3.62 at the end of the storage time for uncoated, Ca 75-1% SO, and Na 75-1% SO, respectively. Desirable lower pH values at the end of storage were obtained with the calcium caseinate formulations. Over the duration of storage, other quality parameters (e.g., firmness) were better retained in coated fruits compared to the uncoated (control) one. At the last storage day, the firmness of the uncoated sample was 0.67 N·mm−1 while the sodium and calcium caseinate was 0.63 and 0.81 N.mm−1, respectively. Moreover, the microbial growth was reduced in coated fruits, indicating the effectiveness of coatings in preserving fruit quality. The mold /yeast count was 1.4 and 2.3 log CFU/g for CaCa 75-1% SO and NaCa 75-1% SO compared with uncoated with 4.2 log CFU/g. Adding soybean oil to the caseinate–carboxymethyl chitosan composite edible coating has the potential to positively influence retention of various quality parameters of blueberries, thereby extending their shelf life and maintaining overall quality. Further research could explore the optimization of coating formulations and application methods to enhance their effectiveness in preserving fruit quality during storage.
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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".