Influence of Nano-Silica/Chitosan Film Coating on the Quality of ‘Tommy Atkins’ Mango
Bibliographic record
Abstract
In this study, we assessed the coating of ‘Tommy Atkins’ mangoes with films containing chitosan and nano-silicon dioxide in terms of the effects on fruit parameters as an indicator of quality. After coating, the fruits were first stored at 13 ± 1 °C and 90–95% RH for 30 days, and then at 20 ± 2 °C and 70–75% RH for 5 days, which corresponds to the marketing period. The results showed that coating treatments significantly decreased the fruits’ weight loss and decay percentage compared to the uncoated control samples over the storage period. Additionally, all coated treatments delayed skin degreening, reduced endogenous ethylene production, suppressed respiration rate, and maintained the firmness, compared to untreated control fruit. Titratable acidity and vitamin C significantly decreased in all samples during storage, but this decrease was less pronounced in the coated fruits. Furthermore, coating can delay the increments in total soluble solids and total sugars while maintaining total phenolics, and high antioxidant content of fruits, thereby extending the effective length of the marketing period of treated fruits compared to the control. It was shown that the coating combination of 2% chitosan plus 1% nano-silicon dioxide was the most successful in maintaining the mango’s quality under cold storage and during marketing.
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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) | 0.001 | 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".