Cu-chitosan nano-net improves keeping quality of tomato by modulating physio-biochemical responses
Bibliographic record
Abstract
Abstract Minimizing the post-harvest losses in fruits and vegetables is one of the challenging tasks in agriculture. To address this issue, we report nano-net of Cu-chitosan nanoparticles (Cu-chitosan NPs) which has the ability to extend the shelf-life of stored tomato. The application of Cu-chitosan NPs (0.01–0.04%) significantly curtailed microbial decay (< 5 versus > 50% in control), physiological loss in weight (14.36 versus 28.13% in control), respiration rate (0.01173 versus 0.01879 g CO 2 kg −1 h −1 ) and maintained fruit firmness (34.0 versus 17.33 N in control) during storage. Further, these NPs significantly retarded loss of titratable acidity, retained total soluble solids, total and reducing sugars, lycopene, ascorbic acid and inhibited polyphenol oxidase. Likewise, NPs effectively preserved L * (lightness), a* (red/green) and b* (blue/yellow) values and maintained organoleptic score. Scanning electron microscopy study confirmed that Cu-chitosan NPs orchestrate into an invisible-intangible nano-net over tomato surface which may plausibly act as a potential barrier at all possible openings (stem scar, cuticle wax, lenticels, and aquaporins) to control microbial infection, moisture loss, gas exchanges and respiration rate. Overall, nano-net extended keeping quality of tomatoes up to 21 days at room temperature (27 ± 2 °C, 55 ± 2% relative humidity).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".