Synthesis of Neem-Oil-Infused Niosome and Starch Nanoparticle Coatings for Preserving the Quality of Strawberry Fruit
Bibliographic record
Abstract
Strawberries face marketing challenges due to their short post-harvest shelf-life, largely impacted by shrivelling, weight loss, fungal decay, and mechanical damage. Neem oil (NO) is known for its shelf-life extension benefits; however, encapsulation is needed to maintain its efficacy. This study aimed to stabilize and encapsulate NO in a polymeric and lipid material to preserve the quality of strawberries stored at 4 ± 1 °C, 80 ± 2% RH for seven days. After seven days, the nanoparticle-coated fruits showed a weight loss of around 5.9% with niosomes and 8.9% with starch nanoparticles, while the control had a significant 32.45% weight loss. Additionally, both nanoparticle coatings significantly (p < 0.05) preserved fruit colour compared to the untreated control. The findings suggest that nanoparticle coatings could serve as an active agent in preserving the quality of strawberries within the food supply chain. The study provides valuable insights into post-harvest management and fruit preservation, showcasing the effectiveness of these coatings as active packaging solutions.
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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".