Enhancing Papaya Shelf‐Life With Upcycled Pea Starch–Neem Oil Polymeric Nanoparticles Synthesized via a Novel Rapid Spray Nanoprecipitation Technique
Bibliographic record
Abstract
ABSTRACT Marketing of fresh ripened papaya is challenging due to its short shelf‐life (2–3 days) resulting in high post‐harvest losses (30%–50%), primarily caused by fungal diseases such as Anthracnose. Neem oil (NO) is well recognized for its ability to extend the shelf‐life of fresh produce, but encapsulation is required to preserve its properties. This study aimed to stabilize and encapsulate NO in a polymeric material via a novel rapid spray nanoprecipitation technique to extend the shelf‐life of papaya fruits under cold storage (4°C ± 1°C, 80% ± 2% RH) and room temperature (22°C ± 2°C, 45% ± 5% RH). The shelf‐life of papayas was extended by 10 days compared to the control when the nanoparticle coating was combined with cold storage showing no fungal growth. After 10 days of storage, weight loss in coated fruits was approximately ~6.22% at cold storage temperatures and 17.5% at room temperature, whereas, in the control group, the weight loss observed was 9.09% at cold storage temperature and 27.46% at room temperature. Additionally, the NO infused starch nanoparticle coating significantly (p < 0.05) maintained fruit firmness compared to untreated control samples. The NO inhibited fungal growth, while the starch polymer coating slowed ripening. Hence, the application of nanoparticle coating in this study can act as an active agent for prolonging the shelf‐life of papayas within the food distribution chain.
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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".