Emerging Trends and Application of Edible Coating as a Sustainable Solution for Postharvest Management in Stone Fruits: A Comprehensive Review
Bibliographic record
Abstract
Stone fruits, also known as drupes, include apricots, peaches, plums, cherries, and nectarines that have high global demand due to their nutritional benefits and palatable characteristics. Being soft fruits, they are susceptible to various postharvest issues, reducing their shelf life, with postharvest loss reaching 15%-50%. Among various postharvest management techniques, edible coating is emerging as a popular method due to its positive effects on the quality, physiochemical, phytochemical, and organoleptic characteristics of these fruits. By creating a modified atmosphere, edible coatings can effectively reduce weight loss to less than 10%, meeting the international standard for marketing stone fruits. They also help delay firmness loss, as observed in peaches, where coated samples retained a firmness of 5.6 N compared to 1.8 N in control. Furthermore, edible coating can extend shelf life beyond 7 days at ambient temperature and up to 35 days in cold storage, as reported in literature. These coatings create a semipermeable barrier to gaseous exchange and moisture, helping preserve aroma compounds and delay ripening and respiration rates. Thus, this comprehensive review investigates the importance of edible coating in enhancing the quality attributes and the shelf life of stone fruits. This article also evaluates most current research done on edible coating applications in stone fruits and provides details on ideal coating conditions and requirements for these fruits. The study also discusses current developments in the application of bioactive compounds and nanotechnological techniques to enhance the functional properties and performance of edible coatings. Nevertheless, because this technology is still in its infancy, commercial technological adoption necessitates both widespread consumer acceptance and economic viability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".