Ethylene Control Technologies in Extending Postharvest Shelf Life of Climacteric Fruit
Bibliographic record
Abstract
Fresh fruit is important for a healthy diet. However, because of their seasonal production, regional specific cultivation, and perishable nature, it is essential to develop preservation technologies to extend the postharvest shelf life of fresh fruits. Climacteric fruit adopt spoilage because of ethylene, a key hormone associated with the ripening process. Therefore, controlling ethylene activity by following safe and effective approaches is a key to extend the postharvest shelf life of fruit. In this review, ethylene control technologies will be discussed aiming for the need of developing more innovative and effective approaches. The biosynthesis pathway will be given first. Then, the technologies determining the postharvest shelf life of climacteric fruit will be described with special attention to the latest and significant published works in this field. Special attention is given to 1-methylcyclopropene (1-MCP), which is effective in fruit preservation technologies. Finally, the encapsulation technology to improve the stability of 1-MCP will be proposed, using a potential encapsulation agent of 1-MCP, calixarene.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".