Abiotic stress hormesis: An approach to maintain quality, extend storability, and enhance phytochemicals on fresh produce during postharvest
Bibliographic record
Abstract
Postharvest losses of whole and fresh-cut fruits and vegetables cause significant reductions in food availability and an increase in economic losses/damages. Additionally, regulatory agencies are increasingly restricting the postharvest use of synthetic chemicals. This has strengthened the need to develop environmentally friendly approaches to postharvest management, such as utilization of natural compounds, antagonist microorganisms, and treatments with abiotic stresses, among others. The current review focuses on the potential of low doses of abiotic stresses to extend the shelf life, increase the amount of health beneficial phytochemicals, and reduce postharvest losses of fresh produce. The positive effects of the responses to low doses of abiotic stresses are based on a biological phenomenon termed hormesis. Research to develop new technologies to improve postharvest handling of fresh fruit and vegetables as well as minimally processed products is critical. The phenomenon of abiotic stress hormesis in fresh fruit and vegetables shows the potential not only to enhance defense compounds that could reduce diseases during postharvest storage and extend shelf life but also to elevate the content of health-promoting substances. The beneficial effects of UV-C hormesis have been extensively investigated in numerous types of fresh produce. However, our knowledge on hormesis exhibited by other abiotic stresses is still limited. Hence, the objective of this review is to discuss the relevance of hormesis for postharvest research by examining whether all abiotic stresses exhibit the phenomenon, its biological significance, the potential application in various commodities, and how it may direct the future of postharvest research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".