Use of reuterin to inhibit mold growth and preserve quality attributes of strawberries during cold storage
Bibliographic record
Abstract
The use of fungicides, many of which are of chemical origin and are governed by stringent laws, such as maximum residual limits (MRLs), has proven to be the most successful method to date, especially when used prior to harvest. In light of regulatory compliance and public health considerations, there is interest in exploring fungicides of natural origin as alternatives to chemical fungicides. The objective of this study is to validate the antifungal potential of reuterin at the postharvest stage and to compare it over a commercial fungicide, fludioxonil, by in vivo testing on common strawberry pathogens, including Botrytis cinerea, Colletotrichum acutatum, Rhizopus stolonifer , and Penicillium expansum . Analysis of strawberries stored at 4 °C/95 % RH for 12 d revealed that 2000 mM squalene and 100 mM reuterin did not adversely affect fruit quality parameters such as color, total soluble solids, titratable acidity, weight loss, and visual quality. Reuterin at 50 mM resulted in a sizable decrease in spore count of 3 log CFU mL −1 ( p = 0.003). These results suggest that reuterin may be promising as a potential new biofungicide suitable for pre- and post-harvest application.
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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) | 0.001 | 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".