Fumigation of sweet cherries with thymol and acetic acid to reduce postharvest brown rot and blue mold rot
Why this work is in the frame
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Bibliographic record
Abstract
Introduction. Sweet cherries are susceptible to postharvest decay. The use of synthetic fungicides is discouraged in postharvest handling because they can leave a residue and present a safety risk. Therefore, naturally occurring compounds have been considered as an alternative. Fumigation of short-chain organic acids and essential oils has shown promise in controlling fungal activities. This study reports their effects on sweet cherries. Materials and method. `Hedelfingen' sweet cherries (Prunus avium L.) were inoculated with conidia of Monilinia fructicola and Penicillium expansum, then fumigated with three levels of thymol or acetic acid for 10 min before cold storage. Results and discussion. After 13 d at 10°C, sweet cherries fumigated with 10 mg × L-1 of thymol significantly reduced brown rot from 21% to 12%, but had no effect on reducing blue mold rot. Fumigation with 6 or 10 mg × L-1 acetic acid significantly reduced blue mold rot from 16% to 2%, but had no effect on reducing brown rot. Fumigation did not have any effect on the firmness, total soluble solids and titratable acid of the sweet cherries. Fumigation with 2 or 6 mg × L-1 of thymol did not accelerate stem browning compared with the control, but fumigation with 10 mg Yen L-1 of thymol caused almost total stem browning. Fumigation with acetic acid showed no impact on discoloration of the stems. Conclusion. Thus, fumigation with acetic acid or thymol at low concentrations has a potential use for postharvest decay control without adverse effects on fruit quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it