Formulation of mint and thyme essential oils with Arabic gum and Tween to enhance their efficiency in the control of postharvest rots of peach fruit
Bibliographic record
Abstract
The peach (Prunus persica) is a fruit that is susceptible to many fungal infections following harvest, leading to significant losses. This study was aimed at evaluating the efficacy of a novel formulation prepared from mint and thyme essential oils and used to protect peaches from postharvest decay. The formulation was prepared by mixing the oils with Arabic gum as a coating material and Tween 20 as an emulsifier. The formulations were tested for their efficacy in inhibiting fungal growth in vitro and suppressing disease development on the fruit under a wide range of temperatures and under cold storage conditions. The results demonstrated the inhibition of growth of Botrytis cinerea, Penicillium expansum and Rhizopus stolonifer following application of the formulations. Six days following application of the formulations, the reduction in growth of the three pathogens was 83.0%, 77.0% and 88.0%, respectively. The formulations succeeded in protecting peaches from postharvest rot under a wide range of temperatures (5–30°C) in vivo. In cold storage (4°C), the formulations protected peaches from the three fungal pathogens for 10 days and significantly hindered disease progress compared with the controls, Arabic gum and Tween 20, for up to 30 days. Use of oil formulations reduced the disease incidence to 25.0–30.0% and lowered disease severity to 26.6–46.7% throughout the time the peach fruit was kept in storage. The findings support the application of the essential oil formulations, Arabic gum and Tween 20 as effective, natural and edible coating materials to preserve peaches free from infections for an extended period.
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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".