The Use of Vinegar Vapor to Reduce Postharvest Decay of Harvested Fruit
Bibliographic record
Abstract
Vapors of several common vinegars containing 4.2% to 6.0% (= 2.5 to 3.6 mol·L -1 ) acetic acid effectively prevented conidia of brown rot [ Monilinia fructicola (G. Wint.) Honey], gray mold ( Botrytis cinerea Pers.:Fr.) , and blue mold ( Penicillium expansum Link) from germinating and causing decay of stone fruit ( Prunus sp.), strawberries ( Fragaria × ananassa Duchesne), and apples ( Malus × domestica Borkh.), respectively. Fruit were fumigated in 12.7-L sealed containers in which vinegar was dripped on to filter paper wicks or vaporized by heating from an aluminum receptacle. Vapor from 1.0 mL of red wine vinegar (6.0% acetic acid) reduced decay by M. fructicola on `Sundrop' apricots ( Prunus armeniaca L.) from 100% to 0%. Similarly, vapor from 1.0 mL of white vinegar (5.0% acetic acid) reduced decay in strawberries by B. cinerea from 50% to 1.4%. Eight different vinegars, ranging from 4.2% to 6.0% acetic acid, of which 0.5 mL of each vinegar was heat-vaporized, reduced decay by P. expansum to 1% or less in `Jonagold' apples. The volume of heat-vaporized white vinegar (5.0% acetic acid) necessary to reduce decay by P. expansum on `Jonagold' apples to zero was 36.6 μL·L -1 of air. Increasing the number of conidia on the apple surface reduced the effectiveness of vinegar vapor. The number of lesions caused by P. expansum on `McIntosh' apple decreased exponentially with increasing time of fumigation, approaching zero after about 6 hours. These results suggest that vinegar vapor could be an effective alternative to liquid biocides such as sodium hypochlorite for sterilization of surfaces contaminated by conidia of fungal pathogens.
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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".