Lactic acid improves the efficacy of anti-microbial washing solutions for apples
Bibliographic record
Abstract
The market potential of fresh-cut apples is rapidly expanding in the fresh-cut produce industry. The removal of microbial contaminants of apples before processing is an important step in assuring the safety and extending the shelf life of fresh-cut apples. The goal of this study was to identify efficient anti-microbial washing solution(s) utilizing acceptable amounts of generally regarded as safe disinfectants. Ten selected decontaminant solutions were evaluated for the efficacy of removing Escherichia coli, Listeria monocytogenes and Salmonella enterica subsp. enterica serovar Newport. The most efficient wash treatments consisted of 1% (v/v) lactic acid with 200 ppm sodium hypochlorite and 1.5% (v/v) lactic acid in combination with either 1% (v/v) hydrogen peroxide or 80 ppm peroxyacetic acid. These three wash treatments were also highly effective in reducing the level of total aerobic mesophilic native flora of apples. The hydrogen peroxide and lactic acid combination was identified as the most effective sanitizer for stored apples based on its efficiency in removing L. monocytogenes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".