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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".