Decontamination of fresh‐cut Chinese yams with ethanol, ascorbic acid and combinations following produce inoculations with <i>Escherichia coli</i> and <i>Salmonella</i> pathogens
Bibliographic record
Abstract
Abstract Determining the most effective method for preventing pathogen survival during washing operations is important for the safety of fresh‐cut produce. This study evaluated the efficacy of lower concentrations (15–35%, v/v) of ethanol, alone or in combination with ascorbic acid (AA) (1%, m/v), as well as different washing methods (wash time and number of washes), for inactivating the human pathogens Escherichia coli and Salmonella on inoculated fresh‐cut Chinese yam (Rhizoma dioscoreae, Dioscorea opposita) slices. The results showed that a wash solution consisting of 25% ethanol and 1% AA significantly reduced the populations of E. coli and Salmonella pathogens more than 1 log cfu/g, which is better than ethanol alone (p < .05). There was no significant difference found between ethanol concentrations of 20 and 35% (p > .05). Wash time and number of washes also influenced the effectiveness of pathogen inactivation. However, increasing wash time above 5 min or increasing the number of washes above two had little effect on the reduction of pathogen populations. Practical applications The purpose of this study is to investigate the ability of a novel wash method for inactivating the pathogens on inoculated fresh‐cut Chinese yams. The results from this study may be beneficial for the fresh‐cut food industry as pathogen contamination are major concerns and currently there are no satisfactory solutions that can eliminated pathogens while maintaining quality.
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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".