Inactivation of human pathogens and spoilage bacteria on the surface and internalized within fresh produce by using a combination of ultraviolet light and hydrogen peroxide
Bibliographic record
Abstract
AIMS: To evaluate the efficacy of ultraviolet (UV) light (254 nm) combined with hydrogen peroxide (H(2)O(2)) to inactivate bacteria on and within fresh produce. METHODS AND RESULTS: The produce was steep inoculated in bacterial cell suspension followed by vacuum infiltration. The inoculated samples were sprayed with H(2)O(2) under constant UV illumination. The log count reduction (LCR) of Salmonella on and within lettuce was dependent on the H(2)O(2) concentration, temperature and treatment time with UV intensity being less significant. By using the optimized parameters (1.5% H(2)O(2) at 50 degrees C, UV dose of 37.8 mJ cm(-2)), the surface Salmonella were reduced by 4.12 +/- 0.45 and internal counts by 2.84 +/- 0.34 log CFU, which was significantly higher compared with H(2)O(2) or UV alone. Higher LCR of Escherichia coli O157:H7, Pectobacterium carotovora, Pseudomonas fluorescens and Salmonella were achieved on leafy vegetables compared with produce, such as cauliflower. In all cases, the surface LCR were significantly higher compared with the samples treated with 200 ppm hypochlorite. UV-H(2)O(2)-treated lettuce did not develop brown discolouration during storage but growth of residual survivors occurred with samples held at 25 degrees C. CONCLUSIONS: UV-H(2)O(2) reduce the bacterial populations on and within fresh produce without affecting the shelf-life stability. SIGNIFICANCE OF THE STUDY: UV-H(2)O(2) represent an alternative to hypochlorite washes to decontaminate fresh produce.
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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".