Vapor-Phase Hydroxyl or Chlorine Radical Treatment for Inactivating Listeria monocytogenes on Mushrooms (Agaricus bisporus) without Negatively Affecting Quality or Shelf Life
Bibliographic record
Abstract
ABSTRACT: Processes based on generating vapor-phase hydroxyl radicals or chlorine radicals were developed for inactivating Listeria monocytogenes on mushrooms without negatively affecting quality. Antimicrobial radicals were generated from the UV-C degradation of hydrogen peroxide or hypochlorite and ozone gas. Response surface modeling was used to identify the interaction among the operating parameters for the hydroxyl radical process: UV-C254nm intensity, hydrogen peroxide concentration, and ozone delivered. There was an inverse relationship between hydrogen peroxide concentration and UV-C intensity in terms of the log reduction of L. monocytogenes. The independent parameters for the chlorine radical process were hypochlorite concentration, pH, and UV-C intensity. From predictive models, the optimal hydroxyl radical treatment was found to be 5% (v/v) H2O2, 2.86 mW/cm2 UV-C intensity (total UV-C dose 144 mJ/cm2), and 16.5 mg of ozone. The optimal parameters for the chlorine radical process were 10 ppm of hypochlorite (pH 3.0), 11.0 mg of ozone, and 4.60 mW/cm2 UV-C intensity. When inoculated mushrooms were treated with the optimal hydroxyl radical and chlorine radical processes, the reduction of L. monocytogenes was found to be 2.42 ± 0.42 and 2.61 ± 0.30 log CFU, respectively, without any negative effects on mushroom quality (weight loss and Browning index during 14 days of storage at 4°C). These reductions were significantly greater than those from application of the individual elements of the radical processes and those in the control process, which used a 90-s dip in 1% (v/v) hydrogen peroxide. The study has demonstrated that hydroxyl radical and chlorine radical vapor-phase treatments are equally effective at inactivating L. monocytogenes on mushrooms and can be considered as a preventative control step.
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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".