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.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".