Gaseous ozone treatment inactivates Listeria innocua in vitro
Bibliographic record
Abstract
AIMS: To investigate the effects of ozone on inactivation of Listeria innocua on solid media. METHODS AND RESULTS: Suspensions of L. innocua ranging from 4.5 x 10(4 )- 6.4 x 10(4) CFU ml(-1) were inoculated onto potato dextrose agar (PDA, pH 5.6 and 6.8) and nutrient agar (NA, pH 6.0 and 6.8), then exposed to gaseous ozone. Variable factors included postinoculation standing time at 20 degrees C before exposure to ozone, ozone concentration, treatment duration and treatment temperature (5 or 20 degrees C). The interaction among ozone concentration, treatment duration, media and temperature in effecting changes in colony-forming units (CFU) was significant. The 100 nl l(-1) ozone treatment for 2 h reduced the microbial populations by 2-3 log CFU ml(-1). Cell viability decreased more rapidly on PDA than on NA. The average time to obtain a 2 log CFU ml(-1) reduction was 1.3 h at 20 degrees C and 2.5 h at 5 degrees C (P < 0.001). CONCLUSIONS: Gaseous ozone effectively inactivates L. innocua at concentrations of 50 and 100 nl l(-1) during short exposure times at both 5 and 20 degrees C. The Gompretz model can be utilized for determining the response of L. innocua to ozone over time. SIGNIFICANCE AND IMPACT OF THE STUDY: This study provides useful information on ozone inactivating Listeria spp., which may be imposed on ensuring quality and safety of horticultural produce and food products.
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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) | 0.001 | 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".