Reduction of Enteric Pathogens in Bottled Water Using Residual Ozone
Bibliographic record
Abstract
Ozone is an effective and safe disinfectant used globally in the bottled water industry. Ozone infusion at a high level helps maintain a residual level (0.1-0.4 mg/L) at the time of bottling, thus can ensure 4 log microbial reductions. This study validated the efficacy of residual ozone against Escherichia coli O157:H7, non-O157 Shiga toxin-producing E. coli (STEC; O26, O45, O103, O111, O121, O145), and Salmonella Typhimurium. A pilot-scale ozone system infused 0.5 L polyethylene terephthalate (PET) bottles with ozonated water (0.1-0.4 mg/L). Pathogens were inoculated into samples (∼6 or 4 log CFU/mL) immediately after ozonation treatment, followed by incubation at 25 °C for 5-180 min. Additional trials evaluated the isolated effect of pH (5.0, 7.0, and 9.0) and total dissolved solids (TDS; 5, 50, and 500 mg/L) at a fixed ozone concentration (0.1 mg/L ozone) and contact time (30 min). Posttreatment samples were neutralized (0.1% sodium thiosulfate), filtered, and plated on Petri films for enumeration (35 °C, 48 h). Neutralization (0.1% sodium thiosulfate) was validated to have no antimicrobial effect in controls. All pathogens exhibited ≥4 log reductions across ozone concentrations (0.1-0.4 mg/L), contact times (≥5 min), and inoculum levels (6 or 4 log CFU/mL). Extended contact (30-180 min) did not enhance reduction. However, at 500 mg/L TDS, reductions fell below 4 logs: Salmonella (2.60 log ± 1.24), E. coli O157:H7 (1.72 log ± 1.10), and non-O157 STEC (2.74 log ± 1.45). Similarly, pH 5.0 and 9.0 resulted in <4 log reductions. While residual ozone effectively achieves microbial safety benchmarks, elevated TDS and nonneutral pH significantly impair efficacy. These findings underscore the necessity of monitoring water quality parameters to optimize ozone disinfection in bottled water facilities, ensuring consistent compliance with food safety standards.
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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".