Survival of toxigenic <scp><i>Escherichia coli</i></scp> on chamomile, peppermint, green, black, ginger, and cinnamon teas during storage and brewing
Bibliographic record
Abstract
Abstract In this study, the persistence of toxigenic Escherichia coli (E. coli) on dried chamomile, peppermint, ginger, cinnamon, black and green teas stored under 4, 10, and 25°C was determined. The E. coli survival rate in ginger and cinnamon teas decreased below 0 on Day 5. In the other tested teas, E. coli survivability showed a downward trend over time, but never dropped to 0. Chamomile tea retained the greatest population of viable E. coli. Meanwhile, die‐off of E. coli was higher at 25°C compared to lower temperatures. Additionally, fate of E. coli during brewing at 60, 70 and 80°C was evaluated. The E. coli population was reduced to below 2 Log colony forming units (CFU)/g after 1 min at 80°C, At the same time, the E. coli survival at 60°C was higher than that at 70°C in all tested teas. The data indicated that if E. coli survives after storage of prepared teas, it may also survive and grow after the brewing process, especially if performed using temperatures <80°C. Finally, we analyzed the correlations between temperature, time, tea varieties and E. coli survival, and successfully constructed a random forest regression model. The results of this study can be used to predict changes in E. coli during storage and fate during the brewing process. Results will form the basis of undertaking a risk assessment.
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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".