Efficacy of washing produce in removing human coronavirus OC43 and murine norovirus
Bibliographic record
Abstract
AIMS: Fresh produce is often a vehicle for the transmission of foodborne pathogens such as human norovirus. Thus, it is recommended to wash the surface of produce before consumption, and one of the most common ways to wash produce is by rinsing under running tap water. This study determined the effectiveness of removal of human coronavirus-OC43 (HCoV-OC43), as a surrogate for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and murine norovirus-1 (MNV-1), as a surrogate for human norovirus, from contaminated lettuce, apples and cucumbers. METHODS AND RESULTS: The produce surfaces were artificially inoculated in conjunction with faecal material to represent natural contamination. Rinsing under tap water for 10 s at 40 ml/s removed 1.94 ± 0.44, 1.42 ± 0.00 and 1.42 ± 0.42 log of HCoV-OC43 from apple, cucumber and lettuce respectively. The same washing technique removed 1.77 ± 0.17, 1.42 ± 0.07 and 1.79 ± 0.14 log of MNV-1 from apple, cucumber and lettuce respectively. This washing technique was effective at reducing a significant amount of viral contamination, however, it was not enough to eliminate the entire contamination. There was no significant difference in the reduction of viral load between the two viruses, nor between the three surfaces tested in this study. CONCLUSIONS: Our data suggest that washing under tap water would be an efficient way of reducing the risk of foodborne viral transmission only if the level of contamination is less than 2 log PFU. SIGNIFICANCE AND IMPACT OF STUDY: This study demonstrates that running tap water was effective at reducing the amount of infectious HCoV-OC43 and MNV on produce surfaces, and washing produce continues to be an important task to perform prior to consumption to avoid infection by foodborne viruses, particularly for foods which are eaten raw.
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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 | 0.001 | 0.001 |
| 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".