Implications of Multiple Rinsing on Recovery of Bacteria from Fresh Produce
Bibliographic record
Abstract
Washing fruits and vegetables before eating is recommended to reduce the chance of foodborne illness. Rinsing may not be as effective in removing microorganisms, as consumers believe. The current study determined the effect of multiple water rinses in removing bacteria and yeasts/molds in the first study and compared multiple commercial solution and water rinses in the second study. In the first study, over 3 logs of aerobic bacteria per ml of rinse and almost 2 logs of yeasts/molds per ml of rinse were recovered from the fifth rinse. Grapes had very low bacteria and yeasts/molds counts (< 1.0 log CFU/ml of rinse) compared to the other five produce products (2 to > 4.0 CFU/ml of rinse) tested and the commercial rinses did not reduce the number of bacteria or yeasts/molds recovered from the produce greater than water rinsing. Based on this and other studies, consumers should be aware that produce that has been rinsed can retain high populations of microorganism on their surface.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".