Microbiological safety of ready-to-eat fresh-cut fruits and vegetables sold on the Canadian retail market
Bibliographic record
Abstract
Following implementation of Health Canada's Policy on Listeria monocytogenes in Ready-to-Eat Foods by Canadian food safety authorities in 2011, a four-year study (2012-2016) was carried out to gain baseline information on the occurrence of bacterial pathogens, notably the prevalence and levels of Listeria monocytogenes (L. monocytogenes) in various product types of ready-to-eat (RTE) fresh-cut fruits and fresh-cut vegetables sold at retail in Canada. A total of 10,070 pre-packaged samples, including 4691 fresh-cut fruit and 5379 fresh-cut vegetable samples were collected from retail stores across Canada and analyzed for bacterial pathogens and generic Escherichia coli (E. coli). Salmonella species (spp.), E. coli O157:H7, Shigella and Campylobacter were not detected in any of the tested samples. L. monocytogenes was identified in 0.51% (95% CI [0.34, 0.76]) of the fresh-cut fruit and in 0.24% (95% CI [0.14, 0.41]) of the fresh-cut vegetable samples. Of the 37 L. monocytogenes positive samples identified, levels of L. monocytogenes <5 CFU/g, 5-<100 CFU/g, and ≥100 CFU/g were found in 67.6% (25/37), 24.3% (9/37) and 8.1% (3/37) of the samples, respectively. The results of this study indicate that the vast majority of fresh-cut fruits and vegetables sold on the Canadian retail market are safe for consumption. However, contamination by L. monocytogenes can infrequently occur in fresh-cut fruits and vegetables, with certain types of fresh-cut fruits (i.e., melons, apples) and vegetables (i.e., mushrooms, cauliflower) being more likely to be contaminated than others. Safe handling practices are recommended for producers, retailers and consumers including storage at refrigerated temperatures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".