Microbiological Examination of Frozen Corn Linked to a National Salmonellosis Outbreak Reveals Gaps in Product Hygiene and Thermal Inactivation
Bibliographic record
Abstract
A nationwide outbreak of Salmonella enterica ser. Enteritidis linked for the first time to frozen corn occurred in Canada from September 2021 to January 2022. To identify potential contributing factors, contaminated corn samples were analyzed and compared to unrelated retail samples. Contaminated corn samples had significantly lower sugar content (9% vs. 12%) and higher levels of background microbiota (total aerobic mesophiles, Enterobacteriaceae , and total coliforms) compared to retail samples. S . enterica ser. Enteritidis was recovered from the contaminated corn samples at low levels (average = 5 most probable number [MPN]/100 g). These levels were stable over 3 years of frozen storage (average = 3 MPN/100 g). The response of the outbreak strain to heat was comparable to laboratory strains of the same serovar. Heating retail corn samples artificially inoculated with 1 × 10 8 colony-forming units of S . enterica ser. Enteritidis by immersion in a 90°C water bath for 7 min resulted in a linear decrease of cells over time as the temperature of the food sample increased. Extrapolation of the data suggests a five-log reduction would require a cooking time of 8 min, corresponding to a food sample temperature of 66°C. However, cooking naturally contaminated corn samples beyond these parameters to a temperature of 74°C failed to completely eliminate Salmonella . The results of this study highlight the hazards associated with the presence of Salmonella in frozen vegetables and emphasize the need for improved hygiene during processing and the development of clear, validated cooking procedures to mitigate risks associated with frozen vegetables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".