Effect of Storage Temperature on the Behaviour of Escherichia coli O157:H7 and Salmonella enterica Serotype Typhimurium on Salad Vegetables
Bibliographic record
Abstract
The behaviour of Escherichia coli O157:H7 and Salmonella Typhimurium on fresh lettuce (Lactuca sativa), cucumber (Cucumis sativus) epidermis and parsley (Petroselinum sativum) under different storage temperatures was studied. Inoculated vegetable pieces were stored at 10 °C, 20 °C and 30 °C and E. coli O157:H7 and S. Typhimurium were enumerated by plate count. At 10 °C, both E. coli O157:H7 and S. Typhimurium declined slowly on all three vegetables whereas at higher temperatures the behaviour was markedly different. On parsley and lettuce at 20 °C S. Typhimurium population returned to roughly the starting count at the end of the experiment whereas it increased in cucumber. Growth of up to 0.9 log cfu/g was observed on parsley for E. coli O157:H7, but population changes on the other vegetables were negligible at 20 °C. At 30 °C growth was observed on all three vegetables for both E. coli O157:H7 and S. Typhimurium. Growth of E. coli O157:H7 and S. Typhimurium increase the risk to the consumer of some types of fresh vegetable. Storage temperature abuse of the above fresh vegetables can lead to food poisoning if not decontaminated appropriately or via cross contamination. This study shows that food safety of fresh produce is improved via storage in chilled temperatures. Even relatively short exposure (a few hours) at higher temperatures can allow growth to occur in all three vegetables thereby increasing the risk to food safety. Chilling fresh vegetables as soon as possible after harvest could prevent growth of pathogens if vegetables are cross-contaminated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".