Risk Factors and Control Measures for Bacterial Contamination in the Bovine Meat Chain: A Review on Salmonella and Pathogenic E.coli
Bibliographic record
Abstract
Salmonella and pathogenic Escherichia coli are known to be the major bacterial agents responsible for human foodborne infections attributable to meat. A review of the specialized literature was carried out to identify the risk factors for bovine meat contamination by these pathogens from the cattle farm to meat consumption. Animal stress during transport to the slaughterhouse and the duration of the lairage period were identified as the key factors influencing the faecal excretion of Salmonella and pathogenic E. coli as well as cattle contamination prior to slaughter. At the abattoir level, hides and visceral contents appear to be the main sources of pathogenic bacteria that contaminate carcasses along the meat production chain. Finally, temperature abuses during distribution and meat contamination by infected handlers were found to be important contributors to the post-slaughter contamination of bovine meat. The findings of this study indicate that efficient management of human food borne infections attributable to bovine meat requires an integrated application of control measures involving all actors along the meat chain, namely slaughterhouses, meat processing plants, distributors and consumers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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".