Bacterial Contamination on Beef Sold at Selected Wet Markets in Selangor and Kuala Lumpur
Bibliographic record
Abstract
Beef is one of the essential sources of protein in human diet. But retail beef are easily contaminated with pathogenic bacteria and can cause foodborne disease. To determine the bacterial contamination, 45 samples of retail beef including imported beef (n = 24) and local beef (n = 21) were collected from selected wet markets at every district in Selangor. Samples were analyzed for total viable counts (TVC), Escherichia coli, Enterobacteriaceae and the incidence of pathogenic bacteria which are Salmonella spp. and E. coli O157:H7. Overall results showed that all beef samples (n = 45) were positive for TVC and Enterobacteriaceae at an average reading of (mean ± SD) 7.05±0.78 log CFU/g and 5.05±0.87 log CFU/g, respectively. Only 53.3% of the total samples were contaminated with E. coli (4.22±0.60 log CFU/g) whereas only 24.4% of total samples were found to be positive with Salmonella spp. All bacterial count readings fall under the marginal category based on the international standards. There were no significant differences (p > 0.05) in microbial counts between the local and imported beef samples for all parameters. Among the E. coli isolates detected from the beef samples, 3 isolates were identified as E. coli O157:H7. In conclusion, meat safety level for the retail beef sold at wet markets in Selangor and Kuala Lumpur is low and requires more attention from the authorities to ensure its microbiological safety for consumers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".