Microbial Quality of Raw Beef and Chevon From Selected Markets in Cape Coast, Ghana
Bibliographic record
Abstract
This study assessed microbial quality of raw beef and chevon (goat meat) from selected meat retail shops in Abura, Kotokuraba and Science markets in Cape Coast, Ghana. Stock solutions from beef and chevon were analyzed on nutrient agar, MacConkey agar, and potato dextrose agar media using microbiological procedures. Results revealed that beef from Kotokuraba market was the most contaminated with mean highest bacterial counts of 1.15x108 and 9.40x107 cfu/ml in nutrient agar and MacConkey agar media, respectively. The results further showed that chevon from Science market was the most contaminated with mean highest bacterial counts of 1.67x108 and 7.10x107 cfu/ml in nutrient agar and MacConkey agar media, respectively. Mean fungal counts in PDA medium was the least recorded for both beef and chevon from all the three markets. Comparative analyses of results suggest that chevon was more contaminated than beef from Abura market, whereas beef was more contaminated than chevon from Kotokuraba market. However, from Science market, except in MacConkey agar medium, where beef was more contaminated than chevon, chevon was more contaminated than beef in nutrient agar and PDA media. Bacteria isolated were Escherichia coli, Klebsiella spp., Nocardia spp., Salmonella spp., Staphylococcus spp., and Streptococcus spp. Fungi of the genera Aspergillus, Candida, Fusarium, Penicillium, and Rhodotorula were isolated. We conclude that raw beef and chevon sold in markets in Cape Coast are contaminated by pathogenic and toxigenic microbes that may affect meat quality and consequently pose public health concerns to 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".