Microbiological Contamination of some Fresh Leafy Vegetables Sold in Cape Coast, Ghana
Bibliographic record
Abstract
This study evaluated the microbiological contamination of cabbage, lettuce, and scallions sold in Abura and Kotokuraba markets in Cape Coast, Ghana. These vegetables were analyzed for the presence and levels of microorganisms using standard microbiological procedures. Results revealed bacterial and fungal contaminations of the vegetables from Abura and Kotokuraba markets. Mean bacterial counts recorded in Nutrient Agar, for example, from Kotokuraba market were 1.93x108, 1.23x108, and 1.17x108 cfu/ml for cabbage, lettuce and scallion, respectively, higher than mean bacterial counts recorded from Abura market at 9.9x107, 2.8x107, and 6.60x107 cfu/ml for cabbage, lettuce and scallion, respectively. Conversely, the mean fungal counts for cabbage, lettuce and scallion were higher at Abura market than Kotokuraba market. Bacteria isolated from the vegetables include Escherichia coli, Enterobacter spp., Klebsiella spp., Salmonella spp., Serratia marcescens, and Staphylococcus, whereas fungi of the genera Aspergillus, Candida, Fusarium, Penicillium, and Rhodotorula were isolated. These results indicate that the vegetables are significantly contaminated, and have poor microbiological quality that could potentially result in outbreak of foodborne illnesses. Contaminations of the vegetables were due to poor pre- and post-harvest handling practices. The implications of findings of this study on tourism and hospitality industries in Cape Coast are discussed. Keywords: cabbage, food microbiology, foodborne microorganisms, Ghana, lettuce, scallion
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".