Microbial Contamination Associated with “Wagashi-Cheese” Production in Sissala East District- Ghana
Bibliographic record
Abstract
The purpose of the study was to identify the possible sources of microbial contamination in the production of Wagashi-Cheese in the Sissala East District, Upper West Region of Ghana. Also, the hygienic practices involved were examined. A survey, observation and experiment were used to collect data. Eighty (40 raw cow milk producers and 40 Wagashi-Cheese producers) were randomly selected for the survey. Six (3 milk producers and 3 Wagashi –Cheese producers) were purposively sampled for the experimental part. Microbial analysis of the milk and Wagashi-Cheese were carried out using the ISO and NMKL Analytical Standard procedures. Findings from the study revealed that milk and Wagashi-Cheese producers do not practice optimal personal, food and environmental hygiene. Coliform, fecal coliform, Escherichia coli, Total mesophilic (PCA), yeast and mould were identified in both the milk and Wagashi-Cheese. Dirty cow teat, unclean containers for receiving milk and improper handling of milk while transporting to Wagashi-Cheese centers were identified as Critical Control Points along the production line of Wagash-Cheese. Post interventions showed significant (p ≤ 0.05) reduction in microbial levels in the samples analyzed. In conclusion, the application of Hazard Analysis Critical Control Point (HACCP) improved the quality of the final product. HACCP education and training for milk and Wagashi-Cheese producers is highly recommended. In conclusion, there is a significant difference (P ≥ 0.05) in the microbial load of wagashi from the selected producers and thus the alternate hypothesis that there is no significant difference in the microbial loads of wagashi from different producers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".