Assessment of Hygiene Practices and Identification of Critical Control Points Relating to the Production of Skewered Meat Sold in N’Djamena-Chad
Bibliographic record
Abstract
Meat is a food of choice because of its nutritional quality. Grills are regularly consumed in Africa and particularly in the Sahelian countries. These are very popular consumer products. However, they can be contaminated by various microorganisms and cause food poisoning if the meat is not handled in hygienic conditions. In order to contribute to improving the quality of these products, we have followed the steps in production of meat skewers by the method of “5M” of Ishikawa. The “HACCP decision tree” model was used to determine the Critical Control Points (CCP). Hazard Analysis Critical Control Point (HACCP) is a method and principles of management of food safety. The results of monitoring procedures for making meat skewers showed many shortcomings in hygiene. Six (06) critical points were determined. As for testing, we conducted microbiological analyzes on fifty (50) units of samples corresponding to ten (10) different types of products collected at different stages of production. Compared to AFNOR (French Association of Standardization), criteria for cooked and dehydrated soups and considering the analytical variability associated with the methods of analysis, our results indicate that the products contaminated with germs indicating failure to comply with hygiene. Samples analyzed presented at different stages of production compliance rate of 40% for total bacteria (30 °C), 30% for total coliforms and thermotolerant coliforms (44 °C). The rate of non-compliance is 40% compared with sulphite-reducing anaerobes. Molds identified in meat skewered and ingredients are Aspergillus niger, Aspergillus flavus, Penicillium sp and Geotrichum sp. Salmonella, S. aureus and yeasts are absent in the samples. Training on good hygiene practices is required at vendors in order to ensure the hygienic quality of grilled meats.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".