Grilling Meat Technology and Sanitary Risk along the Production Chain in Chad
Bibliographic record
Abstract
Grilled meat has a prominent place in our diet for nutritional and dietary reasons. The scarcity of jobs and the procession of unemployment among young Chadians have given grilled meat a real economic boost. The lack of mastery of technologies and abusive consumption of grilled meat would be responsible for diseases as food poisoning, gout, cardiovascular and metabolic diseases. This paper aimed to review the techniques used and the microorganisms isolated from grilled meats in Chad. Several types of grilling are practiced in Chad with different techniques. Kilichi is produced from dried meat coated with seasoning ingredients. On the other hand, Tchélé or skewer uses a thin stem or wooden with a pointed end on which slices of meat are threaded. The simple grill is directly produced from embers from a wood fire. Several microorganisms have been isolated in different grills. Escherichia coli and Staphylococcus aureus were predominant in grilled meats. The presence of Escherichia coli and staphylococcus aureus in grilled meat can lead to the release of toxins and then cause diseases such as gastroenteritis and food poisoning. On the other hand, Aspergillus flavus, Penicillium sp, Geotrichum sp, Aspergillus niger, Rhizopus sp and aflatoxins have been isolated from ingredients powders. Their presence could lead to alteration, leading to the modification of the organoleptic characteristics and nutritional value of grilled meat. It would, therefore, be necessary to master the technology of grilled meat production in order to improve its microbiological and nutritional quality.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".