Microbiological safety assessment of ready-to-eat cooked foods in the Addis Ababa School Feeding Program, Ethiopia
Bibliographic record
Abstract
This investigation assessed microbial contamination indicators in RTE school meals and drinking water in the Addis Ababa SFP, Ethiopia. Samples were collected from 18 primary school kitchens in March and April 2024. Microbiological analysis was performed on 37 cooked food samples and 18 drinking water samples using ISO and NMKL guidelines. The microbiological investigation of RTE prepared meal samples revealed an overall acceptable level of quality and safety. However, concerns were identified. Yeasts and molds surpassed reference standards in 78.4 % of samples (>10 2 cfu/ml), E. coli exceeded standards in 10.8 % of samples (>10 2 cfu/ml), and S. aureus counts exceeded limits in 5.4 % of samples (10 3 -10 4 ). Cooked rice the highest microbiological counts, especially of E. coli and S. aureus. Approximately 14.4 % of food samples were unsatisfactory, showing contamination from E. coli, S. aureus, and yeasts and molds. Regarding drinking water, the non-potable percentage in drinking water was 23.4 %, raising concerns about APC microbial count, TC, and FC. In particular, 72 % of the drinking water samples surpassed the APC criteria (>100 cfu/ml), 16 % exceeded the TC standard (>1 cfu/ml), and 5.5 % exceeded the FC threshold. The microbiological quality of meals served through the Addis Ababa SFP generally met established standards. However, some food samples exceeded the permitted limits, indicating hygiene difficulties. Therefore, stringent premises and personal hygiene measures must be implemented to safeguard their safety and well-being of the school children.
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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 | 0.001 |
| 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 | 0.000 |
| 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".