Microbiological Evaluation of Different Types of Branded and Non-branded Ready-to-Eat Snacks Sold in Elementary Schools of District Peshawar, Pakistan
Bibliographic record
Abstract
The lack of hygiene in the preparation and packing of ready-to-eat foods is fast becoming a serious public health concern, especially for school children. The intervention of different pathogenic microorganisms in these foods poses the risk of foodborne disease outbreaks. The current study was designed to assess bacterial contamination in different types of ready-to-eat (branded and non-branded) snacks purchased from September 2021 to December 2021 in various elementary schools of Peshawar, Pakistan. A total of 20 samples were collected and analyzed using the pour plate method for total plate count (TPC). Moreover, the multiple fermentation tube method was used for total coliforms (TC) and fecal coliforms (FC). Escherichia coli isolates were identified using E. coli O157:H7 latex test reagent kit Pro Lab, Canada. The results revealed contamination by TPC (6.67%), TC (40%), FC (33.33%), and E. coli (20%) in the samples. These values were higher than the permissible limits set by Food and Agriculture Organization (FAO). Hence, it was concluded that there is a practice gap in food safety knowledge among ready-to-eat food vendors. The vendors are usually untrained and lack the knowledge of proper hygiene and food handling procedures. It is suggested here that the government should pay special attention towards improving public awareness regarding food safety and quality of ready-to-eat foods sold in Peshawar, Pakistan.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".