Microbiological evaluation of different types of branded and un-branded ready to eat snacks sold at Elementary schools at district Peshawar, Pakistan
Bibliographic record
Abstract
The lack of hygiene in the preparation and packing of ready-to-eat foods becoming a serious public health concern especially in school-aged children due to the intervention of different pathogenic microorganisms which pose risk factors for foodborne disease outbreaks. The present study was designed to assess the bacterial contamination in different types of ready-to-eat branded and un-branded snacks purchased in various elementary schools of district Peshawar, Pakistan. A total of 20 samples were collected from various elementary schools located in Peshawar, Pakistan. The samples were analyzed using the pour plate method for total plate counts (TPC) and the multiple fermentation tube method for total coliforms (TC), and fecal coliforms (FC). The Escherichia coli isolates were identified using E. coli O157:H7 latex test reagent kit Pro Lab. Canada. The results revealed total plate counts of 6.67%, total coliforms bacteria 40%, fecal coliforms bacteria 33.33%, and E. coli 20% of samples, which were higher than the permissible limits. The conclusion of this study revealed that there is a practice gap in food safety knowledge among ready-to-eat foods. Ready-to-eat vendors are usually illiterate, and they lack knowledge of proper hygiene and food handling procedures. The government should pay special attention to improving public awareness of food safety and the 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 | 0.000 |
| 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 | 0.000 | 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".