Prevalence, Serotypes, and Antimicrobial Resistance of Salmonella Species in Ready-to-Eat Foods in Erbil, Iraq
Bibliographic record
Abstract
Ready-to-eat (RTE) foods including sandwiches, pastries, shawarma, and burgers are widely consumed and may potentially increase the risk of foodborne infections. This study investigated the prevalence, serovar diversity, and antimicrobial resistance (AMR) of Salmonella spp. in RTE foods collected between January and June 2024 from street vendors and restaurants across Erbil, Iraq. A total of 350, including 85 sandwiches, 75 pastries, 95 shawarma, and 95 burgers obtained from 115 cafeteria, 120 street vendors, and 115 restaurants were analyzed. Salmonella was detected in 7.1% (n = 25) of samples, with a high contamination in shawarma (8.4%; n = 95), followed by sandwiches (7.1%; n = 85), pastries (6.7%; n = 75), and burgers (6.3%; n = 95). Street vendors exhibited a higher (9.2%; n = 120) contamination rate compared to the cafeteria (6.9%; n = 115) and restaurants (5.2%; n = 115). Among 25 Salmonella isolates, 10 serotypes were identified, with S. Anatum (20%) and S. Typhimurium (16%) being the most prevalent. All isolates were susceptible to colistin, cefadroxil, and gentamicin, while showing high resistance to streptomycin (52%) and levofloxacin (48%). Contamination peaked during the warmer months, particularly in June (15.4%) and May (11.5%), when compared to the other sampling months. These findings highlight significant food safety concerns related to Salmonella contamination and AMR in RTE foods, emphasizing the urgent need for enhanced hygiene practices and regulatory oversight especially among street vendors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".