Bacterial community in fresh fruits and vegetables sold in streets and open-air markets of Dakar, Senegal
Bibliographic record
Abstract
In the context of food transition, the agrifood sector has experienced increasingly strong demand for horticultural products. Fresh fruits and vegetables are mainly vehicles of microorganisms, including pathogens that cause human infections. Food safety and compliance with health standards in the fruits and vegetable value chain are significant concerns and multiple challenges, given the prevalence of foodborne pathologies in Southern countries, particularly in Senegal. This study aimed to describe the bacterial community and assess the microbiological quality of a range of fruits and vegetables sold on street stalls and open-air markets in Dakar, the capital city of Senegal. Samples purchased in seven sites were analyzed to isolate Escherichia coli (E. coli) , Salmonella spp. and Vibrio spp . Some primary samples with a positive microbiological culture were subjected to DNA extraction and subsequent metagenomic analysis. A total of 240 fresh fruit and vegetable samples were collected, including lettuce ( n = 40), tomatoes ( n = 40), mango slices ( n = 40), onion slices ( n = 40), mint leaves ( n = 40), strawberries and grapes (each n = 20). Of these samples, 50.83% (122/240) and 60.41% (145/240) were contaminated with E. coli and Vibrio spp., respectively, while Salmonella was not isolated in any product. E. coli and Vibrio spp. contamination levels, 1.3 10 3 to 6 10 6 Colonies Forming Units per gram of sample (CFU/g) and 4.55 10 2 to 8.73 10 6 CFU/g, respectively, were significantly above the thresholds acceptable for human consumption. The most contaminated samples were lettuce, with a prevalence of 98% (39/40) for E. coli and Vibrio spp. followed by mint leaves with 100% (40/40) and 93% (37/40) of the samples containing E. coli and Vibrio spp. respectively. Out of 46 samples sequenced, the metagenomic analysis revealed high contamination rates for E. coli , Vibrio spp. and Salmonella spp., with 100%, 67.39% and 93.47% of prevalence, respectively. On the other hand, the alpha diversity analysis shows high bacterial diversity in lettuce and mint leaf samples, while the beta diversity analysis highlighted the presence of two major clusters. Our results stress the need for a surveillance system that extends this investigation to a national scale while increasing the number of sampling sites and products analyzed.
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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) | 0.001 | 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 | 0.001 |
| 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".