Optimized enrichment for the detection of Escherichia coli O26 in French raw milk cheeses
Bibliographic record
Abstract
AIMS: Our main objective was to optimize the enrichment of Escherichia coli O26 in raw milk cheeses for their subsequent detection with a new automated immunological method. METHODS AND RESULTS: Ten enrichment broths were tested for the detection of E. coli O26. Two categories of experimentally inoculated raw milk cheeses, semi-hard uncooked cheese and 'Camembert' type cheese, were initially used to investigate the relative efficacy of the different enrichments. The enrichments that were considered optimal for the growth of E. coli O26 in these cheeses were then challenged with other types of raw milk cheeses. Buffered peptone water supplemented with cefixim-tellurite and acriflavin was shown to optimize the growth of E. coli O26 artificially inoculated in the cheeses tested. Despite the low inoculum level (1-10 CFU per 25 g) in the cheeses, E. coli O26 counts reached at least 5.10(4) CFU ml(-1) after 24-h incubation at 41.5 °C in this medium. CONCLUSIONS: All the experimentally inoculated cheeses were found positive by the immunological method in the enrichment broth selected. SIGNIFICANCE AND IMPACT OF THE STUDY: Optimized E. coli O26 enrichment and rapid detection constitute the first steps of a complete procedure that could be used in routine to detect E. coli O26 in raw milk cheeses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".