Evaluation of a Multiplex PCR for Detection of the Top Seven Shiga Toxin-Producing Escherichia coli Serogroups in Ready-to-Eat Meats, Fruits, and Vegetables
Bibliographic record
Abstract
Abstract Background: Ready-to-eat (RTE) meats, fruits, and vegetables contaminated by Shiga toxin producing Escherichia coli (STEC) raise serious concerns because they are often consumed directly without further processing. Objective: To evaluate a multiplex PCR for the detection of STEC across food categories. Methods: Samples (25 g) from seven RTE meat and nine fruit and vegetable matrices were inoculated with each of seven STEC (O157:H7, O26, O121, O145, O45, O103, O111) strains targeting 10 CFU/25 g, enriched in 225 mL of modified tryptone soya broth (mTSB), and tested by a multiplex real-time PCR for stx and eae genes, following U.S. Department of Agriculture (USDA) Food Safety and Inspection Service (FSIS) Microbiology Laboratory Guidebook (MLG) 5B, which was originally validated for meat products and environmental sponge. Results: The mTSB was successful at enriching for STEC in RTE meat, fruit, and vegetable matrices, except for sprouts; however, mEHEC resulted in successful enrichment of target organisms in mung bean sprouts. Suppression of eae results by stx in PCR was observed in six fruit and vegetable matrices. Conversely, suppression of stx gene by eae was not observed. PCR solely targeting eae is recommended if a fruit or vegetable sample tested positive for stx and negative for eae. Despite the significant effect from food matrix, strain, and experimental batch, the cycle threshold of PCR was <30 in inoculated samples, and mostly 30–42 and up in uninoculated samples. Conclusions: The multiplex PCR can be adopted for detection of all seven regulated STEC in RTE meat, fruit and vegetable matrices after validation with cut off value selected and justified based on real samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 teacher head, 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".