A New Whole Genome Culture-Independent Diagnostic Test (WG-CIDT) for Rapid Detection of Salmonella in Lettuce
Bibliographic record
Abstract
The rapid detection of foodborne microbial pathogens contaminating fresh fruits and vegetables during the intervening period between harvest and consumption could revolutionize microbial quality assurance of food usually consumed raw and those with a limited shelf life. We have developed a sensitive, shotgun whole genome sequencing protocol capable of detecting as few as 1 colony forming unit (cfu) of Salmonella Typhimurium spiked on 25 g of lettuce. The Ion Torrent sequencing platform was used to generate reads of globally amplified DNA from microbes recovered from the surface of lettuce followed by bioinformatic analyses of the nucleotide sequences to detect the presence of Salmonella. The test is rapid and sensitive, and appropriate for testing perishable foods, and those consumed raw, for Salmonella contamination. The test has the potential to be universally applicable to any microbial contaminant on lettuce as long as a suitable bioinformatics pipeline is available and validated. A universal test is expected to pave the way for preventive and precision food safety and the re-shaping of the entire spectrum of food safety investigations from the current disease-limiting, reactive procedure to a proactive, disease prevention process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".