Research on the quantification method to detect pathogenic bacteria in instant rice noodles by 3-plex droplet digital polymerase chain reaction test
Bibliographic record
Abstract
ObjectiveThis study aimed to establish a quantitative 3-plex droplet digital polymerase chain reaction （ddPCR） method for simultaneously detecting the copy numbers of Salmonella， Bacillus cereus， and Listeria monocytogenes in instant food.MethodsThree pairs of primers and probes corresponding to three single-copy-genes were selected as target genes. The genes were the essC gene in Bacillus cereus， ttrA/ttrC gene in Salmonella， and invasion-associated endopeptidase gene in Listeria monocytogenes. The specificity of the primers and probes were verified by real-time fluorescence quantitative PCR separately. A 3-plex ddPCR method was constructed to detect the copy numbers of three pathogenic bacteria simultaneously.ResultsThe linear ranges were： 25-22 687 copies/20 µL for Salmonella， 19-15 620 copies/20 µL for Bacillus cereus， and 18-23 373 copies/20 µL for Listeria monocytogenes. The three linear correlation coefficients were r≥0.999. relative standard deviation （RSD）≤12% at six concentrations and repeated thrice， indicating favorable repeatability. The minimum detection limits were six copies/20 µL for Salmonella， three copies/20 µL for Bacillus cereus， and seven copies/20 µL for Listeria monocytogenes. When a simulated sample of contaminated rice noodles was detected by 3-plex ddPCR and the plate counting method， the deviation between these two methods was <9%， indicating a good consistency in the results.ConclusionThe 3-plex ddPCR method for the simultaneous and quantitative detection of Salmonella， Bacillus cereus， and Listeria monocytogenes in instant food was quicker， more sensitive， and more accurate than the plate-counting method.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".