Development of the Multiplex PCR Detection Kit for Salmonella spp., Listeria monocytogenes, and Escherichia coli O157:H7
Bibliographic record
Abstract
This review describes the development of the multiplex PCR detection kit for Salmonella spp., Listeria monocytogenes, and Escherichia coli O157:H7 in food samples. To develop a detection assay, our research team evaluated the optimization of the pre-enrichment broth, the simple DNA extraction method, and the multiplex PCR settings. When this detection protocol was used to detect the above pathogenic bacteria, one cell per 25 g of inoculated sample was detected within 24 h. Moreover, there was excellent agreement between the multiplex PCR assay and the conventional culture method. The multiplex PCR detection assay system was confirmed to be a reliable and useful method for the rapid screening of food products for foodborne pathogens. The assay system was commercialized as a “[TA10] Pathogenic Bacterial Multiplex PCR Detection Kit”. When this kit was provided to four different laboratories for an extensive validation study, there were no significant differences in detection sensitivity among the laboratories. The detection kit will be valuable as a screening method for foods contaminated with these pathogens, and it will also be useful for identifying the sources of outbreaks of foodborne illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".