MicroSEQ®Listeria monocytogenes Detection Kit
Bibliographic record
Abstract
Abstract The Applied Biosystems Performance Tested MethodSM for detecting Listeria monocytogenes in food and environmental samples was compared to the Health Canada reference method (MFHPB-30) for the analysis of five ready-to-eat (RTE) meats (deli turkey, hot dogs, liver paté, deli ham, and raw fermented sausage) and a stainless steel environmental surface. The MicroSEQ® method includes the MicroSEQListeria monocytogenes Detection Kit and the option of two different sample preparation kits, either the automated high-throughput PrepSEQ™ Nucleic Acid Extraction Kit or the manual low- to mid-throughput PrepSEQ Rapid Spin Sample Preparation Kit. For each sample matrix, 20 replicates were analyzed at two inoculum levels: for RTE meats, a low-level inoculum at 0.2–2 CFU/25 g and a high-level inoculum at 2–5 CFU/25 g; and for environmental surfaces, a low-level inoculum at 0.2–2 CFU/5 cm2 sampling area and a high-level inoculum at 2–5 CFU/5 cm2 sampling area. Five control replicates were also analyzed at 0 CFU/25 g (uninoculated) for food or 0 CFU/5 cm2 sampling area for environmental surface. Both sample preparation methods returned identical results. There was no statistically significant difference in the number of positive samples detected by the MicroSEQ Listeria monocytogenes method and the MFHPB-30 reference method for four of the five RTE meats and the one stainless steel surface tested. For deli turkey, there was a statistically significant difference in the number of positive results detected by the MicroSEQ method and the Health Canada MFHPB-30 reference method for the low inoculation level, with the MicroSEQ method detecting more positives. Because the MicroSEQ method uses real-time PCR to detect pathogens, it provides faster time-to-results compared to culture methods while at the same time demonstrating equivalent detection. The MicroSEQ method detects L. monocytogenes within 2 to 3 h following 24 to 28 h enrichment compared to culture methods that take at least 5 days for presumptive positive results.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.026 |
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".