Comparison of Roka Atlas® System Performance and Health Canada Reference Method for <i>Listeria</i> Detection From Plastic, Sealed Concrete, and Stainless-Steel Surface Samples
Bibliographic record
Abstract
BACKGROUND: There is a high demand in the food industry and in public health for rapid automated methods capable of high-volume sample processing. OBJECTIVE: In an unpaired study, Roka Atlas® System performance was compared with Health Canada reference method MFHPB-30 for Listeria spp. (LSP Roka assay) and Listeria monocytogenes (LmG2 Roka Assay) detection on plastic (PL), sealed concrete (SC), and stainless-steel (SS) surfaces (45 samples each per candidate or reference method). METHODS: Seeking shorter enrichment time for the candidate method, R2 medium pre-enrichment for 14, 16, and 24 h at 35°C was combined with the Roka assay. Listeria welshimeri, L. innocua, and L. monocytogenes were employed to individually inoculate each of the three surfaces, with two competing microorganisms within the 10-100-fold higher concentration range. RESULTS: False negative, false positive, sensitivity, and specificity were 0, 0, 100, and 100%, respectively, for the plastic, sealed concrete, and stainless-steel surfaces, regardless of inoculation level (high, low, and uninoculated) and enrichment time. Candidate method detected 10, 7, and 9 true positives, versus 10, 6, and 10 by the reference method in individually inoculated SS, PL, and SC, respectively. CONCLUSIONS: Probability of detection for all the three surfaces for the Roka Atlas System was comparable to the reference method in this unpaired study. HIGHLIGHTS: The Roka Atlas System detected targets after as little as 14 h enrichment. Surface type did not negatively affect assay sensitivity or specificity. The Roka Atlas System was comparable to the reference method.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".