Use of an Ecosystem-Based Approach to Shed Light on the Heterogeneity of the Contamination Pattern of Listeria monocytogenes on Conveyor Belt Surfaces in a Swine Slaughterhouse in the Province of Quebec, Canada
Bibliographic record
Abstract
The role of the accompanying microbiota in the presence of Listeria monocytogenes on meat processing surfaces is not yet understood, especially in industrial production conditions. In this study, 300 conveyor belt samples from the cutting room of a swine slaughterhouse were collected during production. The samples were subjected to the detection of L. monocytogenes. Recovered strains were characterized by serogrouping-PCR, InlA Sanger sequencing and for their ability to form biofilm. A selection of isolates was compared with core genome multi-locus sequence typing analysis (cgMLST). The sequencing of the V4 region of the 16S RNA gene of the microorganisms harvested from each sample was carried out in parallel using the Illumina MiSeq platform. Diversity analyses were performed and MaAsLin analysis was used to assess the link between L. monocytogenes detection and the surrounding bacteria. The 72 isolates collected showed a low genetic diversity and important persistence characteristics. L. monocytogenes isolates were not stochastically distributed on the surfaces: the isolates were detected on three out of six production lines, each associated with a specific meat cut: the half carcasses, the bostons and the picnics. MaAsLin biomarker analysis identified the taxa Veillonella (p ≤ 0.0397) as a bacterial determinant of the presence of L. monocytogenes on processing surfaces. The results of this study revealed a heterogenous contamination pattern of the processing surfaces by L. monocytogenes and targeted a bacterial indicator of the presence of the pathogen. These results could lead to a better risk assessment of the contamination of meat products.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".