Bibliographic record
Abstract
Rabid and accurate identification of Arcobacter species is of great importance because these bacteria have been considered as emerging foodborne pathogens and potential zoonotic agents. Raman spectroscopy has the ability to differentiate bacteria based upon Raman scattering spectral features of bacterial whole cells, which is fast, reagentless, and easy to perform. Thus, we aimed to detect and discriminate Arcobacter at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with chemometric analysis. A total of 82 isolates of 18 Arcobacter species from clinical, environmental and agri-food sources in both Canada and Germany were included. The genus Arcobacter could be successfully differentiated from closely related genera Campylobacter and Helicobacter using Raman spectroscopy via a principal component analysis model. We also determined that bacterial cultivation time and temperature did not significantly influence the spectral reproducibility and the discrimination capability of Raman spectroscopy. For the identification of Arcobacter to the species level, an overall accuracy of 94.13% was achieved for all 18 Arcobacter species by using Raman spectroscopy in combination with machine learning using a convolutional neural network. Furthermore, a back-propagation neural network was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass with an accuracy of over 99%. Finally, Raman spectroscopy showed the ability to detect trace level (10°-10¹ CFU/mL) of Arcobacter from food sample (i.e., milk) after enrichment. The knowledge received from this study can be applied to further investigate the epidemiology of Arcobacter in the food chain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".