EVALUATION OF FPA‐FTIR SPECTROSCOPY AS A TOOL IN THE DIFFERENTIATION OF<i>CAMPYLOBACTER JEJUNI</i>FROM<i>CAMPYLOBACTER COLI</i>ISOLATED FROM RETAIL CHICKEN SAMPLES
Bibliographic record
Abstract
ABSTRACT A method for differentiation ofCampylobacter jejuniandCampylobacter colibased on focal‐plane‐array Fourier transform infrared (FPA‐FTIR) spectroscopy was evaluated as an alternative to the current cumbersome methodology. Two types of reference data banks were constructed, one containing FTIR spectra ofC. jejuniandC. colistrains, and the other containing FTIR spectra of 11Campylobacterspecies. The FPA–FTIR method was tested by identifying 40C. jejuniand 16C. coliisolates from poultry, previously identified biochemically and by a multiplex polymerase chain reaction, through comparison of their spectra against the data banks. Employing theC. jejuniandC. colidata bank produced high sensitivity towardC. jejuni(95%) andC. coli(94%) and high overall specificity (95%). With theCampylobacterspp. data bank, the corresponding values were 82, 88 and 84%, respectively. We conclude that FPA‐FTIR spectroscopy is a valuable tool for the differentiation ofC. jejuniandC. coli,particularly whenC. jejuniandC. colidata banks are used. PRACTICAL APPLICATIONS FoodborneCampylobacterinfections are highly prevalent, and therefore proper detection and identification ofCampylobacterstrains are of paramount importance. Accurate and fast methods for the identification ofCampylobacter jejuniandCampylobacter coliare pressing needs. This study presents a rapid, accurate method for differentiation betweenC. jejuniandC. colibased on Fourier transform infrared (FTIR) spectroscopy. By employing FTIR imaging instrumentation equipped with an infrared microscope and a focal‐plane‐array detector, thousands of spectra are recorded from each isolate in the amount of time a traditional FTIR spectrometer records a single spectrum. In turn, rich biochemical characterizations of bacterial strains, often termed “whole‐organism fingerprints,” are rapidly produced. Comparison of the FTIR spectra of unknown microorganisms against spectral databases of reference strains through the use of principal component analysis allows quick and accurate identification ofC. jejuniandC. colistrains, offering an invaluable tool for food safety assurance and surveillance.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".