Rapid and Reagent-Free Method for Identification of Enterobacteriaceae Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy
Bibliographic record
Abstract
Background. Fourier transform infrared (FTIR) spectroscopy is a rapid, reagent-free technique for bacterial identification and classification. When implemented in the attenuated total reflectance ([ATR] as opposed to the commonly used transmission) mode of spectral acquisition, bacterial colonies from culture plates can be analyzed directly without any sample preparation steps, allowing for identification within minutes after initial culture. In this study, we evaluated for the first time the discriminatory power of ATR-FTIR spectroscopy for the rapid classification of Enterobacteriaceae. We demonstrate that this phenotypic fingerprinting technique can complement matrix-assisted laser desorption ionization time-of-flight mass spectrometry by providing additional discriminatory capabilities. Methods. The ATR-FTIR spectra were acquired by transferring isolated colonies from MacConkey agar plates onto the ATR sampling surface of a portable ATR-FTIR spectrometer. The ATR-FTIR spectral acquisition time was ∼1 minute. Four spectra were collected for each isolate from different colonies on the same culture plate, yielding a spectral database containing approximately 1400 ATR-FTIR spectra of Enterobacteriaceae. Spectral data analysis was performed by hierarchical cluster analysis and principal component analysis in conjunction with the use of a feature selection algorithm. Results. Approximately 350 clinical isolates of Enterobacteriaceae, including Enterobacter, Klebsiella, Proteus, Citrobacter, Salmonella, and Shigella spp as well as pathogenic (Shiga toxin-producing Escherichia coli, enterohemorrhagic E coli, enteropathogenic E coli, and uropathogenic E coli) and nonpathogenic E coli, were used in this study. Genus-level and species-level classification of all the clinical isolates was achieved by multivariate statistical analysis of the FTIR data with an overall rate of correct classification exceeding 98%, including complete differentiation between E coli and Shigella spp. Further multivariate statistical analysis of the ATR-FTIR spectra of the E coli isolates yielded complete differentiation between pathogenic and non-pathogenic strains and discrimination among the 4 E coli pathotypes included in this study. Conclusion. The ATR-FTIR spectroscopy can provide a simple and rapid method for species identification of Enterobacteriaceae as well as for discrimination between E coli and Shigella spp within minutes after initial culture. Disclosures. All authors: No reported disclosures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".