Identification of bacteria by infrared imaging with the use of focal plane array Fourier transform infrared spectroscopy
Bibliographic record
Abstract
The application of infrared imaging employing focal plane array Fourier transform infrared (FPA-FTIR) instrumentation for the identification of bacteria was investigated. FPA-FTIR spectroscopy was shown to provide new opportunities for bacteria identification with unprecedented reliability and throughput by allowing 102--103 FTIR spectra to be acquired simultaneously from surface areas of 90 x 90 to 200 x 200 mum with a spatial resolution of ∼6 mum. The combination of data redundancy and spatial resolution afforded by infrared imaging made it possible to acquire highly reproducible spectra from bacterial films. A protocol for enhancing the reliability of bacteria identification by transmission-mode FPA-FTIR spectroscopy was developed by optimizing spectral acquisition parameters, spectral processing and data analysis; using the differentiation of two Campylobacter species as a test case. The results for this test case were compared with those obtained from three alternate FTIR spectral acquisition modes. The optimized protocol was employed for the generation of a spectral database of foodborne bacteria, containing over 1,000,000 spectra acquired by infrared imaging of 36 species from 19 genera. The development of a modular hierarchical clustering (MHC) model, in combination with the use of a region selection algorithm, allowed all species in the database to be differentiated from each other down to the species level based on differences in their infrared absorption profiles. A validation study involving the identification of well-characterized isolates by comparison of their spectra to those in the database demonstrated the robustness of the MHC model. In a further study employing 44 strains of Clostridium botulinum, the discriminatory power of FPA-FTIR spectroscopy was compared with that of pulsed-field gel electrophoresis, and the region selection algorithm was applied to identify growth medium-independent spectral regions that allowed for the differentiation of Group I and Group II C. botulinum strains in two blind validation studies. The research carried out also demonstrated the high-throughput potential of bacteria identification by infrared imaging when combined with the use of a microarray system for sample deposition. Overall, the novel FPA-FTIR spectroscopy-based bacteria identification protocol developed in this work provides a rapid-response and reagent-free technique suitable for routine use in both food and clinical microbiology laboratories.
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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".