Rapid identification and classification of «Escherichia coli» and «Shigella» by attenuated total reflectance - Fourier transform infrared spectroscopy
Bibliographic record
Abstract
Rapid identification of microorganisms is a trending topic in research today. By comparison with other methods of microorganism identification like polymerase chain reaction (PCR), pulsed field gel electrophoresis (PFGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy is much quicker in terms of time of analysis. In addition, ATR-FTIR spectroscopy requires no reagents, is cost effective and has potential to identify microorganisms down to the pathotype level. Most foodborne illnesses are due to improper food handling which can lead to the contamination of food products with pathogenic bacteria such as some strains of Escherichia coli (E. coli) and Shigella species. Differentiating between E. coli and Shigella spp. is challenging because they are genetically similar and was investigated in this thesis by using ATR-FTIR spectroscopy. Various strains of Escherichia (n=190) and Shigella (n=145) of fecal and blood origin were obtained from the McGill University Health Center (MUHC), Laboratoire de Santé Publique du Québec (LSPQ) and Health Canada (HC). For preparation of the samples for analysis, the samples were taken from frozen cultures, plated onto culture media, and incubated at 37C for 18-24 h. After incubation, the sample was sub-cultured and incubated using the same parameters as in the first culture. After sub-culturing, a single isolated colony was taken and smeared onto the ATR crystal of the ATR-FTIR instrument to acquire a spectrum. By using principal component analysis (PCA) and hierarchical cluster analysis (HCA) of the ATR-FTIR spectral data, E. coli was successfully discriminated from Shigella species based on their spectral differences at the regions of 1478-1411 and 1070-1040 cm-1. Moreover, successful discrimination between Shigella sonnei and Shigella flexneri was achieved by using the spectral regions of 1136-1113 and 1218-1207 cm-1. For E. coli O157:H7 and non-O157:H7 Shiga-toxin-producing E. coli (STEC), the separation between the two groups was successful using the regions of 1248-1212 and 1356-1344 cm-1. In conclusion, ATR-FTIR spectroscopy has potential for identifying E. coli and Shigella at the genus, species, pathotype and serotype levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".