Discriminating cell line specific features of antibiotic‐resistant strains of Escherichia coli from Raman spectra via machine learning analysis - Archive ouverte HAL Access content directly
Journal Articles Journal of Biophotonics Year : 2022

Discriminating cell line specific features of antibiotic‐resistant strains of Escherichia coli from Raman spectra via machine learning analysis

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Jessica Zahn
Arno Germond
Alice Lundgren

Abstract

While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.
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Dates and versions

hal-03750316 , version 1 (12-08-2022)

Licence

Attribution - CC BY 4.0

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Jessica Zahn, Arno Germond, Alice Lundgren, Marcus Cicerone. Discriminating cell line specific features of antibiotic‐resistant strains of Escherichia coli from Raman spectra via machine learning analysis. Journal of Biophotonics, 2022, 15 (7), ⟨10.1002/jbio.202100274⟩. ⟨hal-03750316⟩

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