Issue 24, 2022

Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning

Abstract

Bacterial endotoxin, a major component of the Gram-negative bacterial outer membrane leaflet, is a lipopolysaccharide shed from bacteria during their growth and infection and can be utilized as a biomarker for bacterial detection. Here, the surface enhanced Raman scattering (SERS) spectra of eleven bacterial endotoxins with an average detection amount of 8.75 pg per measurement have been obtained based on silver nanorod array substrates, and the characteristic SERS peaks have been identified. With appropriate spectral pre-processing procedures, different classical machine learning algorithms, including support vector machine, k-nearest neighbor, random forest, etc., and a modified deep learning algorithm, RamanNet, have been applied to differentiate and classify these endotoxins. It has been found that most conventional machine learning algorithms can attain a differentiation accuracy of >99%, while RamanNet can achieve 100% accuracy. Such an approach has the potential for precise classification of endotoxins and could be used for rapid medical diagnoses and therapeutic decisions for pathogenic infections.

Graphical abstract: Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning

Supplementary files

Article information

Article type
Paper
Submitted
06 mar 2022
Accepted
17 may 2022
First published
17 may 2022

Nanoscale, 2022,14, 8806-8817

Author version available

Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning

Y. Yang, B. Xu, J. Haverstick, N. Ibtehaz, A. Muszyński, X. Chen, M. E. H. Chowdhury, S. M. Zughaier and Y. Zhao, Nanoscale, 2022, 14, 8806 DOI: 10.1039/D2NR01277D

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