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

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    https://doi.org/10.1039/D2NR01277D (98.27Kb)
    Date
    2022-05-17
    Author
    Yang, Yanjun
    Xu, Beibei
    Haverstick, James
    Ibtehaz, Nabil
    Muszyński, Artur
    Chen, Xianyan
    Chowdhury, Muhammad E.H.
    Zughaier, Susu M.
    Zhao, Yiping
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    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.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131827802&origin=inward
    DOI/handle
    http://dx.doi.org/10.1039/d2nr01277d
    http://hdl.handle.net/10576/38322
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    • Electrical Engineering [‎2822‎ items ]
    • Medicine Research [‎1794‎ items ]

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