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المؤلفYang, Yanjun
المؤلفXu, Beibei
المؤلفHaverstick, James
المؤلفIbtehaz, Nabil
المؤلفMuszyński, Artur
المؤلفChen, Xianyan
المؤلفChowdhury, Muhammad E.H.
المؤلفZughaier, Susu M.
المؤلفZhao, Yiping
تاريخ الإتاحة2023-01-15T05:44:00Z
تاريخ النشر2022-05-17
اسم المنشورNanoscale
المعرّفhttp://dx.doi.org/10.1039/d2nr01277d
الاقتباسYang, Y., Xu, B., Haverstick, J., Ibtehaz, N., Muszyński, A., Chen, X., ... & Zhao, Y. (2022). Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning. Nanoscale, 14(24), 8806-8817.
الرقم المعياري الدولي للكتاب20403364
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131827802&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/38322
الملخصBacterial 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.
اللغةen
الناشرRoyal Society of Chemistry
الموضوعRaman spectroscopy
Lipopolysaccharides
silver nanorods
machine learning
Rapid detection
العنوانDifferentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning
النوعArticle
رقم العدد24
رقم المجلد14
ESSN2040-3372
dc.accessType Open Access


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