Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning-Reference-Cited by-同舟云学术

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

Published:2022 Issue:24 Volume:14 Page:8806-8817
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Yang Yanjun¹^ORCID,Xu Beibei²,Haverstick James³,Ibtehaz Nabil⁴^ORCID,Muszyński Artur⁵^ORCID,Chen Xianyan²,Chowdhury Muhammad E. H.⁶,Zughaier Susu M.⁷^ORCID,Zhao Yiping³^ORCID

Affiliation:

1. School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA

2. Department of Statistics, The University of Georgia, Athens, GA 30602, USA

3. Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA

4. Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA

5. Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA

6. Department of Electrical Engineering, College of Engineering, Qatar University, PO. Box 2713, Doha, Qatar

7. Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, PO. Box 2713, Doha, Qatar

Abstract

A rapid and simple method was proposed for differentiation and classification of eleven bacterial endotoxins based on surface enhanced Raman scattering (SERS) and advanced machine learning algorithms.

Funder

Qatar National Research Fund

Office of Science

National Institutes of Health

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2022/NR/D2NR01277D

Reference45 articles.

1. Culture-free diagnostics of Pseudomonas aeruginosa infection by silver nanorod array based SERS from clinical sputum samples