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Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning.
Yang, Yanjun; Xu, Beibei; Murray, Jackelyn; Haverstick, James; Chen, Xianyan; Tripp, Ralph A; Zhao, Yiping.
  • Yang Y; School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA. Electronic address: YanjunYang@uga.edu.
  • Xu B; Department of Statistics, The University of Georgia, Athens, GA, 30602, USA.
  • Murray J; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, GA, 30602, USA.
  • Haverstick J; Department of Physics and Astronomy, The University of Georgia, Athens, GA, 30602, USA.
  • Chen X; Department of Statistics, The University of Georgia, Athens, GA, 30602, USA.
  • Tripp RA; Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, GA, 30602, USA.
  • Zhao Y; Department of Physics and Astronomy, The University of Georgia, Athens, GA, 30602, USA. Electronic address: zhaoy@uga.edu.
Biosens Bioelectron ; 217: 114721, 2022 Dec 01.
Article in English | MEDLINE | ID: covidwho-2031162
ABSTRACT
Rapid and sensitive pathogen detection is important for prevention and control of disease. Here, we report a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) and machine learning for the rapid and accurate detection of thirteen respiratory virus species including SARS-CoV-2, common human coronaviruses, influenza viruses, and others. Virus detection and measurement have been performed using highly sensitive SiO2 coated silver nanorod array substrates, allowing for detection and identification of their characteristic SERS peaks. Using appropriate spectral processing procedures and machine learning algorithms (MLAs) including support vector machine (SVM), k-nearest neighbor, and random forest, the virus species as well as strains and variants have been differentiated and classified and a differentiation accuracy of >99% has been obtained. Utilizing SVM-based regression, quantitative calibration curves have been constructed to accurately estimate the unknown virus concentrations in buffer and saliva. This study shows that using a combination of SERS, MLA, and regression, it is possible to classify and quantify the virus in saliva, which could aid medical diagnosis and therapeutic intervention.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Biosensing Techniques / COVID-19 Type of study: Diagnostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: Biosens Bioelectron Journal subject: Biotechnology Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Biosensing Techniques / COVID-19 Type of study: Diagnostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: Biosens Bioelectron Journal subject: Biotechnology Year: 2022 Document Type: Article