Label-free SARS-CoV-2 Detection Platform Based on Surface-enhanced Raman Spectroscopy with Support Vector Machine Spectral Pattern Recognition

Tieyi Li1

Siddharth Srivastava1

Jun Liu1

Feng Li2

Yong Kim2

David T.W. Wong2

Aaron Carlin3

Ya-Hong Xie1,Email

1Department of Materials Science and Engineering, University of California Los Angeles, Los Angeles, CA 90095, USA
2UCLA School of Dentistry, 10833 Le Conte Ave. Box 951668, Los Angeles, CA 90095-1668, USA
3School of Medicine, University of California San Diego, San Diego, CA 92093, USA

Abstract

We introduce a biosensing platform combining surface-enhanced Raman spectroscopy (SERS) and machine learning for combating COVID-19 and potentially future occurrence of similar pandemics of viral infection in nature. Compared to the RT-PCR and rapid antigen test, our platform can detect SARS-CoV-2 in human saliva with reliable accuracy and in a short time duration. Cross-validation and blind test are performed to identify SARS-CoV-2 virus against close-related particles including SARS-CoV-1 and extracellular vesicles. Simulated clinical samples with SARS-CoV-2 spiked saliva specimens are tested for building the SARS-CoV-2 identifier, 90% sensitivity and 80% specificity are achieved respectively. Clinical samples composed of 5 COVID patients and 5 healthy controls are tested blindly and render 100% sensitivity and 80% specificity based on the trained classifier. Targeting to become a better public pandemic monitoring tool, our platform simplifies the sample harvest and processing procedures and can release test results within five hours. Our study indicates the possibility of inventing a better rapid test compared with RT-PCR and more accurate test compared with antigen test with less cost and complexity. 

Label-free SARS-CoV-2 Detection Platform Based on Surface-enhanced Raman Spectroscopy with Support Vector Machine Spectral Pattern Recognition