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