Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.
【저자키워드】 viral infection, near-infrared spectroscopy, 【초록키워드】 COVID-19, coronavirus disease, SARS-CoV-2, Saliva, Transmission, point-of-care, specificity, Lentiviral particle, nasopharyngeal, Patient, human saliva, optical, information, vesicular stomatitis virus, Analysis, Quantitative analysis, G protein, symptomatic cases, saline solution, PCR-negative, screening strategy, molecular test, SARS-CoV-2 viral loads, pseudotyped, required, the spike protein, reduce, the SARS-CoV-2,