On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations

Huang, Jinglin; Wen, Jiaxing; Zhou, Minjie; Ni, Shuang; Le, Wei; Chen, Guo; Wei, Lai; Zeng, Yong; Qi, Daojian; Pan, Ming; Xu, Jianan; Wu, Yan; Li, Zeyu; Feng, Yuliang; Zhao, Zongqing; He, Zhibing; Li, Bo; Zhao, Songnan; Zhang, Baohan; Xue, Peili; He, Shusen; Fang, Kun; Zhao, Yuanyu; Du, Kai

[Image: see text] A rapid, on-site, and accurate SARS-CoV-2 detection method is crucial for the prevention and control of the COVID-19 epidemic. However, such an ideal screening technology has not yet been developed for the diagnosis of SARS-CoV-2. Here, we have developed a deep learning-based surface-enhanced Raman spectroscopy technique for the sensitive, rapid, and on-site detection of the SARS-CoV-2 antigen in the throat swabs or sputum from 30 confirmed COVID-19 patients. A Raman database based on the spike protein of SARS-CoV-2 was established from experiments and theoretical calculations. The corresponding biochemical foundation for this method is also discussed. The deep learning model could predict the SARS-CoV-2 antigen with an identification accuracy of 87.7%. These results suggested that this method has great potential for the diagnosis, monitoring, and control of SARS-CoV-2 worldwide.