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