Analytical chemistry · 2021
On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations.
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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
Аннотация
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.