TY - JOUR
T1 - Integrated System for On-Site Rapid and Safe Screening of COVID-19
AU - Zhang, Dongheyu
AU - Guo, Yuntao
AU - Zhang, Liyang
AU - Wang, Yao
AU - Peng, Siqi
AU - Duan, Simeng
AU - Geng, Lin
AU - Zhang, Xiao
AU - Wang, Wei
AU - Yang, Mengjie
AU - Wu, Guizhen
AU - Chen, Jiayi
AU - Feng, Zihao
AU - Wang, Xinyuan
AU - Wu, Yue
AU - Jiang, Haotian
AU - Zhang, Qikang
AU - Sun, Jingjun
AU - Li, Shenwei
AU - He, Yuping
AU - Xiao, Meng
AU - Xu, Yingchun
AU - Wang, Hongqiu
AU - Liu, Peipei
AU - Zhou, Qun
AU - Luo, Haiyun
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/10/11
Y1 - 2022/10/11
N2 - Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.
AB - Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.
UR - https://www.scopus.com/pages/publications/85139425291
U2 - 10.1021/acs.analchem.2c02337
DO - 10.1021/acs.analchem.2c02337
M3 - Article
AN - SCOPUS:85139425291
SN - 0003-2700
VL - 94
SP - 13810
EP - 13819
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 40
ER -