Integrated System for On-Site Rapid and Safe Screening of COVID-19

Dongheyu Zhang; Yuntao Guo; Liyang Zhang; Yao Wang; Siqi Peng; Simeng Duan; Lin Geng; Xiao Zhang; Wei Wang; Mengjie Yang; Guizhen Wu; Jiayi Chen; Zihao Feng; Xinyuan Wang; Yue Wu; Haotian Jiang; Qikang Zhang; Jingjun Sun; Shenwei Li; Yuping He; Meng Xiao; Yingchun Xu; Hongqiu Wang; Peipei Liu; Qun Zhou; Haiyun Luo

doi:10.1021/acs.analchem.2c02337

Integrated System for On-Site Rapid and Safe Screening of COVID-19

Dongheyu Zhang, Yuntao Guo, Liyang Zhang, Yao Wang, Siqi Peng, Simeng Duan, Lin Geng, Xiao Zhang, Wei Wang, Mengjie Yang, Guizhen Wu, Jiayi Chen, Zihao Feng, Xinyuan Wang, Yue Wu, Haotian Jiang, Qikang Zhang, Jingjun Sun, Shenwei Li, Yuping HeMeng Xiao, Yingchun Xu^*, Hongqiu Wang^*, Peipei Liu^*, Qun Zhou^*, Haiyun Luo^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	13810-13819
Number of pages	10
Journal	Analytical Chemistry
Volume	94
Issue number	40
DOIs	https://doi.org/10.1021/acs.analchem.2c02337
Publication status	Published - 11 Oct 2022
Externally published	Yes

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10.1021/acs.analchem.2c02337

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@article{4e511788b995486084052ceca313eaba,

title = "Integrated System for On-Site Rapid and Safe Screening of COVID-19",

abstract = "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.",

author = "Dongheyu Zhang and Yuntao Guo and Liyang Zhang and Yao Wang and Siqi Peng and Simeng Duan and Lin Geng and Xiao Zhang and Wei Wang and Mengjie Yang and Guizhen Wu and Jiayi Chen and Zihao Feng and Xinyuan Wang and Yue Wu and Haotian Jiang and Qikang Zhang and Jingjun Sun and Shenwei Li and Yuping He and Meng Xiao and Yingchun Xu and Hongqiu Wang and Peipei Liu and Qun Zhou and Haiyun Luo",

note = "Publisher Copyright: {\textcopyright} 2022 American Chemical Society.",

year = "2022",

month = oct,

day = "11",

doi = "10.1021/acs.analchem.2c02337",

language = "English",

volume = "94",

pages = "13810--13819",

journal = "Analytical Chemistry",

issn = "0003-2700",

publisher = "American Chemical Society",

number = "40",

}

Zhang, D, Guo, Y, Zhang, L, Wang, Y, Peng, S, Duan, S, Geng, L, Zhang, X, Wang, W, Yang, M, Wu, G, Chen, J, Feng, Z, Wang, X, Wu, Y, Jiang, H, Zhang, Q, Sun, J, Li, S, He, Y, Xiao, M, Xu, Y, Wang, H, Liu, P, Zhou, Q & Luo, H 2022, 'Integrated System for On-Site Rapid and Safe Screening of COVID-19', Analytical Chemistry, vol. 94, no. 40, pp. 13810-13819. https://doi.org/10.1021/acs.analchem.2c02337

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

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 - http://www.scopus.com/inward/record.url?scp=85139425291&partnerID=8YFLogxK

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 -

Integrated System for On-Site Rapid and Safe Screening of COVID-19

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