Fast screening of COVID-19 inpatient samples by integrating machine learning and label-free SERS methods.

Journal: Analytica chimica acta
PMID: 40155171

Abstract

BACKGROUND: Advances in bio-analyte detection demonstrate the need for innovation to overcome the limitations of traditional methods. Emerging viruses evolve into variants, driving the need for fast screening to minimize the time required for positive detection and establish standardized detection. In this study, a SERS-active substrate with Au NPs on a regularly arranged ZrO nanoporous structure was utilized to obtain the SERS spectrum of inpatient samples from COVID-19 patients. Two analytical approaches were applied to classify clinical samples - empirical method to identify peak assignments corresponding to the target SARS-CoV-2 BA.2 variant, and machine learning (ML) method to build classifier models.

Authors

  • Jaya Sitjar
    Engineered Materials for Biomedical Applications Laboratory, Department of Materials Science and Engineering, National Cheng Kung University, Tainan, 701, Taiwan. Electronic address: jaya.sitjar@gmail.com.
  • Huey-Pin Tsai
    Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Han Lee
    Engineered Materials for Biomedical Applications Laboratory, Department of Materials Science and Engineering, National Cheng Kung University, Tainan, 701, Taiwan. Electronic address: 10608102@gs.ncku.edu.tw.
  • Chun-Wei Chang
    Section of Epilepsy, Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan.
  • Xin-Ni Wu
    Engineered Materials for Biomedical Applications Laboratory, Department of Materials Science and Engineering, National Cheng Kung University, Tainan, 701, Taiwan. Electronic address: poal1031623@gmail.com.
  • Jiunn-Der Liao
    Engineered Materials for Biomedical Applications Laboratory, Department of Materials Science and Engineering, National Cheng Kung University, Tainan, 701, Taiwan. Electronic address: jdliao@mail.ncku.edu.tw.

Keywords

External Resources

Popular Topics
Recent Journals