Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-infrared Spectroscopy and Chemometrics

Weiran Song, Hui Wang, Ultan Power, Enayetur Rahman, Judit Barabas, JianDong Huang, James McLaughlin, C D Nugent, P Maguire

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Abstract

There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infra-red (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-infrared (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-infrared spectroscopy and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco's modified eagle medium (DMEM), were collected in long-term and short-term experiments. The spectra were pre-processed, and analysed by partial least squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on-site and low-cost virus pre-screening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Sensors Journal
Early online date6 Oct 2022
DOIs
Publication statusPublished online - 6 Oct 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Chemometrics
  • classification
  • near-infrared spectroscopy,
  • partial least squares discriminant analysis
  • Sendia virus
  • respiratory syncytial virus
  • near-infrared spectroscopy
  • Sea measurements
  • Coronaviruses
  • Standards
  • Reflectivity
  • Training
  • Influenza
  • Viruses (medical)

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