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 language | English |
|---|---|
| Pages (from-to) | 1-8 |
| Number of pages | 8 |
| Journal | IEEE Sensors Journal |
| Volume | 23 |
| Issue number | 9 |
| Early online date | 6 Oct 2022 |
| DOIs | |
| Publication status | Published online - 6 Oct 2022 |
Bibliographical note
Publisher Copyright:IEEE
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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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