TY - JOUR
T1 - Classification of respiratory syncytial virus and Sendai virus using portable near-infrared spectroscopy and chemometrics
AU - Song, Weiran
AU - Wang, Hui
AU - Power, Ultan
AU - Rahman, Enayetur
AU - Barabas, Judit
AU - Huang, Jiandong
AU - McLaughlin, James
AU - Nugent, Chris
AU - Maguire , Paul
PY - 2022/10/6
Y1 - 2022/10/6
N2 - 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.
AB - 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.
U2 - 10.1109/JSEN.2022.3207222
DO - 10.1109/JSEN.2022.3207222
M3 - Article
SN - 1530-437X
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
ER -