Rapid Classification of Respiratory Syncytial Virus and Sendai Virus by a Low-cost and Portable Near-infrared Spectrometer

Weiran Song; Hui Wang; Enayetur Rahman; Judit Barabas; Jiandong Huang; Ultan F. Power; Hugh J. Byrne; James McLaughlin; Chris Nugent; Paul Maguire

doi:10.1109/SENSORS47087.2021.9639533

Rapid Classification of Respiratory Syncytial Virus and Sendai Virus by a Low-cost and Portable Near-infrared Spectrometer

Weiran Song, Hui Wang, Enayetur Rahman, Judit Barabas, Jiandong Huang, Ultan F. Power, Hugh J. Byrne, James McLaughlin, Chris Nugent, Paul Maguire

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

In this work, we present the combination of near-infrared spectroscopy and chemometrics to distinguish respiratory syncytial virus (RSV) and Sendai virus (SeV), the first study of its kind. Using a low-cost and portable spectrometer, a total of 440 virus spectra were collected over four separate sessions. The spectra were pre-processed by normalisation and baseline removal, and variable elimination was conducted based on the standard deviation. Partial least squares discrimination analysis was used to model the relationship between the spectra and the virus categories, resulting in the accuracy of 0.825 and 0.855 for validation and prediction, respectively. Since the portable spectrometer has simple operation and can provide analytical results in real time, it can be used as a viable tool for rapid, on-site and low-cost virus screening for RSV, SeV and possibly other similar viruses such as SARS-CoV-2.

Original language	English
Journal	2021 IEEE SENSORS
DOIs	https://doi.org/10.1109/SENSORS47087.2021.9639533
Publication status	Published (in print/issue) - 17 Dec 2021

Keywords

near-infrared spectroscopy
respiratory syncytial virus
Sendai virus
partial least squares discriminant analysis
data pre-processing
classification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/SENSORS47087.2021.9639533

Cite this

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title = "Rapid Classification of Respiratory Syncytial Virus and Sendai Virus by a Low-cost and Portable Near-infrared Spectrometer",

abstract = "In this work, we present the combination of near-infrared spectroscopy and chemometrics to distinguish respiratory syncytial virus (RSV) and Sendai virus (SeV), the first study of its kind. Using a low-cost and portable spectrometer, a total of 440 virus spectra were collected over four separate sessions. The spectra were pre-processed by normalisation and baseline removal, and variable elimination was conducted based on the standard deviation. Partial least squares discrimination analysis was used to model the relationship between the spectra and the virus categories, resulting in the accuracy of 0.825 and 0.855 for validation and prediction, respectively. Since the portable spectrometer has simple operation and can provide analytical results in real time, it can be used as a viable tool for rapid, on-site and low-cost virus screening for RSV, SeV and possibly other similar viruses such as SARS-CoV-2.",

keywords = "near-infrared spectroscopy, respiratory syncytial virus, Sendai virus, partial least squares discriminant analysis, data pre-processing, classification",

author = "Weiran Song and Hui Wang and Enayetur Rahman and Judit Barabas and Jiandong Huang and Power, {Ultan F.} and Byrne, {Hugh J.} and James McLaughlin and Chris Nugent and Paul Maguire",

year = "2021",

month = dec,

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doi = "10.1109/SENSORS47087.2021.9639533",

language = "English",

journal = "2021 IEEE SENSORS",

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TY - JOUR

T1 - Rapid Classification of Respiratory Syncytial Virus and Sendai Virus by a Low-cost and Portable Near-infrared Spectrometer

AU - Song, Weiran

AU - Wang, Hui

AU - Rahman, Enayetur

AU - Barabas, Judit

AU - Huang, Jiandong

AU - Power, Ultan F.

AU - Byrne, Hugh J.

AU - McLaughlin, James

AU - Nugent, Chris

AU - Maguire, Paul

PY - 2021/12/17

Y1 - 2021/12/17

N2 - In this work, we present the combination of near-infrared spectroscopy and chemometrics to distinguish respiratory syncytial virus (RSV) and Sendai virus (SeV), the first study of its kind. Using a low-cost and portable spectrometer, a total of 440 virus spectra were collected over four separate sessions. The spectra were pre-processed by normalisation and baseline removal, and variable elimination was conducted based on the standard deviation. Partial least squares discrimination analysis was used to model the relationship between the spectra and the virus categories, resulting in the accuracy of 0.825 and 0.855 for validation and prediction, respectively. Since the portable spectrometer has simple operation and can provide analytical results in real time, it can be used as a viable tool for rapid, on-site and low-cost virus screening for RSV, SeV and possibly other similar viruses such as SARS-CoV-2.

AB - In this work, we present the combination of near-infrared spectroscopy and chemometrics to distinguish respiratory syncytial virus (RSV) and Sendai virus (SeV), the first study of its kind. Using a low-cost and portable spectrometer, a total of 440 virus spectra were collected over four separate sessions. The spectra were pre-processed by normalisation and baseline removal, and variable elimination was conducted based on the standard deviation. Partial least squares discrimination analysis was used to model the relationship between the spectra and the virus categories, resulting in the accuracy of 0.825 and 0.855 for validation and prediction, respectively. Since the portable spectrometer has simple operation and can provide analytical results in real time, it can be used as a viable tool for rapid, on-site and low-cost virus screening for RSV, SeV and possibly other similar viruses such as SARS-CoV-2.

KW - near-infrared spectroscopy

KW - respiratory syncytial virus

KW - Sendai virus

KW - partial least squares discriminant analysis

KW - data pre-processing

KW - classification

U2 - 10.1109/SENSORS47087.2021.9639533

DO - 10.1109/SENSORS47087.2021.9639533

M3 - Conference article

SN - 1930-0395

JO - 2021 IEEE SENSORS

JF - 2021 IEEE SENSORS

ER -

Rapid Classification of Respiratory Syncytial Virus and Sendai Virus by a Low-cost and Portable Near-infrared Spectrometer

Abstract

Keywords

UN SDGs

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Cite this