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Detecting Respiratory Viruses Using a Portable NIR Spectrometer—A Preliminary Exploration with a Data Driven Approach

Huang, J-D. ORCID: 0000-0002-3519-0559, Wang, H. ORCID: 0000-0003-2633-6015, Power, U. ORCID: 0000-0003-3246-3774 , McLaughlin, J. A. ORCID: 0000-0001-6026-8971, Nugent, C. ORCID: 0000-0003-0882-7902, Rahman, E. ORCID: 0000-0001-7238-1859, Barabas, J. ORCID: 0000-0002-0297-1412 & Maguire, P. ORCID: 0000-0002-2725-4647 (2024). Detecting Respiratory Viruses Using a Portable NIR Spectrometer—A Preliminary Exploration with a Data Driven Approach. Sensors, 24(1), article number 308. doi: 10.3390/s24010308


Respiratory viruses’ detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an ‘unknown’ dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Publication Type: Article
Additional Information: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Publisher Keywords: Near-Infrared (NIR) spectroscopy; portable; handhold; respiratory syncytial virus (RSV); Sendai virus (SEV); detection; Artificial Intelligence; machine learning; variable importance in projection (VIP) scores; Quantile; variable truncation
Subjects: Q Science > QR Microbiology > QR180 Immunology
R Medicine > RC Internal medicine
T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Science & Technology
School of Science & Technology > Engineering
SWORD Depositor:
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