Detecting Respiratory Viruses Using a Portable NIR Spectrometer—A Preliminary Exploration with a Data Driven Approach Article Swipe
Jiandong Huang
,
Hui Wang
,
Ultan F. Power
,
James McLaughlin
,
Chris Nugent
,
Enayetur Rahman
,
Judit Barabas
,
Paul Maguire
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24010308
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24010308
- https://www.mdpi.com/1424-8220/24/1/308/pdf?version=1704373941
- OA Status
- gold
- Cited By
- 4
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390589374
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390589374Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24010308Digital Object Identifier
- Title
-
Detecting Respiratory Viruses Using a Portable NIR Spectrometer—A Preliminary Exploration with a Data Driven ApproachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-04Full publication date if available
- Authors
-
Jiandong Huang, Hui Wang, Ultan F. Power, James McLaughlin, Chris Nugent, Enayetur Rahman, Judit Barabas, Paul MaguireList of authors in order
- Landing page
-
https://doi.org/10.3390/s24010308Publisher landing page
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https://www.mdpi.com/1424-8220/24/1/308/pdf?version=1704373941Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/24/1/308/pdf?version=1704373941Direct OA link when available
- Concepts
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Feature selection, Computer science, Software portability, Artificial intelligence, Artificial neural network, Machine learning, Obstacle, Pattern recognition (psychology), Political science, Law, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.subsequent | 105 |
| abstract_inverted_index.truncation | 185 |
| abstract_inverted_index.validation | 279, 304 |
| abstract_inverted_index.viruses’ | 1 |
| abstract_inverted_index.Respiratory | 0 |
| abstract_inverted_index.alternative | 22 |
| abstract_inverted_index.application | 61 |
| abstract_inverted_index.data-driven | 132 |
| abstract_inverted_index.demonstrate | 313 |
| abstract_inverted_index.difficulty. | 102 |
| abstract_inverted_index.experiments | 198 |
| abstract_inverted_index.feasibility | 315 |
| abstract_inverted_index.processing, | 186 |
| abstract_inverted_index.respiratory | 140, 143, 327 |
| abstract_inverted_index.specificity | 71 |
| abstract_inverted_index.Conventional | 13 |
| abstract_inverted_index.interweaving | 81 |
| abstract_inverted_index.limitations, | 66 |
| abstract_inverted_index.portability, | 42 |
| abstract_inverted_index.specifically | 204 |
| abstract_inverted_index.spectrometer | 156 |
| abstract_inverted_index.spectroscopy | 320 |
| abstract_inverted_index.virus-absent | 87 |
| abstract_inverted_index.Near-Infrared | 25 |
| abstract_inverted_index.deployability | 49 |
| abstract_inverted_index.respectively, | 232 |
| abstract_inverted_index.spectroscopy, | 27 |
| abstract_inverted_index.virus-present | 85 |
| abstract_inverted_index.‘unknown’ | 259 |
| abstract_inverted_index.classification | 217 |
| abstract_inverted_index.laboratory-based, | 17 |
| abstract_inverted_index.Characteristically, | 75 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5100460917, https://openalex.org/A5101625188 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I138801177 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8199999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.85564541 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |