ArticleBiosensors2022
Detection of A and B Influenza Viruses by Surface-Enhanced Raman Scattering Spectroscopy and Machine Learning.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Electrochemical and optical biosensors as transformative tools for multiplex detection of influenza viruses.Archives of microbiology · 2026Review
- Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Approaches.Analytical chemistry · 2025Article
- Electro-active evanescent-wave cavity ring-down spectroscopy immunosensor for influenza virus detection.Biomedical optics express · 2025Article
- Rapid profiling of carcinogenic types ofComputational and structural biotechnology journal · 2024Article
- Rapid and label-free influenza A virus subtyping using surface-enhanced Raman spectroscopy with incident-wavelength analysis.Biomedical optics express · 2024Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.Biomedicines · 2024Article
- Recent Advances in Molecular and Immunological Diagnostic Platform for Virus Detection: A Review.Biosensors · 2023Review
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Authors and funding
15 authors.
Funding
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Abstract
We demonstrate the possibility of applying surface-enhanced Raman spectroscopy (SERS) combined with machine learning technology to detect and differentiate influenza type A and B viruses in a buffer environment. The SERS spectra of the influenza viruses do not possess specific peaks that allow for their straight classification and detection. Machine learning technologies (particularly, the support vector machine method) enabled the differentiation of samples containing influenza A and B viruses using SERS with an accuracy of 93% at a concentration of 200 μg/mL. The minimum detectable concentration of the virus in the sample using the proposed approach was ~0.05 μg/mL of protein (according to the Lowry protein assay), and the detection accuracy of a sample with this pathogen concentration was 84%.
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