Using the SERS method and machine learning technology to detect the influenza A virus

Tabarov, A.T.; Vitkin, Vladimir; Danilenko, Daria; Andreeva, O. V.; Shemanaeva, A.A.; Popov, E. E.; Dobroslavin, A.A.; Kurikova, V.V.; Kuznetsova, O.B.

doi:10.1109/iclo54117.2022.9840020

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“…Surface-enhanced Raman spectroscopy (SERS) is considered a promising alternative for the differential detection of viral infections [ 7 , 8 , 9 , 10 , 11 ]. The viral envelope contains surface proteins that differ in their amino acid composition and conformation which leads to the difference in Raman spectra of various viruses.…”

Section: Introductionmentioning

confidence: 99%

“…The latter allows amplification of the Raman scattering signal of the test sample due to the plasmon resonance effect, by 10 3 –10 9 times, and the subsequent detection of the pathogen in low concentrations (5 × 10 8 PFU/mL) [ 7 ]. Identification of viral particles by SERS spectroscopy has a number of potential advantages over classical diagnostic methods, such as the speed of testing and the possibility of diagnosing without the need to use specific antibodies or aptamers [ 7 , 9 , 10 , 11 ]. On the other hand, there are developments that are aimed at the selective detection of a specific pathogen based on SERS substrates made from precious metal nanoparticles and immobilized antibodies or aptamers [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Detection of A and B Influenza Viruses by Surface-Enhanced Raman Scattering Spectroscopy and Machine Learning

et al. 2022

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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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