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

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 t...

Full description

Saved in:
Bibliographic Details
Published in:Biosensors (Basel) 2022-11, Vol.12 (12), p.1065
Main Authors: Tabarov, Artem, Vitkin, Vladimir, Andreeva, Olga, Shemanaeva, Arina, Popov, Evgeniy, Dobroslavin, Alexander, Kurikova, Valeria, Kuznetsova, Olga, Grigorenko, Konstantin, Tzibizov, Ivan, Kovalev, Anton, Savchenko, Vitaliy, Zheltuhina, Alyona, Gorshkov, Andrey, Danilenko, Daria
Format: Article
Language:English
Subjects:
Accuracy
Adenoviruses
Algorithms
Antibodies
Antigens
Biomarkers
Care and treatment
Classification
Coronaviruses
detection
Diagnosis
Discriminant analysis
Embryos
Epidemics
Evaluation
Herpesvirus 1, Cercopithecine
Humans
Influenza
Influenza A
Influenza A virus
influenza B virus
Influenza viruses
Influenza, Human - diagnosis
Learning algorithms
Machine learning
Medical research
Methods
Nanoparticles
Orthomyxoviridae
Pathogens
Proteins
Raman spectra
Raman spectroscopy
SERS
Severe acute respiratory syndrome coronavirus 2
Silver
Spectroscopy
Spectrum analysis
Spectrum Analysis, Raman - methods
Support vector machines
surface-enhanced Raman spectroscopy
Viral infections
Viruses
Zika virus
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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%.
ISSN:2079-6374
2079-6374
DOI:10.3390/bios12121065