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

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

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

2022 International Conference Laser Optics, ICLO 2022 ; 2022.

Article in English | Scopus | ID: covidwho-2018852

ABSTRACT

ABSTRACT

The worldwide pandemic caused by Covid-19 demonstrates the need to develop new methods for the respiratory viral diseases' diagnosis, which should be fast and accurate. Surface-enhanced Raman spectroscopy (SERS) can be one of such methods. Our work demonstrated the possibility of using SERS technology and machine learning for fast (about 30 seconds) and accurate detection of the influenza A virus in a biological sample. © 2022 IEEE.

Keywords

machine learning; SERS; virus; Diagnosis; Engineering education; Raman spectroscopy; RNA; Biological samples; Disease diagnosis; Influenza A virus; Machine learning technology; Machine-learning; Spectroscopy technology; Surface enhanced Raman spectroscopy; Technology learning; Viral disease; Viruses

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 International Conference Laser Optics, ICLO 2022 Year: 2022 Document Type: Article

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