Olga Andreeva , Artem Tabarov , Konstantin Grigorenko , Alexander Dobroslavin , Azat Gazizulin , Andrey Gorshkov , Alyona Zheltukhina , Nina Gavrilova , Daria Danilenko , Vladimir Vitkin
{"title":"通过机器学习算法比较完整病毒和灭活病毒的 SERS 光谱,用于病毒性疾病的诊断应用","authors":"Olga Andreeva , Artem Tabarov , Konstantin Grigorenko , Alexander Dobroslavin , Azat Gazizulin , Andrey Gorshkov , Alyona Zheltukhina , Nina Gavrilova , Daria Danilenko , Vladimir Vitkin","doi":"10.1016/j.photonics.2024.101290","DOIUrl":null,"url":null,"abstract":"<div><p>In this work, Surface-enhanced Raman spectroscopy (SERS) along with machine learning algorithms (MLA) were used to detect and classify the viral particles to assess the possibility of using the spectra of inactivated influenza A viruses for MLA training and spectra database compilation for further study and diagnosis of intact forms of the virus. Viral particles inactivation was performed by formalin, ultraviolet and beta-propiolactone. Support vector method and principal component analysis allowed to classify intact and inactivated viral particles spectra with an accuracy of 80.0–96.7 %. The results obtained suggest that it is not advisable to create a spectral database and train machine learning algorithms for their further application in SERS diagnostics of intact viruses based on the spectra of the inactivated virus particles.</p></div>","PeriodicalId":49699,"journal":{"name":"Photonics and Nanostructures-Fundamentals and Applications","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of SERS spectra of intact and inactivated viruses via machine learning algorithms for the viral disease’s diagnosis application\",\"authors\":\"Olga Andreeva , Artem Tabarov , Konstantin Grigorenko , Alexander Dobroslavin , Azat Gazizulin , Andrey Gorshkov , Alyona Zheltukhina , Nina Gavrilova , Daria Danilenko , Vladimir Vitkin\",\"doi\":\"10.1016/j.photonics.2024.101290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this work, Surface-enhanced Raman spectroscopy (SERS) along with machine learning algorithms (MLA) were used to detect and classify the viral particles to assess the possibility of using the spectra of inactivated influenza A viruses for MLA training and spectra database compilation for further study and diagnosis of intact forms of the virus. Viral particles inactivation was performed by formalin, ultraviolet and beta-propiolactone. Support vector method and principal component analysis allowed to classify intact and inactivated viral particles spectra with an accuracy of 80.0–96.7 %. The results obtained suggest that it is not advisable to create a spectral database and train machine learning algorithms for their further application in SERS diagnostics of intact viruses based on the spectra of the inactivated virus particles.</p></div>\",\"PeriodicalId\":49699,\"journal\":{\"name\":\"Photonics and Nanostructures-Fundamentals and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photonics and Nanostructures-Fundamentals and Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569441024000658\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photonics and Nanostructures-Fundamentals and Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569441024000658","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Comparison of SERS spectra of intact and inactivated viruses via machine learning algorithms for the viral disease’s diagnosis application
In this work, Surface-enhanced Raman spectroscopy (SERS) along with machine learning algorithms (MLA) were used to detect and classify the viral particles to assess the possibility of using the spectra of inactivated influenza A viruses for MLA training and spectra database compilation for further study and diagnosis of intact forms of the virus. Viral particles inactivation was performed by formalin, ultraviolet and beta-propiolactone. Support vector method and principal component analysis allowed to classify intact and inactivated viral particles spectra with an accuracy of 80.0–96.7 %. The results obtained suggest that it is not advisable to create a spectral database and train machine learning algorithms for their further application in SERS diagnostics of intact viruses based on the spectra of the inactivated virus particles.
期刊介绍:
This journal establishes a dedicated channel for physicists, material scientists, chemists, engineers and computer scientists who are interested in photonics and nanostructures, and especially in research related to photonic crystals, photonic band gaps and metamaterials. The Journal sheds light on the latest developments in this growing field of science that will see the emergence of faster telecommunications and ultimately computers that use light instead of electrons to connect components.