Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by...
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| Veröffentlicht in: | Analytical and bioanalytical chemistry 2023-07, Vol.415 (18), p.3945-3966 |
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| creator | dos Santos, Diego P. Sena, Marcelo M. Almeida, Mariana R. Mazali, Italo O. Olivieri, Alejandro C. Villa, Javier E. L. |
| description | Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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| doi_str_mv | 10.1007/s00216-023-04620-y |
| format | Article |
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L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends</atitle><jtitle>Analytical and bioanalytical chemistry</jtitle><stitle>Anal Bioanal Chem</stitle><addtitle>Anal Bioanal Chem</addtitle><date>2023-07-01</date><risdate>2023</risdate><volume>415</volume><issue>18</issue><spage>3945</spage><epage>3966</epage><pages>3945-3966</pages><issn>1618-2642</issn><issn>1618-2650</issn><eissn>1618-2650</eissn><abstract>Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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| subjects | Analysis Analytical Chemistry Artificial Intelligence Biochemistry Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science Chemometrics Critical Review Data analysis food safety Food Science Forensic science forensic sciences Laboratory Medicine Learning algorithms Machine Learning Mathematical analysis Methods Microbiology Monitoring/Environmental Analysis Multivariate analysis Principles Qualitative analysis Raman spectroscopy Spectroscopy Spectrum analysis Spectrum Analysis, Raman - methods Standardization Statistical analysis Trends Young Investigators in (Bio-)Analytical Chemistry 2023 |
| title | Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends |
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