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Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering
یادگیری عمیق و روش های هوش مصنوعی برای پراکندگی رامان و سطح رو به افزایش رامان-2020 Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides
an unprecedented opportunity to extract information from complex or big datasets in chromatography,
mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and
surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical
mixtures are acquired as large datasets for the analysis or imaging of chemical systems. The classical
linear methods of processing the information no longer suffice and thus machine learning methods for
extracting the chemical information from Raman and SERS experiments have been implemented
recently. In this review, we will provide a brief overview of the most common machine learning techniques
employed in Raman, a guideline for new users to implement machine learning in their data
analysis process, and an overview of modern applications of machine learning in Raman and SERS. Keywords: Deep learning | Machine learning | Artificial intelligence | Artificial neural network | Raman | Surface enhanced Raman scattering | SERS | Sensors |
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