Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Abstract With the development of Raman spectroscopy and the expansion of its application domains, conventional methods for spectral data analysis have manifested many limitations. Exploring new approaches to facilitate Raman spectroscopy and analysis has become an area of intensifying focus for research. It has been demonstrated that machine learning techniques can more efficiently extract valuable information from spectral data, creating unprecedented opportunities for analytical science. This paper outlines traditional and more recently developed statistical methods that are commonly used in machine learning (ML) and ML‐algorithms for different Raman spectroscopy‐based classification and recognition…

• PU
  Purdue University
• FP
  Fundo para o Desenvolvimento das Ciências e da Tecnologia
• MU
  Macau University of Science and Technology

  Award: FRG‐21‐034‐MISE