A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

TL;DR

This review discusses how combining Raman spectroscopy with artificial intelligence techniques enhances the rapid, accurate, and noninvasive identification of mixture compositions, surpassing traditional chemical analysis methods.

Contribution

It provides a comprehensive overview of AI methods integrated with Raman spectroscopy for substance identification, highlighting recent advances and future prospects.

Findings

AI improves accuracy of mixture analysis

Deep learning enables real-time identification

Raman spectroscopy combined with AI outperforms traditional methods

Abstract

In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques have paved the way for achieving efficient analytical tools capable of identifying mixture components, making an apparent breakthrough in the identification of mixtures beyond the traditional chemical analysis methods. This article summarizes the work of Raman spectroscopy in identifying the composition of substances as well as provides detailed reviews on the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail.