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

arXiv: https://arxiv.org/abs/2104.04599 · HTML: https://mlsanity.com/paper/2104.04599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 108 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network](https://mlsanity.com/paper/2010.15654/) (2020)
- [High-dimensional cluster analysis with the Masked EM Algorithm](https://mlsanity.com/paper/1309.2848/) (2013)

## Built on by (most cited first, 1 of 1)

- [Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders](https://mlsanity.com/paper/2403.04526/) (2024)

## Abstract (arXiv)

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.