Open Access

REVIEW

A Survey on Machine Learning in Chemical Spectral Analysis

Dongfang Yu, Jinwei Wang^*

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Jinwei Wang. Email:

Journal of Information Hiding and Privacy Protection 2020, 2(4), 165-174. https://doi.org/10.32604/jihpp.2020.010466

Received 15 August 2020; Accepted 21 September 2020; Issue published 07 January 2021

Abstract

Chemical spectral analysis is contemporarily undergoing a revolution and drawing much attention of scientists owing to machine learning algorithms, in particular convolutional networks. Hence, this paper outlines the major machine learning and especially deep learning methods contributed to interpret chemical images, and overviews the current application, development and breakthrough in different spectral characterization. Brief categorization of reviewed literatures is provided for studies per application apparatus: X-Ray spectra, UV-Vis-IR spectra, Micro-scope, Raman spectra, Photoluminescence spectrum. End with the overview of existing circumstances in this research area, we provide unique insight and promising directions for the chemical imaging field to fully couple machine learning subsequently.

---

Keywords

---