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
Cite This Article
D. Yu and J. Wang, "A survey on machine learning in chemical spectral analysis,"
Journal of Information Hiding and Privacy Protection, vol. 2, no.4, pp. 165–174, 2020.