SE‐ResNet‐based classifier for highly similar mixtures based on Raman spectrum: Classification for alcohol systems as an example

• Published in: Journal of Raman spectroscopy Vol. 54; no. 2; pp. 191 - 200
• Main Authors: Xie, Yuhao, Yang, Siwei, Zhou, Shenghua, Liu, Jiazhen, Zhao, Shuai, Jin, Shangzhong, Chen, Qiang, Liang, Pei
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.02.2023
• Subjects: Artificial neural networks; deep learning; Ethanol; high similarity substance; methanol; Mixtures; Neural networks; Propanol; Raman spectra; Raman spectroscopy; ResNet; Spectroscopy; Spectrum analysis
• ISSN: 0377-0486; 1097-4555
• DOI: 10.1002/jrs.6466

Raman spectroscopy is widely used in the identification of substances. Raman spectra contain molecular information from various components and interference from noise and instruments. Therefore, using Raman spectroscopy to identify components is still challenging, especially for substances with high similarity. In this study, a class of highly similar products (methanol and ethanol) and a multicomponent mixture of propanol and water were tested using a portable Raman spectrometer. They are divided into 11 categories according to the volume fraction ratio. A total of 5,060 groups of Raman spectrum data were obtained, constituting the data set of this study. The deep neural network structure adopted in this study is based on ResNet architecture, on which the SE module in the attention mechanism is added, which increases the weight of some spectral features and achieves significant performance improvement. Finally, SE‐ResNet achieves a recall of 0.95, a precision rate of 0.95, a Micro‐F1 of 0.95, and an accuracy rate of 99.20%. In this study, the SE‐ResNet model was used to realize the classification of the proportion of multi substances in the highly similar hybrid system, taking the methanol ethanol mixed system as an example. The data set consists of 5,060 sets of original Raman spectra of methanol‐ethanol hybrid system collected by a portable Raman spectrometer. Finally, SE‐ResNet achieved a recall of 0.95, a precision of 0.95, a Micro‐F1 of 0.95, and an accuracy rate of 99.20%.