基于激光诱导击穿光谱和拉曼光谱对四唑类化合物的快速识别和分类实验研究

王宪双; 郭帅; 徐向君; 李昂泽; 何雅格; 郭伟; 刘瑞斌; 张纬经; 张同来

doi:10.3788/CO.20191204.0888

Volume 12 Issue 4

Aug. 2019

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Chinese Optics > 2019 > 12(4): 888-895.

WANG Xian-shuang, GUO Shuai, XU Xiang-jun, LI Ang-ze, HE Ya-ge, GUO Wei, LIU Rui-bin, ZHANG Wei-jing, ZHANG Tong-lai. Fast recognition and classification of tetrazole compounds based on laser-induced breakdown spectroscopy and raman spectroscopy[J]. Chinese Optics, 2019, 12(4): 888-895. doi: 10.3788/CO.20191204.0888

Citation:

WANG Xian-shuang, GUO Shuai, XU Xiang-jun, LI Ang-ze, HE Ya-ge, GUO Wei, LIU Rui-bin, ZHANG Wei-jing, ZHANG Tong-lai. Fast recognition and classification of tetrazole compounds based on laser-induced breakdown spectroscopy and raman spectroscopy[J]. Chinese Optics, 2019, 12(4): 888-895. doi: 10.3788/CO.20191204.0888

Citation:

WANG Xian-shuang, GUO Shuai, XU Xiang-jun, LI Ang-ze, HE Ya-ge, GUO Wei, LIU Rui-bin, ZHANG Wei-jing, ZHANG Tong-lai. Fast recognition and classification of tetrazole compounds based on laser-induced breakdown spectroscopy and raman spectroscopy[J]. Chinese Optics, 2019, 12(4): 888-895. doi: 10.3788/CO.20191204.0888

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Fast recognition and classification of tetrazole compounds based on laser-induced breakdown spectroscopy and raman spectroscopy

doi: 10.3788/CO.20191204.0888

1.
School of Physics, Beijing Institute of Technology, Beijing 100081, China
2.
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Funds:

National Natural Science Foundation of China 61574017

Beijing Institute of Technology Explosives Chain Innovation Project 2017CX10007

More Information

Corresponding author: LIU Rui-bin, E-mail:liuruibin8@gmail.com
Received Date: 28 Sep 2018
Rev Recd Date: 19 Nov 2018
Publish Date: 01 Aug 2019

Abstract

Abstract

In order to achieve fast non-contact recognition and classification of tetrazoles, an integrated system of laser-induced breakdown spectroscopy(LIBS) and Raman spectroscopy was established. First, the Raman spectra of four tetrazolium compounds, including tetrazolium, 5-aminotetrazol, 1, 5-diaminodiazole and 1-methyl-5-aminotetrazol were collected at an excitation wavelength of 1 064 nm. By analyzing the Raman shift of specific functional groups, they were successfully identified. The plasma radiation spectrum of each sample was collected based on LIBS technology. 140 sets of spectral data were selected for training and a classification model was established. The accuracy of the type area was verified by the remaining 60 sets of data. In this paper, two classification models were established based on PCA(Principal Component Analysis) and SVM(Support Vector Machine). On the one hand, the full spectra were used for PCA. The first 64 principal components were selected and the model was established using an SVM algorithm. On the other hand, 10 characteristic wavelengths were selected for PCA by comparing spectral differences and the first three were selected to establish the model. It was found that the average prediction accuracy of the former is only 88.3%, while the 60 spectral sample points of the latter are all located in the corresponding standard sample type area. The classification accuracy meets 100%. Experimental results show that the combination of LIBS and Raman spectroscopy can accurately identify tetrazole compounds.
- laser induced breakdown spectroscopy(LIBS),
- recognition and classification,
- support vector machine,
- tetrazole compounds,
- Raman spectroscopy,
- principal component analysis

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References(28)

References

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