Oct. 2019

Article Contents

Chinese Optics> 2019> 12(5): 1139-1146.

LI Ang-ze, WANG Xian-shuang, XU Xiang-jun, HE Ya-ge, GUO Shuai, LIU Yu-fei, GUO Wei, LIU Rui-bin. Fast classification of tobacco based on laser-induced breakdown spectroscopy[J]. Chinese Optics, 2019, 12(5): 1139-1146. doi: 10.3788/CO.20191205.1139

Citation:

LI Ang-ze, WANG Xian-shuang, XU Xiang-jun, HE Ya-ge, GUO Shuai, LIU Yu-fei, GUO Wei, LIU Rui-bin. Fast classification of tobacco based on laser-induced breakdown spectroscopy[J].Chinese Optics, 2019, 12(5): 1139-1146.doi:10.3788/CO.20191205.1139

Citation:

LI Ang-ze, WANG Xian-shuang, XU Xiang-jun, HE Ya-ge, GUO Shuai, LIU Yu-fei, GUO Wei, LIU Rui-bin. Fast classification of tobacco based on laser-induced breakdown spectroscopy[J].Chinese Optics, 2019, 12(5): 1139-1146.doi:10.3788/CO.20191205.1139

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Fast classification of tobacco based on laser-induced breakdown spectroscopy

doi:10.3788/CO.20191205.1139

1.
School of Physics, Beijing Institute of Technology, Beijing 100081, China
2.
Bao Rui Laser Technology Co., Ltd, Changzhou 213000, China

Funds:

National Natural Science Foundation of China61574017

Beijing Institute of Technology Whole Chain of Explosives Innovation Project2017CX10007

More Information

Corresponding author:LIU Rui-bin, E-mail:liuruibin8@gmail.com
Received Date:29 Oct 2018
Rev Recd Date:13 Dec 2018
Publish Date:01 Oct 2019

Abstract

Abstract

Different types of tobacco differ in element type and content. Based on Laser-Induced Breakdown Spectroscopy(LIBS), the atomic spectra were collected to realize fast classification and identification of different tobacco species combined with a support vector machine. In this paper, 9 kinds of cigarettes of different brands were selected and all characteristic peaks of spectral lines were extracted separately. Before classification, all feature peaks need to be preprocessed through multiple methods, including window smoothing to remove background and peak shift correction. After this, principal component analysis was used to reduce the dimension and the support vector machine method(SVM) was used to establish the classification model. Finally, classification results of 9 brands of cigarettes were obtained, with an average accuracy of 97.47%. It was proven that LIBS has great potential for applications in tobacco rapid identification, anti-counterfeiting identification and on-site rapid classification.
- laser induced breakdown spectroscopy,
- principal component analysis,
- support vector machine,
- tobacco,
- classification and recognition

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References (19)

References

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