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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

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. 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[J]. 中国光学(中英文), 2019, 12(4): 888-895. doi: 10.3788/CO.20191204.0888
引用本文: 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. 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[J]. 中国光学(中英文), 2019, 12(4): 888-895. doi: 10.3788/CO.20191204.0888
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

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

doi: 10.3788/CO.20191204.0888
基金项目: 

国家自然科学基金项目 61574017

北京理工大学火炸药全链条创新专项 2017CX10007

详细信息
    作者简介:

    王宪双(1994-), 女, 山东济南人, 硕士研究生, 主要从事激光诱导激光光谱学, 含能材料的谱学分析, 爆炸反应动力学等方面的研究。E-mail:g5michelle@163.com

    刘瑞斌(1977-), 男, 河北承德人, 博士, 副教授, 博士生导师, 1999年、2003年于长春光机学院分别获得学士、硕士学位, 2007年于中国科学院物理研究所获得博士学位, 主要从事半导体材料和微纳光电器件光学性质、激光器、光电探测、激光光谱学、可调激光等方面的研究。E-mail:liuruibin8@gmail.com

  • 中图分类号: O433.4

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

Funds: 

National Natural Science Foundation of China 61574017

Beijing Institute of Technology Explosives Chain Innovation Project 2017CX10007

More Information
  • 摘要: 为了实现对四唑类化合物的快速非接触识别和分类,本文搭建了激光诱导击穿光谱和拉曼光谱集成测试系统。首先采集了4种四唑类化合物在1 064 nm激发波长下的拉曼光谱,包括四氮唑、5-氨基四氮唑、1,5-二氨基四氮唑和1-甲基-5-氨基四氮唑。通过对特定官能团拉曼峰位的分析,成功地将它们鉴别出来。然后基于激光诱导击穿光谱(LIBS)技术,采集各个样本的等离子体辐射光谱。选取140组光谱数据进行训练,建立分类模型,剩余60组数据对所得的类型区域的准确性进行验证。本文基于主成分分析(PCA)与支持向量机(SVM)相结合的算法,建立了两个分类模型。一是将全谱进行主成分分析,选取前64个主成分,利用支持向量机(SVM)算法建立模型。二是通过对比光谱差异,选取10个特征波长进行主成分分析,选取前3个主成分建立模型。发现前者平均预测准确度只有88.3%,而后者60个光谱样本点全部落在其对应的标准样品类型区域内,分类准确度达到100%。实验结果表明,将激光诱导击穿光谱和拉曼光谱联合使用,可以准确地鉴别四唑类化合物。

     

  • 图 1  LIBS-Raman集成测试系统

    Figure 1.  Schematic diagram of integrated LIBS-Ramansystem

    图 2  四唑类化合物的Raman光谱图

    Figure 2.  Raman spectra of tetrazole compounds

    图 3  四唑类化合物的LIBS光谱图

    Figure 3.  LIBS spectra of tetrazole compounds

    图 4  前3个主成分对原始数据的解释率及累计解释率

    Figure 4.  Individual interpretation rate(left, bar graph) and cumulative interpretation rates(right, line) of the first three principal components to the original data

    图 5  前3个主成分的三维得分图

    Figure 5.  Scoreplot of the tetrazole compounds samples′ LIBS spectra with respect to the first three principal components

    表  1  四唑类化合物的主要信息

    Table  1.   Main information of tetrazole compounds

    Code Compounds Molecular formula Chemical structure
    No.1 Tetrazolium CH2N4
    No.2 5-aminotetrazolium CH3N5
    No.3 1, 5-diaminodiazole CH4N6
    No.4 1-methyl-5-aminotetrazoliu C2H5N5
    下载: 导出CSV

    表  2  基于特征变量降维的四唑类化合物分类结果

    Table  2.   Classification results of tetrazole compounds based on dimension reduction of characteristic variables

    Variable Number Predictive variable Accuracy/%
    No.1 No.2 No.3 No.4
    No.1 15 15 0 0 0 100
    No.2 15 0 15 0 0 100
    No.3 15 0 0 15 0 100
    No.4 15 0 0 0 15 100
    下载: 导出CSV

    表  3  基于全谱降维的四唑类化合物分类结果

    Table  3.   Classification results of tetrazole compounds based on dimension reduction of full spectrum

    Variable Number Predictive variable Accuracy/%
    No.1 No.2 No.3 No.4
    No.1 15 9 6 0 0 60
    No.2 15 0 14 1 0 93.3
    No.3 15 0 0 15 0 100
    No.4 15 0 0 0 15 100
    下载: 导出CSV
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  • 收稿日期:  2018-09-28
  • 修回日期:  2018-11-19
  • 刊出日期:  2019-08-01

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