摘要
激光诱导击穿光谱(laser-induced breakdown spectroscopy, LIBS)技术在爆炸物的实时和远程分析识别领域有着巨大的应用潜力。为将LIBS技术用于痕量爆炸物的快速准确检测,开展了铝合金基板上痕量爆炸物测量的模拟实验。利用1 064 nm的Nd:YAG激光器作为激发光源,烧蚀铝基板上浓度均为200μg·cm-2的黑火药、2-硝基苯甲醚、氨基甲酸乙酯和甘氨酸4种样品,获得等离子体光谱。将所采集的每种样品的100个光谱数据按7:3的比例划分为训练集与测试集进行化学计量建模分析。采用主成分分析法降低输入数据维度,再利用线性判别分析法建立光谱识别模型。在使用前30个主成分作为输入数据时,通过计算测试集与训练集样品之间的马氏距离来预测样品的所属类别,可得到测试集样品分类正确率为100%。研究结果表明,LIBS技术结合化学计量算法可准确识别铝合金基板上的痕量爆炸物,可为进一步开展痕量爆炸物的远程LIBS检测提供方法参考。
Laser-induced breakdown spectroscopy(LIBS)has been demonstrated as a candidate method to identify the bulk explosives in real time and remote analysis.To apply LIBS technology to trace explosives detection,simulation experiments of trace explosives deposited on the surface of aluminum alloy substrate are carried out.A Nd YAG laser with 1064 nm is used as the excitation light source,and plasma spectra are obtained by ablating black powder with concentrations of 200μm·cm-2 on the aluminum substrate,as well as four samples of 2-nitroanisole,ethyl carbamate,and glycine.100 spectra data of each sample collected are divided into training and testing sets in a 73 ratio for stoichiometric modeling analysis.The principal component analysis(PCA)method is employed to reduce the dimension of the input data,and then the linear discriminant analysis(LDA)is used to establish a spectral recognition model.As a result,100%classification accuracy is obtained for samples in the testing set by introducing Mahalanobis distance(MD)when the first 30 principal components extracted by PCA are used as input.The research results indicate that LIBS technology combined with stoichiometric algorithms can accurately identify trace explosives on aluminum alloy substrates,providing a method reference for further remote LIBS detection of trace explosives.
作者
谷天予
张大成
冯中琦
侯佳佳
朱江峰
GU Tianyu;ZHANG Dacheng;FENG Zhongqi;HOU Jiajia;ZHU Jiangfeng(School of Optoelectronic Engineering,Xidian University,Xi’an 710071,China)
出处
《现代应用物理》
2023年第2期104-109,130,共7页
Modern Applied Physics
基金
国家自然科学基金资助项目(U2241288,U2032136)
陕西省自然科学基础研究计划资助项目(2019JCW-03)。
关键词
激光诱导击穿光谱
爆炸物鉴别
主成分分析
线性判别分析
化学计量
laser-induced breakdown spectroscopy
explosive identification
principal component analysis
linear discriminant analysis
chemometric algorithms
