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。

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.