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摘要: 不同类型的烟草在元素种类和元素含量上存在一定的差异，本文基于激光诱导击穿光谱（LIBS）技术，采集了不同种类烟草的原子发射光谱，并结合支持向量机方法，实现了烟草的快速分类鉴别。文章选取了市面上9种不同品牌的香烟，提取了其烟丝LIBS谱线的全部特征峰，通过对全谱进行窗口平滑去背景和峰位漂移的修正等预处理，再进行主成分分析降维，结合支持向量机方法（SVM），建立了分类模型，给出了9种品牌香烟烟草的分类结果，平均准确度达到97.47%。实验结果表明：激光诱导击穿光谱技术在烟草防伪鉴定和现场快速识别分类等方面具有巨大的应用潜力。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.
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图 5 每个主成分得分和主成分累积得分
Figure 5. Each principal component score and principal component cumulative score
图 6 9种香烟前3个主成分的散点分布图
Figure 6. Scatter diagram of the first three principal components for 9 types of cigarettes
表 1 9种香烟的理化参数
Table 1. The physical and chemical parameters of 9 kinds of cigarettes
Brand Production place Tar content/mg CO content/mg Nicotine content/mg Esse Korea 4 - 0.3 HM Hainan 10 12 0.8 DQM Shanghai 10 12 0.8 JMT Henan 10 11 0.8 YY Taiyuan 10 12 1.1 ZNH Beijing 5 6 0.4 HHL Hubei 11 13 1.1 FRW Hunan 11 11 1.2 ZH Shanghai 11 11 1 
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表 2 测试集准确率
Table 2. Test set accuracy
Esse ZH ZNH YY DQM HM FRW HJY HHL Accuracy HHL 4.55% 95.45% 95.45% HJY 100% 100% FRW 100% 100% HM 100% 100% DQM 95.45% 4.55% 95.45% YY 100% 100% ZNH 100% 100% ZH 86.36% 13.64% 86.36% Esse 100% 100%
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