基于波段选择的拉曼光谱血痕鉴别

doi:10.3964/j.issn.1000-0593(2021)10-3137-05

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摘要：血痕的种属鉴别在刑事技术和检验检疫等领域有重要的实践意义，拉曼光谱技术为血痕种属鉴别提供了思路。实验采集人血及猪、鸡、鸭、牛、鼠5种动物的血样并获取其拉曼光谱，采用Savitzky-Golay方法平滑降噪，airPLS方法进行基线校正，选取100~1 700 cm^-1波段进行实验。训练集有600组数据，测试集有300组拉曼光谱数据。第一部分实验对比了PLS-DA，LDA，PCA+LDA，SVM和PCA+SVM等方法，测试集准确率分别为84.0%，49.3%，78%，83.0%和85.7%，验证了降维算法结合SVM分类器的有效性。第二部分采用互信息算法、遗传算法和等间隔组合三种波段选择算法，结合SVM分类器做对比实验，结果显示互信息结合SVM算法的分类准确率最优，在选择波段数为50时，测试集准确率达到86.0%。在波段选择数为300时，三种波段选择算法结合SVM分类器的准确率都达到93%左右，大幅高于传统分类方法。实验结果表明，采用波段选择算法进行光谱降维，可以有效的提高算法的准确率和鲁棒性，同时使拉曼光谱种属鉴定的可解释性更强。波段选择算法确定了血痕鉴别的关键波段位置，对设计用于执法的便携式拉曼系统也有重要意义。

关键词：血痕；拉曼光谱；分类模型；波段选择

Abstract：The species identification of blood stains has important practical significance in criminal technology and inspection and quarantine. Raman spectroscopy provides an idea for the identification of bloodstain species. In this paper, human blood samples and blood samples of pig, chicken, duck, cow and mouse were collected and their Raman spectra were obtained. Savitzky-Golay method was used to smooth noise reduction, airPLS method was used for baseline correction, and 100~1 700 cm^-1 bands were selected for the experiment. The training set contained 600 sets of data, and the test set contained 300 sets of Raman spectral data. The first part of the experiment compared plS-DA, LDA, PCA+LDA, SVM and PCA+SVM. The accuracy of the test set was 84.0%, 49.3%, 78%, 83.0% and 85.7% respectively, which verified the effectiveness of the combination of the dimension-reduction algorithm and the SVM classifier. In the second part, three band selection algorithms of mutual information algorithm, genetic algorithm and equispaced combination were adopted. A comparative experiment was conducted in combination with the SVM classifier. The results showed that the combination of mutual information and the SVM algorithm had the best classification accuracy. When the number of band selection is 300, the accuracy of the three band selection algorithms combined with the SVM classifier is about 93%, which is much higher than the traditional classification method. The experimental results show that the spectral dimension reduction using a band selection algorithm can effectively improve the accuracy and robustness of the algorithm, and at the same time, make the identification of Raman spectral species more interpretable. The band selection algorithm determines the key band location of blood stain identification, which is also important for the design of a portable Raman system for law enforcement.

Key words：Blood stain; Raman spectrum; Classification model; Band selection

收稿日期: 2020-08-20 修订日期: 2020-12-20

中图分类号:

O433.4

基金资助: 国家重点研发计划项目(2018YFC0807401)，浙江省教育厅科研项目(Y201737880)，浙江警察学院项目（2020XJY015）资助

作者简介: 杨志超，1985年生, 浙江警察学院讲师 e-mail：yangzhichao@zjjcxy.cn

引用本文:

杨志超，石璐，蔡竞，张辉. 基于波段选择的拉曼光谱血痕鉴别[J]. 光谱学与光谱分析, 2021, 41(10): 3137-3141.
YANG Zhi-chao, SHI Lu, CAI Jing, ZHANG Hui. Raman Spectral Blood Stain Identification Based on Band Selection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3137-3141.

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