LIBS中药材石斛等级识别研究

doi:10.3964/j.issn.1000-0593(2020)03-0941-04

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摘要：石斛是一种常用的中药材，经常使用新鲜的或干燥的茎条入药，有益胃生津、滋阴清热的效果。近年来，药理学研究探索出石斛具有抗白内障、抗氧化、抗肿瘤、提高免疫力的作用，其在许多病例中疗效显著，引起了国内外学者的关注，然而不同时间采集的石斛中氨基酸、微量元素等含量各不同，其对应药用价值，价格也不同，因此石斛价格等级分辨的研究具有重要意义。为快速鉴别不同价格、不同药效的石斛，研究了随机森林分类模型结合激光诱导击穿光谱技术(LIBS)对石斛价格等级进行分析建模。选取5个等级的石斛样品进行建模，为了对样品进行精确稳定分析，所有石斛样品均通过粉碎压片减小实验误差。采用1 064 nm波长的Nd∶YAG脉冲激光器作为激发光源，设置激光脉冲能量50 mJ，探测延时1 μs，采集五个等级石斛样本的光谱数据，每个等级的样本采集40组光谱，共200组数据，并采用归一化处理，使所有的光谱数据转换到-1～1之间。采用归一化处理后的光谱数据进行主成分分析，通过主成分分析获得前7个主成分的得分矩阵，其累计解释95.24%的光谱信息。将选取的7个主成分作为输入，建立波段为220~880 nm的随机森林鉴别模型。并将石斛样本编号打乱，任意选取50%的光谱数据作为训练集，剩下50%的光谱数据作为测试集，默认决策树个数n_tree为500，分裂属性集中属性个数m_try为5，建立模型对不同等级的石斛进行分类。等级一、二、三、四、五的识别率分别为95.45%，100%，78.26%，94.12%和85%，平均识别率为90.57%。为提高识别率，研究了不同的n_tree和m_try对分类模型的影响，利用袋外数据误差率估计对随机森林的两个参数进行了优化。选择n_tree为300，m_try为1，等级一、二、三、四、五的识别率分别为100%，100%，92.31%，100%和90%，平均识别率为96.46%，识别率提高了5.89%。综上所述，采用LIBS技术结合优化后的随机森林模型鉴别石斛等级具有一定的可行性，为未来快速鉴定不同价格的石斛等级分类提供了可行性的判别系统。

关键词：中药材石斛；LIBS；随机森林；等级识别

Abstract：Dendrobium is a commonly used Chinese herbal medicine, often using fresh or dry stems into the medicine, beneficial to the stomach, nourishing yin and clearing heat. In recent years, pharmacological studies have found that Dendrobium has the functions of anti-cataract, anti-oxidation, anti-tumor and improving immunity. It has remarkable effects in many cases, which has attracted the attention of scholars at domestic and abroad. However, the contents of amino acids and trace elements in Dendrobium collected at different times are different, and their medicinal value and price are different. So the study of price grade discrimination of Dendrobium is of great significance. In order to quickly identify Dendrobium with different price and efficacy, the random forest classification modela combined with laser induced breakdown spectroscopy (Laser-induced Breakdown Spectroscopy, LIBS) was developed to model the price grade of Dendrobium. In this paper, five samples of Dendrobium were selected for modeling. In order to analyze the samples accurately and stably, all Dendrobium samples were pressed to reduce the experimental error. The Nd∶YAG pulse laser with 1 064 nm wavelength was used as the excitation light source, the detection delay of 50 mJ, laser pulse energy was set to 1 μs, the spectral data of five grades of Dendrobium were collected, 40 sets of spectra were collected from each grade of samples, and a total of 200 sets of data were collected. Normalized processing was used to convert all spectral data from -1 to 1. The principal component analysis (PCA) was used to analyze the normalized spectral data. The score matrix of the first seven principal components was obtained by principal component analysis, and the cumulative interpretation of the total spectral information was 95.24%. So seven principal components were selected as input, and a random forest identification model with 220～880 nm was established. The number of Dendrobium samples was disrupted, and 50% spectral data were randomly selected as training sets, and 50% spectral data were left as test sets. The default number of decision trees (n_tree) was 500, and the number of attributes in the split attribute set (m_try) was 5. The model was established to classify Dendrobium in different grades. And the recognition rates of grades one, two, three, four and five were 95.45%, 100%, 78.26%, respectively. 94.12%, 85%, with an average recognition rate of 90.57%. In order to improve the recognition rate, the influence of different n_tree and m_try on the classification model was studied, and the two parameters of the random forest were optimized by using the out-of-bag data error rate estimation. The n_tree was 300, the m_try was 1, the recognition rates of grade one, two, three, four and five were 100%, 100%, 92.31%, 100%, 90%, the average recognition rate was 96.46%, and the recognition rate was increased by 5.89%. In conclusion, it is feasible to identify the Dendrobium classification by LIBS technology combined with the optimized random forest model, which provides a feasible discrimination system for the rapid identification of Dendrobium classification with different prices in the future.

Key words：Dendrobium; Libs; Random forests; Level to identify

收稿日期: 2019-03-01 修订日期: 2019-07-14

中图分类号:

O433.4

基金资助: 重庆市留学归国人员创新创业项目（cx2017126，cx2018127）资助

通讯作者: 王金梅 E-mail: wangjm@cqupt.edu.cn

作者简介: 郑培超，1980年生，重庆邮电大学光电工程学院教授 e-mail：zhengpc@cqupt.edu.cn

引用本文:

郑培超，郑爽，王金梅，廖香玉，李晓娟，彭锐. LIBS中药材石斛等级识别研究[J]. 光谱学与光谱分析, 2020, 40(03): 941-944.
ZHENG Pei-chao, ZHENG Shuang, WANG Jin-mei, LIAO Xiang-yu, LI Xiao-juan, PENG Rui. Study on Grade Identification of Dendrobium by LIBS. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 941-944.

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