中国中药杂志

2019, v.44(24) 5375-5381

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基于电子鼻气味指纹图谱与XGBoost算法鉴别姜黄属中药
Identification of Curcuma herbs using XGBoost algorithm in electronic nose odor fingerprint

拱健婷;王佳宇;李莉;徐东;丛悦;关佳莉;吴浩忠;邹慧琴;闫永红;
GONG Jian-ting;WANG Jia-yu;LI Li;XU Dong;CONG Yue;GUAN Jia-li;WU Hao-zhong;ZOU Hui-qin;YAN Yong-hong;Beijing Institute of Chinese Medicine;Changchun Medical College;School of Chinese Pharmacy,Beijing University of Chinese Medicine;

摘要(Abstract):

针对姜黄属中药的鉴别问题,通过电子鼻采集姜黄属郁金、莪术、姜黄、片姜黄4味中药的气味指纹图谱,应用XGBoost算法对中药的气味特征进行学习,并建立快速有效的判别模型;以准确率、精确率、召回率、F度量为指标评估XGBoost的性能。实验结果表明XGBoost建立的判别模型对训练集中166个样本和测试集中69个样本的回代正判率分别为99. 39%,95. 65%,能准确判别姜黄属4种中药;对XGBoost判别模型的贡献度排在前四位的传感器依次为LY2/g CT,P40/1,LY2/Gh,LY2/LG,贡献度最低的传感器是T70/2; XGBoost判别模型预测集准确率、精确率、召回率、F度量分别为95. 65%,95. 25%,93. 07%,93. 75%,均优于传统的支持向量机、随机森林、神经网络,验证了XGBoost在姜黄属中药鉴别中的优越性。电子鼻气味指纹图谱结合XGBoost建立的判别模型可以实现姜黄属中药郁金、莪术、姜黄、片姜黄的快速准确鉴别,为中药智能鉴别提供一种快速、可靠而有效的分析方法; XGBoost算法的引入也提示可将更多性能优异的算法引入到中药领域,为中药气味指纹图谱的数据挖掘提供更多途径。
This article aims to identify four commonly applied herbs from Curcuma genus of Zingiberaceae family,namely Curcumae Radix( Yujin),Curcumae Rhizoma( Ezhu),Curcumae Longae Rhizoma( Jianghuang) and Wenyujin Rhizoma Concisum( Pianjianghuang). The odor fingerprints of those four herbal medicines were collected by electronic nose,respectively. Meanwhile,XGBoost algorithm was introduced to data analysis and discriminant model establishment,with four indexes for performance evaluation,including accuracy,precision,recall,and F-measure. The discriminant model was established by XGBoost with positive rate of returning to 166 samples in the training set and 69 samples in the test set were 99. 39% and 95. 65%,respectively. The top four of the contribution to the discriminant model were LY2/g CT,P40/1,LY2/Gh and LY2/LG,the least contributing sensor was T70/2. Compared with support vector machine,random forest and artificial neural network,XGBoost algorithms shows better identification capacity with higher recognition efficiency. The accuracy,precision,recall and F-measure of the XGBoost discriminant model forecast set were 95. 65%,95. 25%,93. 07%,93. 75%,respectively. The superiority of XGBoost in the identification of Curcuma herbs was verified. Obviously,this new method could not only be suitable for digitization and objectification of traditional Chinese medicine( TCM) odor indicators,but also achieve the identification of different TCM based on their odor fingerprint in electronic nose system. The introduction of XGBoost algorithm and more excellent algorithms provide more ideas for the application of electronic nose in data mining for TCM studies.

关键词(KeyWords): XGBoost;电子鼻;气味指纹图谱;姜黄属;郁金;姜黄;莪术;片姜黄
XGBoost;electronic nose;odor fingerprint;Curcuma herbs;Yujin;Jianghuang;Ezhu;Pianjianghuang

Abstract:

Keywords:

基金项目(Foundation): 北京中医药科技发展资金项目(QN2018-20);; 国家自然科学基金项目(81573542);; 北京中医药大学自主选题项目(2019-JYB-JS-006)

作者(Author): 拱健婷;王佳宇;李莉;徐东;丛悦;关佳莉;吴浩忠;邹慧琴;闫永红;
GONG Jian-ting;WANG Jia-yu;LI Li;XU Dong;CONG Yue;GUAN Jia-li;WU Hao-zhong;ZOU Hui-qin;YAN Yong-hong;Beijing Institute of Chinese Medicine;Changchun Medical College;School of Chinese Pharmacy,Beijing University of Chinese Medicine;

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