Distinguish Fritillaria cirrhosa and nonFritillaria cirrhosa using laser-induced breakdown spectroscopy  

作　　者：Kai WEI Xutai CUI Geer TENG Mohammad Nouman KHAN Qianqian WANG 魏凯;崔旭泰;腾格尔;Mohammad Nouman KHAN;王茜蒨(School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,People's Republic of China;Key Laboratory of Photonic Information Technology,Ministry of Industry and Information Technology,Beijing Institute of Technology,Beijing 100081,People's Republic of China)

机构地区：[1]School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,People's Republic of China [2]Key Laboratory of Photonic Information Technology,Ministry of Industry and Information Technology,Beijing Institute of Technology,Beijing 100081,People's Republic of China

出　　处：《Plasma Science and Technology》2021年第8期161-166,共6页等离子体科学和技术（英文版）

基　　金：supported by National Natural Science Foundation of China(No.62075011);Graduate Technological Innovation Project of Beijing Institute of Technology(No.2019CX20026)。

摘　　要：As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ)was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.We also studied the performance of linear discriminant analysis,and support vector machine on the same data set.Among these three classifiers,LVQ had the highest correct classification rate of 99.17%.The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.

关 键 词：laser-induced breakdown spectroscopy(LIBS) learning vector quantization chemometric models robustness of model 

分 类 号：R575.2[医药卫生—消化系统] O657.38[医药卫生—内科学]