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

被引：8

作者：

Wei, Kai ^{[1
,2
]}

Cui, Xutai ^{[1
,2
]}

Teng, Geer ^{[1
,2
]}

Khan, Mohammad Nouman ^{[1
,2
]}

Wang, Qianqian ^{[1
,2
]}

机构：

[1] Beijing Inst Technol, Sch Opt & Photon, Beijing 100081, Peoples R China

[2] Beijing Inst Technol, Key Lab Photon Informat Technol, Minist Ind & Informat Technol, Beijing 100081, Peoples R China

来源：

PLASMA SCIENCE & TECHNOLOGY | 2021年 / 23卷 / 08期

基金：

中国国家自然科学基金;

关键词：

laser-induced breakdown spectroscopy (LIBS); learning vector quantization; chemometric models; robustness of model; LEARNING VECTOR QUANTIZATION; IDENTIFICATION; LIBS; CLASSIFICATION; ADULTERATION; ELEMENTS; STEEL;

D O I：

10.1088/2058-6272/ac0969

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 ; 080103 ; 080704 ;

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.

引用

页数：6

共 37 条

[1] Detection of chemical elements related to impurities leached from raw sugarcane: Use of laser-induced breakdown spectroscopy (LIBS) and chemometrics [J].