TY - JOUR
T1 - Distinguish Fritillaria cirrhosa and non- Fritillaria cirrhosa using laser-induced breakdown spectroscopy
AU - WEI, Kai
AU - CUI, Xutai
AU - TENG, Geer
AU - KHAN, Mohammad Nouman
AU - WANG, Qianqian
N1 - Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Chemometric models
KW - Laser-induced breakdown spectroscopy (LIBS)
KW - Learning vector quantization
KW - Robustness of model
UR - http://www.scopus.com/inward/record.url?scp=85110293977&partnerID=8YFLogxK
U2 - 10.1088/2058-6272/ac0969
DO - 10.1088/2058-6272/ac0969
M3 - Article
AN - SCOPUS:85110293977
SN - 1009-0630
VL - 23
JO - Plasma Science and Technology
JF - Plasma Science and Technology
IS - 8
M1 - 085507
ER -