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

Kai WEI, Xutai CUI, Geer TENG, Mohammad Nouman KHAN, Qianqian WANG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number085507
JournalPlasma Science and Technology
Volume23
Issue number8
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Chemometric models
  • Laser-induced breakdown spectroscopy (LIBS)
  • Learning vector quantization
  • Robustness of model

Fingerprint

Dive into the research topics of 'Distinguish Fritillaria cirrhosa and non- Fritillaria cirrhosa using laser-induced breakdown spectroscopy'. Together they form a unique fingerprint.

Cite this