Classficiation of Bupleuri Radix according to Geographical Origins using Near Infrared Spectroscopy (NIRS) Combined with Supervised Pattern Recognition
10.20307/nps.2018.24.3.164
- Author:
Dong Young LEE
1
;
Kyo Bin KANG
;
Jina KIM
;
Hyo Jin KIM
;
Sang Hyun SUNG
Author Information
1. College of Pharmacy and Research Institute of Pharmaceutical Science, Seoul National University, Seoul 151-742, Republic of Korea. shsung@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Near infrared spectroscopy;
Bupleuri Radix;
Geographical classification;
Supervised pattern recognition
- MeSH:
Classification;
Principal Component Analysis;
Quality Control;
Spectrum Analysis;
Support Vector Machine
- From:Natural Product Sciences
2018;24(3):164-170
- CountryRepublic of Korea
- Language:English
-
Abstract:
Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.
