Discrimination of Radix Isatidis and Rhizoma et Radix Baphicacanthis Cusia samples by near infrared spectroscopy with the aid of chemometrics

被引:30
|
作者
Ni, Yongnian [1 ,2 ]
Song, Rongmei [1 ]
Kokot, Serge [3 ]
机构
[1] Nanchang Univ, Dept Chem, Nanchang 330031, Peoples R China
[2] Nanchang Univ, State Key Lab Food Sci & Technol, Nanchang 330047, Peoples R China
[3] Queensland Univ Technol, Fac Sci & Engn, Sch Chem Phys & Mech Engn, Brisbane, Qld 4001, Australia
基金
中国国家自然科学基金;
关键词
Near infrared spectroscopy; Wavelet transform; Chemometrics; Indigowoad Root samples; Radix Isatidis; Rhizoma et Raitx Baphicacanthis Cusia; SUCCESSIVE PROJECTIONS ALGORITHM; LEAST-SQUARES REGRESSION; VARIABLE SELECTION; GENETIC ALGORITHM; NEURAL-NETWORKS; MULTICOMPONENT ANALYSIS; SPECTRA; CLASSIFICATION; TECHNOLOGIES; PREDICTION;
D O I
10.1016/j.saa.2012.05.031
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
A novel method for the discrimination of the three kinds of Indigowoad Root sample, Radix Isatidis (RI), Rhizoma et Radix Baphicacanthis Cusia (RRBC) and simulated adulterated samples (AD) was researched and developed with the use of near infrared spectroscopy (NIR) and chemometrics. Principal component analysis (PCA) was applied to process the NIR data of 75 collected Indigowoad Root samples, and the three kinds of such sample were discriminated along the first principal component (PC1) axis. In addition, the data pretreatment methods - genetic algorithm-partial least squares (GA-PLS), successive projections algorithm (SPA), and wavelet transform (WT), were employed to select the key analytical wavelengths, and then, these were used as input variables for the three kinds of the pattern recognition method, such as K-nearest neighbor (KNN), radial basis function-artificial neural network (RBF-ANN), least squares-support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN). The WT was the method of choice for data pretreatment, and three pretreatment-prediction method combinations performed well (basis: %recognition rate) - WT-KNN (98.2%) and BP-ANN (97.3%) as well as GA-PLS - LS-SVM (97.2). A BP-ANN calibration model was built for the quantitative discrimination of the three types of the complex Indigowoad Root samples, and it was successfully validated. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:252 / 258
页数:7
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