Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

被引:24
|
作者
Wang, Yong [1 ,2 ]
Mei, Minghua [2 ]
Ni, Yongnian [1 ,2 ]
Kokot, Serge [3 ]
机构
[1] Nanchang Univ, State Key Lab Food Sci & Technol, Nanchang 330047, Peoples R China
[2] Nanchang Univ, Dept Chem, Nanchang 330031, Peoples R China
[3] Queensland Univ Technol, Fac Sci & Engn, Sch Chem Phys & Mech Engn, Brisbane, Qld 4001, Australia
关键词
Near- and mid-infrared spectroscopy; Medicinal and adulterated samples; Illicium verum Hook. F. and luicium lanceolatum AC Smith; Chemometrics; SUCCESSIVE PROJECTIONS ALGORITHM; PERFORMANCE LIQUID-CHROMATOGRAPHY; INFRARED REFLECTANCE SPECTROSCOPY; CHEMICAL-COMPONENTS; GAS-CHROMATOGRAPHY; VARIABLE SELECTION; STAR ANISE; SAMPLES; SPECTRA; AID;
D O I
10.1016/j.saa.2014.04.062
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, luicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95-100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:539 / 545
页数:7
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