Data fusion of near-infrared and mid-infrared spectra for identification of rhubarb

被引：89

作者：

Sun, Wenjuan ^{[1
]}

Zhang, Xin ^{[1
]}

Zhang, Zhuoyong ^{[1
]}

Zhu, Ruohua ^{[1
]}

机构：

[1] Capital Normal Univ, Dept Chem, Beijing 100048, Peoples R China

来源：

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY | 2017年 / 171卷

基金：

中国国家自然科学基金;

关键词：

Rhubarb; Near-infrared; Mid-infrared; Data fusion; PARTIAL LEAST-SQUARES; PERFORMANCE LIQUID-CHROMATOGRAPHY; DISCRIMINANT-ANALYSIS; SPECTROSCOPY; SPECTROMETRY; SAMPLES;

D O I：

10.1016/j.saa.2016.07.039

中图分类号：

O433 [光谱学];

学科分类号：

0703 ; 070302 ;

摘要：

Rhubarb has different medicinal efficacy to official rhubarb and may affect the clinical medication safety. In order to guarantee the quality of rhubarb, we established a method to distinguish unofficial rhubarbs. 52 official and unofficial rhubarb samples were analyzed using near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy for classification. The feature vectors, which were selected by wavelet compression (WC) and interval partial least squares (iPLS) from NIR, MIR spectra, were fused together for identifying rhubarb samples. Partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogies (SIMCA), support vector machine (SVM) and artificial neural network (ANN) were compared for classifying rhubarb. The use of data fusion strategies improved the classification model and allowed correct classification of all the samples. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：72 / 79

页数：8

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