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Kernel-based calibration methods combined with multivariate feature selection to improve accuracy of near-infrared spectroscopic analysis

Title
Kernel-based calibration methods combined with multivariate feature selection to improve accuracy of near-infrared spectroscopic analysis
Author
정회일
Keywords
Filter method; Kernel partial least squares regression; Kernel support vector regression; Variable importance in projection score; Weight vector coefficient
Issue Date
2015-10
Publisher
ELSEVIER SCIENCE BV
Citation
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v. 147, Page. 139-146
Abstract
We present kernel-based calibration models combined with multivariate feature selection for complex quantitative near-infrared (NIR) spectroscopic analysis of three different types of sample. Because the spectra include hundreds of features (variables), an optimal selection of features that provide relevant information for target analysis improves the accuracy of spectroscopic analysis. For this purpose, we combined feature selection with kernel partial least squares regression and kernel support vector regression (K-SVR) by evaluating ranking of the features based on their variable importance in projection scores and weight vector coefficients, respectively. Then, the methods were applied to identify components in three datasets of NIR spectra. The kernel-based models without feature selection and the kernel-based models with other feature selection methods were also used for comparison. K-SVR combined with feature selection was effective when the spectral features of samples were complex and recognition of minute spectral variation was necessary for modeling. The combination of feature selection and kernel calibration model can improve the accuracy of spectral analysis by keeping optimal features. (C) 2015 Elsevier B.V. All rights reserved.
URI
http://www.sciencedirect.com/science/article/pii/S0169743915002002?via%3Dihubhttp://hdl.handle.net/20.500.11754/28210
ISSN
0169-7439; 1873-3239
DOI
10.1016/j.chemolab.2015.08.009
Appears in Collections:
COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > CHEMISTRY(화학과) > Articles
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