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Journal of Spectroscopy
Volume 2018, Article ID 2689750, 8 pages
Research Article

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis

1Shaanxi Key Laboratory of Smart Grid & the State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Correspondence should be addressed to Yan Zhou; nc.ude.utjx.liam@uohz.nay

Received 11 July 2017; Accepted 25 October 2017; Published 10 January 2018

Academic Editor: Vincenza Crupi

Copyright © 2018 Xiaoyan Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.