¹College of Information Engineering, Zhengzhou University, Zhengzhou, PR China
²College of Grain Oil and Food Science, Henan University of Technology, Zhengzhou, PR China

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Abstract—A new hybrid algorithm named EODT-LS-SVR based on least squares support vector regression (LS-SVR) with wavelet-based EODT algorithm as preprocessed tools is proposed for removing the interferences and developing the quantitative models with high precision in near-infrared (NIR) spectra. EODT-LS-SVR algorithm is composed of two steps. In the first step, the preprocessing algorithm named EODT, which combines the ideas of wavelet packet transform (WPT), orthogonal signal correction (OSC) and information theory, is employed for the characteristic extraction of analyte information through multi-scale analysis. Entropy-based baseline signal removing (EBSR) algorithm is applied to remove the baseline of the spectra based on information theory with WPT-based analysis, and then the information orthogonal to the concentrations of analyte is removed by OSC algorithm in each frequency band of spectra. In the second step, LS-SVR method coupled with grid search and particle swarm optimization (PSO) technique for parameters optimization is used to enhancing the quality of regression models. EODT-LS-SVR algorithm was validated by two NIR spectral datasets, one used for measuring the fat concentration of milk and the other used for measuring the oil content of corn. The comparison of prediction results demonstrated that the performance of calibration models developed by EODT-LSSVR algorithm is better than that developed by other conventional algorithms, showing the high efficiency and the high quality for quantitative model development in NIR spectra of complex samples.

Index Terms—Quantitative analysis, Entropy, Least squares support vector regression, Interference information, Near-infrared spectroscopy, multi-scale analysis.

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Cite: Yingqiang Ding, Dan Peng, "Quantitative Analysis of Near-Infrared Spectra by Wavelet-based Interferences Removal and Least Squares Support Vector Regression," Journal of Computers vol. 7, no. 4, pp. 880-889, 2012.