Spectra Data Analysis and Calibration Modeling Method Using Spectra Subspace Separation and Multiblock Independent Component Regression Strategy

In this article, a spectra data analysis and calibration modeling approach is proposed for the estimation of the concentration of sources species in chemical mixture. Based on the multiplicity of underlying spectra characteristics, it designs spectra subspace separation and multiblock independent component regression modeling strategy. It is performed in two steps: The first step aims at an automatic partition of the original wavelength space into different spectra subspaces to reveal the changes of underlying spectra information. In different spectra subspaces, each being well fitted by one independent component analysis (ICA) model, it better explores the existing chemical constituent species of interest. In the second step, multiblock regression system is designed for concentration estimation. The advantage is mainly to allow for easier interpretation and enhanced understanding by zooming into different smaller specific segments and thus well tracking the wavelength-varying effects on qualities. It is theoretically and experimentally illustrated that the proposed method can result in better predictive power compared with standard ICR (SICR) modeling focusing on the full-range wavelength. (C) 2010 American Institute of Chemical Engineers AIChE J, 57: 1202-1215, 201