Two-level independent component regression model for multivariate spectroscopic calibration

Abstract In this paper, a two-level independent component regression (ICR) model is developed for multivariate spectroscopic calibration. Compared to the traditionally used principal component regression and partial least squared regression model, the ICR model is more efficient to extract high order statistical information from the spectra data. To improve the calibration performance, an ensemble form of the ICR model is proposed. In the first level of the method, various subspaces are constructed based on the independent component decomposition of the original data space. Meanwhile, by defining a related index, the most important variables in each subspace are selected for ICR modeling, which form the second level of the proposed method. A Bayesian inference strategy is further developed for probabilistic combination of calibration results obtained from different subspaces. For performance evaluation, two case studies are carried out on a benchmark spectra dataset.

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