Maximum likelihood estimation based regression for multivariate calibration.

In this paper, we propose a maximum likelihood estimation based regression (MLER) model for multivariate calibration. The proposed MLER method seeks for the maximum likelihood estimation (MLE) solution of the least-squares problem, and it is much more robust to noise or outliers and accurate than the traditional least-squares method. An efficient iteratively reweighted least squares technique is proposed to solve the MLER model. As a result, our model can obtain accurate spectra-concentrate relations. Experimental results on three real near-infrared (NIR) spectra data sets demonstrate that the proposed MLER model is much more efficacious and effective than state-of-the-art partial least squares (PLS) methods.