In this paper, we propose a novel approach for the estimation of the concentration of chemical components through spectrophotometric measurements. It is based on the exploitation of the whole spectral information available in the original spectral data space by means of a Multiple Regression System (MRS) whose design is performed in three successive steps. The first one aims at a simple partitioning of the original spectral data space into subspaces of reduced dimensionality. The second step consists in training a (linear or nonlinear) regression method in each of the subspaces obtained in the previous step. In the third and final step, the estimates provided by the ensemble of regressors are combined in order to produce a global estimate of the concentration of the chemical component of interest. For this purpose, two linear and one nonlinear combination strategies are explored.

The experimental assessment of the MRS was carried out on two different datasets: 1) a wine dataset for the determination of alcohol concentration by mid-infrared spectroscopy; and 2) an orange juice dataset where near-infrared reflectance spectroscopy is used to estimate the saccharose concentration. The obtained results suggest that the proposed MRS approach represents a promising alternative to the traditional regression methods.