Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy

A relationship was established between the organic matter content in soils determined by conventional chemical measurements and by diffuse reflectance spectra in the near infrared region (1000-2500 nm). Radial basis function networks (RBFN) with regularized forward selection to control the model complexity were used for non-parametric regression, resulting in a RMSEP of 0.25%. The observed results using RBFN were better than those obtained by partial least squares regression (PLS) and multi-layer perceptron (MLP) feed-forward networks with a back-propagation learning algorithm. RBFN is a suitable tool to model this complex system, with additional advantages over MLP, since the training procedure is less dependent on the initial conditions.