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

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.