Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum

The capacity of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict deoxynivalenol (DON) accumulation in barley seeds contaminated with Fusarium culmorum under different conditions has been assessed. Temperature (20–28 °C), water activity (0.94–0.98), inoculum size (7–15 mm diameter), and time were the inputs while DON concentration was the output. The dataset was used to train, validate and test many ANNs. Minimizing the mean-square error (MSE) was used to choose the optimal network. Single-layer perceptrons with low number of hidden nodes proved better than double-layer perceptrons, but the performance depended on the training algorithm. The RBFN reached lower errors and better generalization than MLP-ANN but they required a high number of hidden nodes. Accurate prediction of DON accumulation in barley seeds by F. culmorum was possible using MLP-ANNs or RBFNs.