Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data

Odorous contaminants in wheat have been detected using a conducting polymer array. A radial basis function artificial neural network (RBFann) was used to correlate sensor array responses with human grading of off-taints in wheat. Wheat samples moulded by artificial means in the laboratory were used to evaluate the network, operating in quantitative mode, and also to develop strategies for evaluating real samples. Commercial wheat samples were then evaluated using the RBFann as a classifier network with great success, achieving a predictive success of 92.3% with no bad samples misclassified as good in a 40-sample population (24 good, 17 bad) using a training set of 92 samples (72 good, 20 bad).