Pearson, T.Çetin, A. EnisTewfik, A. H.Haff, R. P.2015-07-282015-07-282007-051051-2004http://hdl.handle.net/11693/11422A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.EnglishNeural networkSpectral analysisInsect damage kernelsSortingAcoustic emissionsArticle10.1016/j.dsp.2005.08.0021095-4333