Acoustic Testing of Snack Food Crispness Using Neural Networks

The crispness of selected snack foods, Pringles potato chips, Paprika extruded snack and Munchy crackers, was evaluated by crushing the snack samples with a pair of pincers, and analyzing the frequency domain spectra of acoustic patterns by the neural networks (NNs). The inputs for training the NNs comprised 102 amplitudes of sound signals in 0–7 kHz frequency range at the intervals of about 69 Hz with moisture content or crispness grades as outputs. Both backpropagation (BPNN) and probabilistic (PNN) models showed good performance in classifying the snack foods into four grades of sensory crispness. The prediction accuracy of PNN models ranged approximately from 96 to 98% and was higher than the accuracy of BPNN models by about 10 to 25%. Results showed that frequency domain spectra of acoustic signals could be successfully analyzed by the NNs for predicting the crispness of snack food products.