Acoustic Wave Analysis for Food Crispness Evaluation

Sound signals for five snack food products at two moisture levels were recorded digitally and product crispness was evaluated by a trained sensory panel. Sound signal features were extracted by analyzing signal‐value and power‐value dependencies. Principal component regression and neural network techniques were used to determine the usefulness of the sound signal features as predictors of sensory crispness. In a validation test, a trained neural network model predicted sensory crispness from sound signal features to an R 2 ‐value of 0.89. The results show the effectiveness of the techniques employed to extract and use sound signal features for crispness evaluation.