The sounds emitted during chewing of dry crisp snacks could be successfully applied to distinguish different snack types, using FFT and multivariate data analysis techniques. The classification was improved by taking the logarithm of the power spectra for further analysis. Different people produced different sound spectra, which makes recalibration of the model necessary when a new chewer is used as 'measuring instrument'. Multi-way models distinguished better between chewing sounds of different snack types than PCA on bite or chew separately and than unfold PLS. From all three-way models applied, N-PLS with 3 components showed the best classification capabilities, resulting in classification errors of 14-18%. The major amount of incorrect classifications was due to one type of potato chips that had a very irregular shape, resulting in a wide variation of the emitted sounds
