Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics
An algorithm was developed to separate pistachio nuts with closed shells from those with open shells. It was observed that upon impact on a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Two feature vectors extracted from the sound signals were Mel cepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both Mel cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed shells and open shells were used to obtain a representative vector of each class. The accuracy of closed shell nuts was more than 99% on the test set.