Non-destructive prediction of pork meat degradation using a stacked autoencoder classifier on hyperspectral images

This work presents initial results on a multitemporal hyperspectral image analysis method to evaluate the time degradation of pork meat. The proposed method is inexpensive and practically non-destructive. The hyperspectral data is analyzed and the relevant information is reduced to the information in only three wavelengths. The analysis is performed by a binary classifier composed by two stacked autoencoders and a softmax output layer. The use of autoencoders reduces tenfold the dimension of the input space. The proposed classifier has led to 97.2% of correct decisions, which indicates the great potential of the methodology to monitor the safety of meat.