Discriminating varieties of waxberry using near infrared spectra

A new nondestructive method for discriminating varieties of waxberry by visible and near infrared spectroscopy (Vis/NIRS) was developed. First, the spectral data were analyzed by principal component analysis (PCA) for varieties clustering. Then diagnostic information was obtained from original spectra, these informations were used for pattern recognition based on ANN model. The score plot provided the reasonable clustering of the varieties of waxberry. Small quantities of principal components from PCA were used as inputs of a back propagation neural network (BPNN) with one hidden layer. 100 samples were selected randomly from four varieties, then they were used to build BPNN model. This model had been used to predict the varieties of 20 unknown samples. The recognition rate of 95% was achieved. This model is reliable and practicable. So this method could offer a new approach to the fast discriminating varieties of waxberry.