Application of Probabilistic Neural Networks in Qualitative Analysis of Near Infrared Spectra: Determination of Producing Area and Variety of Loquats

Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats ('Dahongpao' and 'Jiajiaozhong') picked from two producing areas of 'Tangxi' and 'Cunan' in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA-PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA-PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA-PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties.