Quantitative Analysis of the Varieties of Apple Using Near Infrared Spectroscopy by Principal Component Analysis and BP Model

摘要

Artificial neural networks (ANN) combined with PCA are being used in a growing number of applications. In this study, the fingerprint wavebands of apple were got through principal component analysis (PCA). The 2-dimensions plot was drawn with the scores of the first and the second principal components. It appeared to provide the best clustering of the varieties of apple. The several variables compressed by PCA were applied as inputs to a back propagation neural network with one hidden layer. This BP model had been used to predict the varieties of 15 unknown samples; the recognition rate of 100% was achieved. This model is reliable and practicable. So a PCA-BP model can be used to exactly distinguish the varieties of apple.