Quality Analysis of Peanut Seed by Visible/Near-Infrared Spectra

In this paper, three representative varieties of peanut seeds were selected for the experiment based on visible/near-infrared reflectance spectroscopy living in the wavelength rang from 600 to 1 100 nm. Firstly, spectral datas ware collected by the near-infrared fiber optic spectrometer, and the spectral features of the original spectral dates were extracted by the wavelet analysis. Then the principal component analysis (PCA) was used for cluster analysis of spectral features. Finally, the four principal components were applied as the inputs, the varieties category as the output and the Mahalanobis distance as the discriminant function of the recognitionmodel, so a linear discriminant analysis model was established. In the 50 samples of each varieties, 30 samples were randomly selected as the training set, and the remaining 20 samples as the predictor set. The recognitionmodel for three peanut varieties have a recognition rate of 95% on average. As the experimental results show that this method is reliable and effectively, and a new method to distinguish and discriminate the quality of peanut seeds was put forword.