Discrimination of Apples Using Near Infrared Spectroscopy and Sorting Discriminant Analysis

Near infrared spectra of apples contain the most useful information of the soluble solids content and firmness of apples. A new feature extraction method, called sorting discriminant analysis, was proposed to use a sorting method based on principal component analysis and linear discriminant analysis to extract the features of near infrared spectra. The objective of this research was to make use of feature extraction methods, such as principal component analysis, linear discriminant analysis, discriminant partial least squares, and sorting discriminant analysis to extract information from near infrared spectra of the “Huaniu” apples and the “Fuji” apples. After feature extraction, the nearest neighbor classifier was used to classify the apples, and the classification results were compared to study that which feature extraction method performed best. The experimental results showed principal component analysis + linear discriminant analysis and sorting discriminant analysis could extract discriminant information from near infrared spectra of apples better than principal component analysis and discriminant partial least squares, and sorting discriminant analysis was the best one. Sorting discriminant analysis can not only compress the high-dimensional near infrared spectra to the low-dimensional data but also project near infrared spectra to a new feature space where the data can be classified easily and effectively, and sorting discriminant analysis is superior to principal component analysis + linear discriminant analysis in most cases.