Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apple samples

In this paper, near infrared (NIR) spectroscopy combined with pattern recognition methods was used in an attempt to classify different types of apple samples. Three pattern recognition methods such as K-nearest neighbour (KNN), partial least-squares discriminant analysis (PLSDA) and moving window partial least-squares discriminant analysis (MWPLSDA) were used to classify apple samples of different geographical origins, grades and varieties. The result indicates that MWPLSDA is superior to these two conventional pattern recognition methods. Because MWPLSDA method can select narrow but informative wavelength intervals to reconstruct an efficacious classification model with high predicting accuracy. In conclusion, MWPLSDA coupled with near-infrared fibre-optic technology is proved to be an effective method for fruit classification.