Application PCA Method to Fast Discrimination of Dyed Navel Oranges Using Near Infrared Spectroscopy

The spectral data of dyed navel oranges was achieved by near infrared spectrum instrument (NIRS). The data was preprocessed by different means and analyzed with principal component analysis (PCA). A PLS model was established based on this,and the performance of the model was evauated according to root mean squared error of calibration (RMSEC) and correction coefficient (R²). Results show that:It appeared to provide PCA could be used to identify the dyed navel oranges,the recognition rate achieved 94%. RMSEC of the PLS regression model established based on PCA was 0.26,R² was 0.96,the model was well. It was feasible to identify the dyed orange by near infrared spectroscopy,which would provide a new and nondestructive method for the identification of dyed orange.