NEAR-INFRARED SPECTROSCOPY FOR CLASSIFICATION OF ORANGES AND PREDICTION OF THE SUGAR CONTENT

摘要

A nondestructive method for the classification of orange samples according to their growing conditions and geographic areas was developed using Vis/Near infrared spectroscopy. The results showed that the NIR spectra of the samples were moderately clustered in the principle component space and pattern recognition wavelet transform (WT) combined artificial neural network (BP-ANN) provided satisfactory classification results. Additionally, a partial least square (PLS) method was constructed to predict the sugar content of certain oranges. It showed excellent predictions of the sugar content of oranges, with standard error of prediction (SEP) values of 0.290 and 0.301 for Shatangju and Huangyanbendizao, respectively.