Fast Discrimination of Apple Varieties Using Vis/NIR Spectroscopy

Abstract

We evaluated the potential of visible/near-infrared (Vis/NIR) spectroscopy for its ability to nondestructively differentiate apple varieties. The apple varieties used in this research included, Fuji apples, Red Delicious apples, and Copefrut Royal Gala apples. The chemometrics procedures applied to the Vis/NIR data were principal component analysis (PCA), wavelet transform (WT), and artificial neural network (ANN). The apple varieties could be qualitatively discriminated in the PC1-PC2 space resulted from PCA. Wavelet transform was used as a tool for dimension reduction and noise removal, reducing spectral to wavelet components. Wavelet components were utilized as input for three-layer back propagation ANN model. WT-ANN model gave the highest level of correct classification (100%) of the apple varieties.

Keywords:

• Visible/Near infrared spectroscopy (Vis/NIRS)
• Apple
• Wavelet transform (WT)
• Principal component analysis (PCA)
• Artificial neural network (ANN)
• Discrimination

ACKNOWLEDGMENTS

This study was supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, P. R. C., Natural Science Foundation of China, Specialized Research Fund for the Doctoral Program of Higher Education (Project No: 20040335034), and Natural Science Foundation of Zhejiang (Project No: RC02067).