Journal
INTERNATIONAL JOURNAL OF FOOD PROPERTIES
Volume 12, Issue 3, Pages 644-658Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10942910801992991
Keywords
Vis; NIR spectroscopy; Orange; PCA; PLS; WT; BP-ANN
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Funding
- National Science and Technology Support Program of China [2006BAD10A0403]
- Zhejiang Provincial Natural Science Foundation of China [Y307158]
- Science and Technology Department of Ningbo [2008C10037]
- Scientific Research Fund of Zhejiang Provincial Education Department [20071064]
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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.
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