Fast discrimination of apple varieties using Vis/NIR spectroscopy

被引：47

作者：

He, Yong ^{[1
]}

Li, Xiaoli ^{[1
]}

Shao, Yongni ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310029, Peoples R China

来源：

INTERNATIONAL JOURNAL OF FOOD PROPERTIES | 2007年 / 10卷 / 01期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

visible/near infrared spectroscopy (Vis/NIRS); apple; wavelet transform (WT); principal component analysis (PCA); artificial neural network (ANN); discrimination;

D O I：

10.1080/10942910600575666

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

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 chemometries 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.

引用

页码：9 / 18

页数：10

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