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Sensors 2012, 12(12), 17234-17246; doi:10.3390/s121217234
Article

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

1
,
1
,
1,2,3,*  and 1,2,3
1 College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China 2 Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China 3 Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, 866 Yuhangtang Road, Hangzhou 310058, China
* Author to whom correspondence should be addressed.
Received: 12 October 2012 / Revised: 27 November 2012 / Accepted: 10 December 2012 / Published: 12 December 2012
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Abstract

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds.
Keywords: maize seed; variety identification; hyperspectral imaging; principal component analysis; kernel principal component analysis; gray-level co-occurrence; least squares-support vector machine; back propagation neural network maize seed; variety identification; hyperspectral imaging; principal component analysis; kernel principal component analysis; gray-level co-occurrence; least squares-support vector machine; back propagation neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, X.; Liu, F.; He, Y.; Li, X. Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds. Sensors 2012, 12, 17234-17246.

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