| 光谱学与光谱分析 |
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| Fast Discrimination of Varieties of Transgene Wheat Based on Biomimetic Pattern Recognition and Near Infrared Spectra |
| ZHAI Ya-feng1, SU Qian2, WU Wen-jin2, HE Zhen-tian3, ZHANG Zong-ying3, AN Jia-shuang1, DONG Jin1, DENG Xin1, HAN Cheng-gui1, YU Jia-lin1, LI Da-wei1, CHEN Xiu-lan3, AN Dong2* |
1. College of Biological Sciences, China Agricultural University, Beijing 100193, China
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
3. The Agricultural Science Institute of Jiangsu Lixiahe, Yangzhou 225002, China |
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Abstract A new method for the fast discrimination of varieties of transgene wheat by means of near infrared spectroscopy and biomimetic pattern recognition (BPR) was proposed and the recognition models of seven varieties of transgene wheat and two varieties of acceptor wheat were built. The experiment adopted 225 samples, which were acquired from nine varieties of wheat. Firstly, a field spectroradiometer was used for collecting spectra in the wave number range from 4 000 to 12 000 cm-1. Secondly, the original spectral data were pretreated in order to eliminate noise and improve the efficiency of models. Thirdly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 97.28%. Finally, the recognition models were established based on BPR. For the every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 200 samples of the other varieties were used as the second testing set. As the 96.7% samples in the second set were correctly rejected, the average correct recognition rate of first testing set was 94.3%. The experimental results demonstrated that the recognition models were effective and efficient. In short, it is feasible to discriminate varieties of transgene wheat based on near infrared spectroscopy and BPR.
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Received: 2009-06-02
Accepted: 2009-09-06
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Corresponding Authors:
ZHAI Ya-feng
E-mail: andong@semi.ac.cn
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