| 光谱学与光谱分析 |
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| Study on Near Infrared Spectroscopy of Transgenic Soybean Identification Based on Principal Component Analysis and Neural Network |
| WU Jiang1, HUANG Fu-rong1*, HUANG Cai-huan2, ZHANG Jun1, CHEN Xing-dan1, 3 |
1. Opto-Electronic Department of Jinan University, Guangzhou 510632, China
2. Department of Food Science and Engineering of Jinan University, Guangzhou 510632, China
3. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
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Abstract In order to explore a rapid identification method for transgenic soybeans, non-transgenic and transgenic soybeans were tested as the experimental samples via near infrared spectroscopy (NIR) and principal component analysis (PCA) combined with back propagation artificial neural network (BP-ANN) model. The spectrum data was collected after NIRS scanning the samples, and then analyzed by PCA plus BP-ANN model. The accumulative reliabilities of the six components were 99. 03% through the PCA. Then BP-ANN model was used to further test these six components and a three-layer BP-ANN model was developed. The final result achieved a 100% recognition rate of all 22 test samples respectively. In conclusion, the measure of NIRS and PCA combined with BP-ANN model has proved to be a rapid and accurate method to detect transgenic soybean nondestructively.
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Received: 2012-07-05
Accepted: 2012-10-07
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Corresponding Authors:
HUANG Fu-rong
E-mail: furong_huang@163.com
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