Spectroscopy and Spectral Analysis, Volume. 33, Issue 6, 1537(2013)
Study on Near Infrared Spectroscopy of Transgenic Soybean Identification Based on Principal Component Analysis and Neural Network
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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WU Jiang, HUANG Fu-rong, HUANG Cai-huan, ZHANG Jun, CHEN Xing-dan. Study on Near Infrared Spectroscopy of Transgenic Soybean Identification Based on Principal Component Analysis and Neural Network[J]. Spectroscopy and Spectral Analysis, 2013, 33(6): 1537
Received: Jul. 5, 2012
Accepted: --
Published Online: Jun. 7, 2013
The Author Email: Jiang WU (furong_huang@163.com)