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| 近红外光谱结合主成分分析和BP神经网络的转基因大豆无损鉴别研究 | |
| 引用本文： | 吴江,黄富荣,黄才欢,张军,陈星旦. 近红外光谱结合主成分分析和BP神经网络的转基因大豆无损鉴别研究[J]. 光谱学与光谱分析, 2013, 33(6): 1537-1541. DOI: 10.3964/j.issn.1000-0593(2013)06-1537-05 |
| 作者姓名： | 吴江 黄富荣 黄才欢 张军 陈星旦 |
| 作者单位： | 1. 暨南大学光电工程系，广东 广州 510632 2. 暨南大学食品科学与工程系，广东 广州 510632 3. 中国科学院长春光学精密机械与物理研究所，吉林 长春 130033 |
| 基金项目： | 高等学校博士学科点专项科研基金项目，广东省自然科学基金项目，广东高校优秀青年创新人才培养计划项目，中央高校基本科研业务费专项资金项目 |
| 摘 要： | 为探究无损鉴别转基因大豆的可行性，利用近红外光谱分析仪对大豆扫描得到反射光谱，应用主成分分析结合BP神经网络方法进行分析鉴别。首先应用主成分分析法，得到包含大豆99.03%的光谱信息的6个主成分，再将其作为BP神经网络的输入，对应的大豆种类作为输出，建立一个三层BP神经网络模型。该模型对于转基因大豆的正确识别率为100%，说明近红外光谱结合主成分分析和BP神经网络的方法能无损快速准确地鉴别转基因大豆。 |
| 关 键 词： | 近红外光谱 转基因大豆 主成分分析 BP神经网络 |
| 收稿时间： | 2012-07-05 |
Study on Near Infrared Spectroscopy of Transgenic Soybean Identification Based on Principal Component Analysis and Neural Network |
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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): 1537-1541. DOI: 10.3964/j.issn.1000-0593(2013)06-1537-05 | |
| Authors: | WU Jiang HUANG Fu-rong HUANG Cai-huan ZHANG Jun CHEN Xing-dan |
| Affiliation: | 1. Opto-Electronic Department of Jinan University, Guangzhou 510632, China 2. Department of Food Science and Engineering of Jinan University, Guangzhou 510632, China3. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
| 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. |
| Keywords: | NIR Transgenic soybean PCA ANN-BP |
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