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作 者:潘学文 赵永红 刘元明[3] Pan Xuewen;Zhao Yonghong;Liu Yuanming(School of Electronics and Information Engineering,Hunan University of Science and Engineering,Yongzhou 425199,China;Shool of Mechano-Electronic Engineering,Xidian University,Xi’an 710071,China;Jiujiang University,Jiujiang 332005,China)
机构地区:[1]湖南科技学院电子与信息工程学院,永州425199 [2]西安电子科技大学机电工程学院,西安710071 [3]九江学院,九江332005
出 处:《电子测量与仪器学报》2018年第12期93-98,共6页Journal of Electronic Measurement and Instrumentation
基 金:永州市科技创新技术指导性项目(永科发[2017]41号);国家自然科学基金(61401193);广西自动检测技术与仪器重点实验室开放基金(YQ16204)资助项目
摘 要:提出了一种基于太赫兹光谱和超香肠神经元网络的检测方法,该方法能实现对转基因甜菜的无损鉴别。以转基因甜菜及其亲本为研究对象,通过太赫兹光谱仪扫描获得其在0.1~1.5 THz波段范围内的光谱数据,利用主成分分析方法提取能反映样本97.28%光谱信息的前3个主成分。对每个品种,随机挑选50个样本作为训练集合,建立超香肠神经元鉴别模型,挑选35个样本作为第1测试集,其他非同类样本作为第2测试集对鉴别模型进行验证。实验结果表明,鉴别模型对第1测试集的平均正确识别率达97.86%,对第2测试集的平均正确拒识率达95.08%,该方法具能较好的对转基因甜菜进行鉴别且准确率高。This paper proposes a detection method based on terahertz spectroscopy and hyper sausage neuron ( HSN) to fast and nondestructively distinguish transgenic substance. The transgenic sugar beets and their parents were studied and its Spectral data in the range of 0. 1 to 1. 5 THz were obtained by scanning through terahertz spectrometer,principal component analysis ( PCA) is applied to extract spectral data of transgenic sugar beets,the cumulate reliabilities of the first three components were more than 97. 28%. 50 samples were randomly selected as the training set in each variety and the recognition models were established base on hyper sausage neuron. 35 samples were selected as the first testing set and all the other non-similar samples were selected as the second testing set to verify the feasibility and effectiveness of this model,as the 95. 08% samples in the second set were correctly rejected,the average rate of correct recognition in first testing set was 97. 86%. The experiment results show that the detection method is feasible to distinguish transgenic sugar beets based on terahertz spectroscopy and hyper sausage neuron.
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