基于太赫兹波段内的光谱分析技术以及主成分特性分析与反向前馈神经网络建模, 提出了一种转基因大豆鉴别方法.从光谱数据中提取累计方差贡献率达到97.582%的前8种主成分因子, 并将其作为输入源导入神经网络模型, 通过剔除冗余数据、降低数据维数, 所建立的神经网络模型能准确识别校验集.该方法可以实现转基因大豆的快速、无损检测, 在农业安全领域有广泛的应用前景.

An approach for recognition of transgenic soybeans was proposed based on spectral analysis in the terahertz (THz) range combing with Principle Component Analysis (PCA) and Back Propagation Neural (BPN) network. Eight principal component factors, whose accumulated variance reached 97.582%, were extracted from the original spectra data and then fed as inputs into the BPN network model. The utilization of the dimension-reduced data in training the network model can recognize the validation set accurately. The nondestructive testing of transgenic soybeans could be achieved by using THz spectroscopy, which could be widely applied in agricultural security areas.