基于可见-近红外反射光谱技术的葡萄品种鉴别方法的研究  

Variety Discrimination of Grapes Based on Visible-Near Reflection Infrared Spectroscopy

作　　者：曹芳[1] 吴迪[1] 何勇[1] 鲍一丹[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310029

出　　处：《光学学报》2009年第2期537-540,共4页Acta Optica Sinica

基　　金：浙江省自然科学基金(Y307158);宁波科技攻关国际合作项目(2008CL0037);浙江省教育厅(20071064)资助课题

摘　　要：提出一种利用可见-近红外反射光谱技术快速无损鉴别葡萄品种的新方法。采用主成分分析法对三个葡萄品种的光谱进行聚类分析。结果表明,黑提葡萄能够被区分。进一步采用人工神经网络技术对马奶子和木拉格两种葡萄进行品种鉴别。以前10个主成分作为神经网络的输入,品种类型作为神经网络的输出,建立三层BP神经网络模型。结果显示,这两个品种的识别准确率达到98.28%,结果优于簇类独立软模式(SIMCA)。同时提出葡萄品种鉴别的四个敏感波段:452、493、542和668 nm。基于敏感波段光谱的BP神经网络预测准确率为97.41%。说明采用可见-近红外光谱分析技术结合主成分分析和人工神经网络的方法能够快速无损鉴别葡萄的品种,为葡萄品种的鉴别提供了一种新方法。A non-destructive method for discriminating varieties of grapes by visible and near reflection infrared spectroscopy （VIS-NIRS） was developed. The spectral data of three varieties of grape samples were clustered by principal component analysis （PCA）. The results indicate that Heiti grape sample can be totally separated from the other two. Mainaizi and Mulage grape samples were discriminated based on back propagation-neural networks （BP- NN） model. The three hidden-layer BP-NN model was built with the first ten PCs as inputs, and the dummy variety numbers of grapes as outputs. The correct answer rate 98.28 % of BP-NN model is achieved, which is better than the one achieved by the soft independent modeling of class analogy（SIMCA） method. Four effective wavelengths for variety discrimination are 453,493,542 and 668 nm. The correct answer rate of BP-NN model based on the spectra of effective wavelengths is 97.41%. The result indicates that variety discrimination of grapes can be achieved rapidly and non-destructively by using VIS-NIRS with PCA and BP-NN.

关 键 词：光谱学 葡萄品种鉴别 可见-近红外反射光谱 主成分分析 人工神经网络 

分 类 号：S123[农业科学—农业基础科学] TH744.1[机械工程—光学工程]