一种基于可见-近红外光谱快速鉴别茶叶品种的新方法  

Application PCA-ANN Method to Fast Discrimination of Tea Varieties Using Visible/Near Infrared Spectroscopy

作　　者：李晓丽[1] 何勇[1] 裘正军[1] 

机构地区：[1]浙江大学生物工程与食品科学学院,浙江杭州310029

出　　处：《光谱学与光谱分析》2007年第2期279-282,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(30671213;30600371);高等学校博士学科点专项科研基金课题(20040335034);高等学校优秀青年教师教学科研奖励计划(02411);浙江省重大科技攻关项目(2005C12029)资助

摘　　要：提出了一种用可见-近红外光谱技术快速无损鉴别茶叶品种的新方法。应用可见-近红外光谱仪测定5个品种茶叶的光谱曲线,用主成分分析法对不同品种茶叶进行聚类分析并获得茶叶的可见-近红外光谱数据的主成分,再结合人工神经网络技术建立模型进行品种鉴别。主成分分析表明,以主成分1和2对所有建模样本的得分值做出的得分图,对不同种类茶叶具有较好的聚类作用,可以定性分析茶叶种类。把主成分分析得到的前6个主成分作为神经网络的输入,茶叶品种值作为神经网络的输出,通过5个茶叶品种共125个样本的训练和学习,建立了茶叶品种鉴别的3层BP人工神经网络模型,对未知的25个样本进行鉴别,品种识别准确率达到100%。说明本文提出的方法具有很好的分类和鉴别作用,为茶叶的品种快速鉴别提供了一种新方法。A new method for the discrimination of varieties of tea by means of visible/near infrared spectroscopy （Vis/NIRS） （325-1 075 nm） was developed. A relation has been established between the reflectance spectra and the tea varieties. The data set consists of a total of 150 samples of tea. First, the data was analyzed with principal component analysis（PCA）. It appeared to provide the reasonable clustering of the varieties of tea. Meanwhile PCA compressed hundreds of spectral data into a small quantity of principal components which described the body of the spectra; the first 6 principal components computed by PCA were applied as inputs to a back propagation neural network with one hidden layer. One hundred twenty five samples from five varieties were selected randomly, then they were used to build BP-ANN model. This model has been used to predict the varieties of 25 unknown samples; the residual error for the calibration samples is 1. 267 × 10^-4. The recognition rate of 100% was achieved. This model is reliable and practicable. So this paper could offer a new approach to the fast discrimination of varieties of tea.

关 键 词：可见-近红外光谱 茶叶 品种 主成分分析 人工神经网络 鉴别 

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