近红外光谱测定茶叶中茶多酚和茶多糖的人工神经网络模型研究  

Studies on ANN Models of Determination of Tea Polyphenol and Amylose in Tea by Near-Infrared Spectroscopy

作　　者：罗一帆[1,2] 郭振飞[3] 朱振宇[4] 王川丕[1] 江和源[1] 韩宝瑜[1] 

机构地区：[1]农业部茶叶化学工程重点开放实验室 [2]华南师范大学化学与环境学院,广东广州510631 [3]华南农业大学生命科学院 [4]中山大学基础医学院

出　　处：《光谱学与光谱分析》2005年第8期1230-1233,共4页Spectroscopy and Spectral Analysis

基　　金：国家重点基础研究"973"计划(G1998051201);广东省科技计划(2003C20405);农业部茶叶化学工程重点开放实验室开放课题(200401)资助项目

摘　　要：为了建立近红外光谱测定茶叶中茶多酚和茶多糖的模型, 应用了人工神经网络方法, 选择了7 432.3～6 155.7 cm-1和5 484.6～4 192.5 cm-1特征光谱范围, 以网络结构参数的输入层、隐层、输出层神经元数目分别为(8, 4, 1)和(7, 5, 1)来建立茶多酚和茶多糖的测定模型, 模型的结果表明建模的茶多酚和茶多糖的r, RMSECV, RSECV分别为0.984 7, 0.460, 0.123和0.947 0, 0.136, 0.224;预测集的r, RMSEP, RSEP则分别为0.980 4, 0.529, 0.017和0.968 2, 0.111, 0.030. 由此说明建立的近红外光谱-人工神经网络模型可用于预测茶叶中茶多酚和茶多糖的含量.The objectives of the present paper were to build the models for the determination of tea polyphenol （TP） and tea amylose （TA） in tea by near-infrared spectroscopy （NIR）. According to the range of 7 432.3-6 155.7 cm^-1 and 5 484.6-4 192.5 cm^-1 of NIR spectra, the models are built for determining the contents of TP and TA in tea with the input layer, hidden layer and node （（8, 4, 1 ） and （7, 5, 1 ） respectively） in network structure by the artificial neural network. The correlation coefficient （ r ）, the root mean square error of cross validation （RMSECV） and the root mean square error of prediction （RMSEP） were selected as the indexes for evaluating the performance of calibration models. The results show that r, RMSECV and RSECV by the model samples for TP and TAare 0.984 7, 0.460 and 0.123, and 0.947 0, 0.136 and 0.224 respectively, and r, RMSEP and RSEP by the prediction samples for TP and TA are 0.980 4, 0.529 and 0.017, and 0.968 2, 0.111 and 0.029 8 respectively. These indicated that the NIRANN models can be used to determine the contents of TP and TA in tea.

关 键 词：人工神经网络 近红外光谱 茶多酚 茶多糖 

分 类 号：O657.1[理学—分析化学]