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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
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
关键词
人工神经网络
近红外光谱
茶多酚
茶多糖
Artificial neural network
Near-infrared spectrum
Tea polyphenol
Tea amylose
