Li Xiao-li, He Yong, Qiu Zheng-jun
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Feb;27(2):279-82.
A new method for the discrimination of varieties of tea by means of visible/near infrared spectroscopy (Vis/NIRS) (325-1075 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 x 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.
开发了一种利用可见/近红外光谱（Vis/NIRS）（325 - 1075 nm）鉴别茶叶品种的新方法。已在反射光谱与茶叶品种之间建立了一种关系。数据集总共包含150个茶叶样本。首先，用主成分分析（PCA）对数据进行分析。结果表明它能对茶叶品种进行合理聚类。同时，PCA将数百个光谱数据压缩为少量描述光谱主体的主成分；由PCA计算得到的前6个主成分被用作具有一个隐藏层的反向传播神经网络的输入。从五个品种中随机选取125个样本，然后用它们构建BP - ANN模型。该模型已用于预测25个未知样本的品种；校准样本的残余误差为1.267×10⁻⁴。实现了百分之百的识别率。该模型可靠且实用。所以本文可为快速鉴别茶叶品种提供一种新方法。
