Institute of Food Engineering, College of Life & Environment Science, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China.
Institute of Food Engineering, College of Life & Environment Science, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China.
Int J Biol Macromol. 2015;78:439-46. doi: 10.1016/j.ijbiomac.2015.03.025. Epub 2015 Mar 24.
In order to classify typical Chinese tea varieties, Fourier transform infrared spectroscopy (FTIR) of tea polysaccharides (TPS) was used as an accurate and economical method. Partial least squares (PLS) modeling method along with a self-organizing map (SOM) neural network method was utilized due to the diversity and heterozygosis between teas. FTIR spectra results of tea extracts after spectra preprocessing were used as input data for PLS and SOM multivariate statistical analyses respectively. The predicted correlation coefficient of optimization PLS model was 0.9994, and root mean square error of calibration and cross-validation (RMSECV) was 0.03285. The features of PLS can be visualized in principal component (PC) space, contributing to discover correlation between different classes of spectra samples. After that, a data matrix consisted of the scores on the selected 3PCs computed by principle component analysis (PCA) and the characteristic spectrum data was used as inputs for training of SOM neural network. Compared with the PLS linear technique's recognition rate of 67% only, the correct recognition rate of the PLS-SOM as a non-linear classification algorithm to differentiate types of tea reaches up to 100%. And the models become reliable and provide a reasonable clustering of tea varieties.
为了对中国典型茶叶品种进行分类，采用傅里叶变换红外光谱（FTIR）对茶叶多糖（TPS）进行分析，这是一种准确且经济的方法。由于茶叶之间的多样性和杂合性，采用偏最小二乘法（PLS）建模方法和自组织映射（SOM）神经网络方法。对光谱预处理后的茶叶提取物的 FTIR 光谱结果分别作为 PLS 和 SOM 多元统计分析的输入数据。优化 PLS 模型的预测相关系数为 0.9994，校准和交叉验证的均方根误差（RMSECV）为 0.03285。PLS 的特征可以在主成分（PC）空间中可视化，有助于发现不同类别的光谱样本之间的相关性。之后，将基于主成分分析（PCA）计算的所选 3PC 的得分数据矩阵和特征光谱数据作为输入，用于训练 SOM 神经网络。与 PLS 线性技术的识别率仅为 67%相比，PLS-SOM 作为一种非线性分类算法来区分茶叶类型的正确识别率高达 100%。并且模型变得可靠，并为茶叶品种提供了合理的聚类。
