茶叶咖啡碱近红外光谱模型简化方法

该文以茶叶为研究对象，以近红外光谱分析技术快速检测茶叶中的咖啡碱含量为目的，采用一种小波包分析-移动窗口偏最小二乘法（WPA-MWPLS）的处理光谱数据方法，即利用小波包精细的多层分解功能扣除背景、降低噪声的影响，利用移动窗口偏最小二乘法（MWPLS）挑选与茶叶中咖啡碱相关性较大的波数区间使用偏最小二乘法建立校正模型。与只经过Savitzky–Golay预处理后直接利用PLS所建模型相比，采用小波包分析-移动窗口偏最小二乘法使得预测相关系数R由0.9170提高到了0.9625；预测均方差RESEP由0.3071下降为0.2463。该结果表明：该方法具有预处理简单、优选参数和建模变量少等特点，能在很大程度上简化建模过程、提高建模和分析速度。

Simplification of model for tea caffeine detection by near infrared spectroscopy

Wavelet packet analysis-moving window partial least square(WPA-MWPLS) data processing method was utilized to rapidly detect the caffeine content in tea by near-infrared spectroscopy. Fine multi-level decomposition function of wavelet packet was used to subtract background and noise. At the same time, MWPLS was used to select wavelength ranges that had good relevance with caffeine content in the tea. And then, an adjusted model was established with partial least square. Compared with the model built with the pretreatment of Savitzky-Golay smooth, the prediction correlation coefficient of the model with WPA-MWPLS increased from 0.9170 to 0.9625 and the root mean squared error was reduced from 0.3071 to 0.2463. This method has such advantages as simple pretreatment, few parameters to optimize, and a small number of variables, thus it greatly simplifies the modeling process and enhances the efficiency in building and analyzing the models.