摘要
本文以具有良好代表性的绿茶为材料,通过比较多种预处理方法以及不同统计回归方法,建立绿茶中水分和茶多酚总量的近红外定标模型。结果:水分模型以9点卷积平滑(SG9)结合一阶导,以偏最小二乘法(PLS)建模效果最好,内部交叉验证的定标集、验证集的标准差SEE/SEP和相关系数r分别为:0.1486、0.9940、0.1685、0.9925;茶多酚模型以一阶导结合单位长度归一化(Nle),以偏最小二乘法(PLS)建模效果最好,内部交叉验证的定标集、验证集的标准差SEE/SEP和相关系数r分别为:1.086、0.8946、1.093、0.8344。同时以水分为例比较了原始光谱建模和平均光谱建模的效果差异,认为二者无显著差异,但前者模型更稳定。
In order to develop NIR calibration model for moisture and total tea polyphenols of green tea, 120 green tea samples were analyzed by Buchi N-200 NIP equipment. It indicated by internal cross validation that, the moisture model with SG9 added first derivative was the best, the standard error of calibration （SEE）, standard error of crossvalidation （SEP） and correlation coefficient （r） of calibration set （C-set） and validation set （V-set） were 0. 1486,0. 9940,0. 1685, 0. 9925; the total tea polyphenols model with first derivative added normalization to unit length was the best and its SEE, SEP, SEE-r and SEP-r were 1. 086,0. 8946,1. 093,0. 8344.
出处
《茶叶》
2007年第2期67-70,共4页
Journal of Tea
