中文摘要：

由于原始近红外光谱数据中含有与待测组分不相关的噪音及冗余信息,增加了偏最小二乘法（PLS）模型的复杂程度.为了简化儿茶素的预测模型,采用净分析物预处理法（NAP）对近红外光谱进行预处理,提取出待测组分的净分析物信号,然后利用PLS建立绿茶中三种儿茶素（EGCG、ECG和EGC）含量的（NAP/PLS）模型.在模型建立过程中,通过交互验证的方法优化NAP因子数及模型的主成分因子数,并且将NAP的结果与经典的标准正态变量（SNV）光谱预处理结果相比较.比较结果显示,经过NAP与SNV光谱预处理后,模型的预测结果相差不大,但是经过净分析物预处理后,模型的主成分因子数大大降低.研究结果表明,NAP光谱预处理算法能在保证精度的前提下有效地简化绿茶中儿茶素含量的预测模型.

英文摘要：

Complex degree of partial least squares（PLS） model is often increased due to the noise and redundant information in raw near infrared spectra. In order to simplify PLS model,net analyte preprocessing （NAP） algorithm was used to extract some useful net analyte signals from the raw spectra,then three NAP/PLS models of EGCG,ECG and EGC were constructed. The number of NAP factors and the number of PLS components were optimized by cross-validation. The spectral preprocessing result of NAP algorithm was compared with that of the classical standard normal variate（SNV）. The predicting result of NAP is almost similar to that of SNV,but the number of PLS components factor by NAP is much less than that by SNV. This work demonstrates that NAP pretreatment can simplify the prediction models of catechin content in green tea.