摘要:
以随机抽取的42个市售速溶茶产品为研究对象,采用近红外光谱分析技术并结合偏最小二乘法(PLS)对其水分、咖啡碱和茶多酚含量进行定标建模分析。建模结果以校正集相关系数(Rc)、校正集均方根误差(RMSEC)、交互验证相关系数(Rcv)和交互验证均方根误差(RMSECV)为指标评价模型的优劣。结果表明,水分较为理想的定标模型Rc=0.9266,RMSEC=0.6439,Rcv=0.8809,RMSECV=0.8509;咖啡碱的最优定标模型Rc=0.9964,RMSEC=0.1337,Rcv=0.9543,RMSECV=0.4958;茶多酚较为理想的定标模型Rc=0.9845,RMSEC=1.2097,Rcv=0.9679,RMSECV=1.8083。经验证集样品检验,水分、咖啡碱、茶多酚的预测相关系数分别是0.9050、0.9350、0.9557,与实际测定值吻合度较高,可为速溶普洱茶制品理化成分的快速检测提供参考。
Abstract:
The calibration analysis of moisture,caffeine and tea-polyphenols were studied by combined near infrared spectroscopy and partial least squares (PLS),based on in random selection of 42 instant teas. The evaluation index of prediction model included correlation coefficient of calibration (Rc),the root mean square error of prediction (RMSEC),cross-validation correlation coefficient of calibration (Rcv),and the root mean square error of cross-validation error (RMSECV). The results showed that the ideal calibration model of Rc,RMSEC,Rcv,and RMSECV for moisture were 0.9266,0.6439,0.8809,0.8509.The optimal effect of calibation model for caffeine were 0.9964,0.1337,0.9543,0.4958.The ideal modeling of the Rc,RMSEC,Rcv,and RMSECV for tea-poly phenols were 0.9845,1.2097,0.9679,1.8083.The prediction correlation coefficient of moisture,caffeine and tea polyphenols was 0.9050,0.9350 and 0.9557 respectively after the validation set sample test,which has a high agreement with the actual measured value and can be used as a reference for the rapid detection of the physicochemical components of the instant Pu'er tea products.