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(R_c)、校正集均方根误差(RMSEC)、交互验证相关系数(R_(cv))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R_c=0.9266,RMSEC=0.6439,R_(cv)=0.8809,RMSECV=0.8509;咖啡碱的最优定标模型R_c=0.9964,RMSEC=0.1337,R_(cv)=0.9543,RMSECV=0.4958;茶多酚较为理想的定标模型R_c=0.9845,RMSEC=1.2097,R_(cv)=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。
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.
作者
白晓丽
郭卫华
孔俊豪
刘顺航
李长文
BAI Xiao-li;GUO Wei-hua;KONG Jun-hao;LIU Shun-hang;LI Chang-wen(Yunnan Tasly Deepure Biological Tea Group Co.,Ltd.,Pu'er 665000,China;Hangzhou Tea Research Institute,CHINA COOP,Hangzhou 310016,China)
出处
《食品工业科技》
CAS
北大核心
2019年第1期234-238,245,共6页
Science and Technology of Food Industry
基金
"十三五"国家重点研发计划项目(2016YFD0400804)
关键词
速溶普洱茶
近红外光谱
偏最小二乘
快速测定
instant Pu'er tea
near infrared spectroscopy
PLS
rapid determination
