Rapid quantification of acid value in frying oil using iron tetraphenylporphyrin fluorescent sensor coupled with density functional theory and multivariate analysis  

作　　者：Haiyang Gu Yining Dong Riqin Lv Xingyi Huang Quansheng Chen 顾海洋;董艺凝;吕日琴;黄星奕;陈全胜(Department of Biological and Chemical Engineering,Yangzhou Polytechnic College,Yangzhou,China;School of Bio and Food Engineering,Chuzhou University,Chuzhou,China;School of Food and Biological Engineering,Jiangsu University,Zhenjiang,China)

机构地区：[1]Department of Biological and Chemical Engineering,Yangzhou Polytechnic College,Yangzhou,China [2]School of Bio and Food Engineering,Chuzhou University,Chuzhou,China [3]School of Food and Biological Engineering,Jiangsu University,Zhenjiang,China

出　　处：《Food Quality and Safety》2022年第4期534-544,共11页食品品质与安全研究（英文版）

基　　金：sponsored by the National Natural Science Foundation of China(No.31701685);Educational Commission of Anhui Province(KJ2021A1071);Chuzhou Municipal Science and Technology(Nos.2021GJ011,2021ZD017),China.

摘　　要：A metalloporphyrin-based fluorescent sensor was developed to determine the acid value in frying oil.The electronic and structural performances of iron tetraphenylporphyrin(FeTPP)were theoretically investigated using time-dependent density functional theory and density functional theory at the B3LYP/LANL2DZ level.The quantified FeTPP-based fluorescent sensor results revealed its excellent performance in discriminating different analytes.In the present work,the acid value of palm olein was determined after every single frying cycle.A total of 10 frying cycles were conducted each day for 10 consecutive days.The FeTPP-based fluorescent sensor was used to quantify the acid value,and the results were compared with the chemical data obtained by conventional titration method.The synchronous fluorescence spectrum for each sample was recorded.Parallel factor analysis was used to decompose the three-dimensional spectrum data.Then,the support vector regression(SVR),partial least squares,and back-propagation artificial neural network methods were applied to build the regression models.After the comparison of the constructed models,the SVR models exhibited the highest correlation coefficients among all models,with 0.9748 and 0.9276 for the training and test sets,respectively.The findings suggested the potential of FeTPP-based fluorescent sensor in rapid monitoring of frying oil quality and perhaps also in other foods with higher oil contents.

关 键 词：Synchronous fluorescence spectrum fluorescent sensor oil quality density functional theory parallel factor analysis 

分 类 号：TS20[轻工技术与工程—食品科学]