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

Abstract

Abstract 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.