<div style="text-align:justify;"> Peanut oil oxidation was to monitor and quantify combining synchronous fluorescence spectroscopy and chemometrics. Peanut oil was subjected to an accelerated oxidation...<div style="text-align:justify;"> Peanut oil oxidation was to monitor and quantify combining synchronous fluorescence spectroscopy and chemometrics. Peanut oil was subjected to an accelerated oxidation testing. The spectral and related chemical indicators were caught during oxidation induce testing. Fluorescence spectra were gained for each sample with simultaneous excitation from 200 to 800 nm and the offsets (Δλ) of 10 to 180 nm during the oxidation process. The results showed the induce period (IP) of the peanut oil was 16.45 h. Parallel factor analysis (PARAFAC) was performed to select the best Δλ interval of 70 nm, which spectral data was the most suitable for interval partial least square (iPLS) and synergy interval PLS (siPLS) modeling and forecast. The study presented all interval selection methods had the better results than the global spectrum modelling. iPLS reached the best into 10 intervals with a ratio of prediction to deviation (RPD) of 2.10. siPLS that separated the whole spectrum into 15 intervals and combined the third intervals (282 to 320 nm, 362 to 400 nm, and 761 to 800 nm) had a ratio of RPD of 2.26. The results showed the optimal siPLS model performed a little better than iPLS. The established model lying on interval selection could improve the prediction accuracy. It could provide a quick, accurate method to monitor oil oxidation process. </div>展开更多
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 t...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.展开更多
文摘<div style="text-align:justify;"> Peanut oil oxidation was to monitor and quantify combining synchronous fluorescence spectroscopy and chemometrics. Peanut oil was subjected to an accelerated oxidation testing. The spectral and related chemical indicators were caught during oxidation induce testing. Fluorescence spectra were gained for each sample with simultaneous excitation from 200 to 800 nm and the offsets (Δλ) of 10 to 180 nm during the oxidation process. The results showed the induce period (IP) of the peanut oil was 16.45 h. Parallel factor analysis (PARAFAC) was performed to select the best Δλ interval of 70 nm, which spectral data was the most suitable for interval partial least square (iPLS) and synergy interval PLS (siPLS) modeling and forecast. The study presented all interval selection methods had the better results than the global spectrum modelling. iPLS reached the best into 10 intervals with a ratio of prediction to deviation (RPD) of 2.10. siPLS that separated the whole spectrum into 15 intervals and combined the third intervals (282 to 320 nm, 362 to 400 nm, and 761 to 800 nm) had a ratio of RPD of 2.26. The results showed the optimal siPLS model performed a little better than iPLS. The established model lying on interval selection could improve the prediction accuracy. It could provide a quick, accurate method to monitor oil oxidation process. </div>
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.
文摘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.
