Characterization Of Thermal Degradation Of Edible Oil And Incorporation Recognition Of Frying Waste Oil By Raman And Infrared Spectroscopy

This topic took the common edible oils in the food industry-soybean oil,canola oil and palm oil as the research objects,conducted physical and chemical measurements and spectral analysis of the oil quality in the frying process,and determined the characteristic indicators of the oil in the frying process.The main research contents and conclusions can be summarized as follows:（1）Firstly,the thermal degradation of edible oil fried for 72 hours was studied by using molecular structure-sensitive fourier transform infrared spectroscopy,and a quantitative prediction model for acid value and a qualitative identification model for frying waste oil were established.The influence of four different preprocessing methods of multiplicative scatter correction（MSC）,moving average smoothing（MAS）,savitzky-golay smoothing（SG）and baseline subtraction（BS）,combined with competitive adaptive reweighted sampling（CARS）of feature extraction method on information extraction and stability of partial least squares regression（PLS）and partial least squares discriminant analysis,PLS-DA)and least squares support vector machine（LSSVM）models was systematically discussed.The statistical results show that the prediction performance of the model built by MSC,MAS and BS combined with the CARS method has achieved ideal results,and the quantitative prediction of edible oleic acid value can be realized.At the same time,the prediction set accuracy of the CARS-LSSVM model of the qualitative identification model of frying oil reached 100%.（2）In order to improve the on-site quality control application of molecular spectroscopy to frying oil,Raman spectroscopy was used to explore the thermal degradation of edible oil during frying,and the thermal degradation process and the acid value of frying oil were characterized by the characteristic peak intensity ratio（PIR）of screening.It was found that the thermal degradation and acid number（AV）of soybean oil,canola oil and palm oil were all correlated with the PIRs of I₁₂₆₇/I₁₇₄₉,I₁₂₆₇/I₁₆₅₉and I₁₂₆₇/I₁₇₄₉,with correlation coefficients of 0.972,0.984 and 0.954,respectively.Through the application of the LSSVM model,a high-performance prediction model for AV evaluation of frying oil is established,and the prediction root mean square error is 0.0160 and the prediction bias ratio is 11.351.The overall results show that Raman spectroscopy can be used to trace the thermal degradation in frying oil and complete the quantitative analysis of AV,which provides a new way for on-site and online quality control of frying oil.（3）Taking waste frying oil exceeding the national acid value regulations as the research object,the model prediction analysis was carried out on the quantitative detection of waste oil incorporation by Raman and infrared spectroscopy.The qualitative identification and quantitative prediction models of frying waste oil mixed with edible oil were established by using MSC,SG,MAS,and standard normal variable（SNV）and CARS feature extraction methods,combined with PLS,PLS-DA and LSSVM models.The results show that infrared spectroscopy combined with PLS-DA and LSSVM models can realize the identification of waste edible oil,and the accuracy of prediction set reaches 100%.The quantitative prediction model MSC-CARS-LSSVM predicts a root-mean-square error of 0.196 and a prediction correlation coefficient of 0.952,indicating that it is feasible to quantify the incorporation of frying waste oil into edible oil.