Study On Detection Method Of Synthetic Phenolic Antioxidant Mixture Based On Fluorescence Spectroscopy

As one of the common additives in the transportation and storage of edible oil products,the synthetic phenolic antioxidants(SPAs)usually exist in trace in soybean oil.However,its toxicity will cause harm to the environment and human body,the detection technology of SPAs has become the focus of attention.Conventional detection methods have difficulty achieving the rapid and accurate determination of antioxidants.Fluorescence spectrometry is easy to operate,no damage to the sample,high sensitivity.It can be used for the detection of synthetic phenolic antioxidants,which is of great significance for the protection of oil and food safety.In this paper,based on the three-dimensional fluorescence mechanism and two second-order correction methods,three antioxidant mixture in soybean oil were classified and determined,and then the support vector machine model was used for qualitative and quantitative analysis to further discuss the experimental results.The main contents of this paper are as follows:(1)We describe the fluorescence generation process,the influence of molecular structure on fluorescence,the influence of environmental factors on fluorescence,the relationship between the fluorescence intensity of the material and concentration of solution,the feasibility of fluorescence detection of SPAs,type and structure of fluorescence detection instruments,and the introduction of several commonly used evaluation performance indicators.(2)Three SPAs were selected as the target analytes in the experiment.They were butylated hydroxytoluene(BHT),propyl gallate(PG),and tertiary butylhydroquinone(TBHQ).The experimental materials and sample preparation were introduced.After we acquired spectral,they were pre-processed(de-scattering,excitation-emission calibration).(3)Due to the spectral overlap and mixture resolved difficultly,traditional detection instruments were unable to determine accurately.Combining fluorescence spectrum technology with two second-order calibration methods could replace chemical separation with mathematical separation under interference and complex backgrounds.The mixtures of synthetic phenolic antioxidants prepared in laboratory were measured qualitatively and quantitatively.(4)Support vector machine classifier was used to classify the experimental results and quantitative analysis.Firstly,genetic algorithm and particles swarm optimization were used to optimize the parameters of SVM,then SPAs were classified and their concentration were predicted.The experimental results shown that the effective combination of genetic algorithm and particle swarm optimization algorithm accelerated the convergence speeds of optimization algorithm,and improved the accuracy of classification results of SVM classifier.