Study On The Detection And Quality Of Edible Oil Composition Based On Raman Spectroscopy

High-quality edible blend oil can not only enhance the flavor of daily diet,but also provide many essential nutrients for people.However,the formula of adulterated edible oil is not open and its proportion is opaque,which seriously infringes the rights and interests of consumers.Therefore,an rapid quantitative analysis methods for the composition and quality of edible oils is studied in this paper based on Raman spectroscopy.The main research contents are summarized as follows:Firstly,the background and significance on adulteration identification of edible oil is introduced.The detection of different edible oil types and quality based on Raman spectroscopy ia investigated at home and abroad.The basic principle of identifying adulterated edible oils by Raman spectroscopy is analyzed,which combined with stoichiometry.And the research objectives and methods of this study are clarified.Secondly,a method is proposed to detect the content of four components blend oil.A hybrid optimization algorithm is presented based on the Multi-output Least Squares Support Vector Machine(MLSSVM),which is optimized by Quantum-Behaved Particle Swarm Optimization(QPSO).A reliable mathematical model is established.The theoretical basis of the mathematical model is introduced.The characteristics of edible oils and the pretreatment process are explored based on Raman spectra.The accuracy of the mathematical model is evaluated by the mean square error(MSE)and correlation analysis(R),and these results are compared with the traditional MLSSVM and BP neural network.Thirdly,the rapid quantitative determination method of fatty acid content in three-component edible blend oil is studied.A convenient method for calculating fatty acid content in mixed edible oils is proposed.A hybrid optimization algorithm based on the support vector regression machine(SVR)is proposed,which is optimized by artificial bee colony(ABC).And the principle of that are introduced.By comparing with traditional SVR and PSO-SVR,the superior performance of the algorithm is demonstrated.Finally,surface-enhanced Raman spectroscopy(SERS)is introduced to study the adulteration of sesame oil.In order to overcome the shortcomings of Raman spectroscopy in the identification of dark oils,a substrate with enhanced effect is fabricated.The mathematical model of ABC-SVR is applied to analyze experimental data,and the proposed method is evaluated by MSE and R.