Study On The Extraction Of Peanut Oil By Super And Subcritical CO₂

In China,peanut is one of the most important oil crops and important protein resource,which is closely related to people's diet and life health,and also closely related to the development of national economy.In recent years,supercritical CO₂?S_pFE-CO₂?and subcritical CO₂?S_bFE-CO₂?extraction technology has shown great potential in the processing and separation of agricultural products,and has been developing rapidly.Innovative research is needed on the green production and comprehensive utilization of peanut.The author's research includes:Four aspects of scheme including pressurization extraction,separation,and high-pressure seal were selected,and the industrial process scheme for the extraction of peanut oil by S_pFE-CO₂ and S_bFE-CO₂ was determined.Based on the uniform design method,S_pFE-CO₂ extraction of peanut oil was studied.According to the extraction pressure,temperature,time and CO₂ flow,four factors and ten levels of design and arrangement experiments were carried out.The mathematical model was established by stepwise regression analysis of the experimental data using SPSS20.0 software.The optimum technological conditions of S_pFE-CO₂ extraction of peanut oil were determined.And the characteristic indexes and fatty acid composition of peanut oil were detected and analyzed.The results indicated that the optimum technological conditions of S_pFE-CO₂ extraction of peanut oil were particle size of 20mesh,30mesh and 40mesh ratio about 1:1:1,extraction pressure34.5MPa,the temperature 45?,the time 140min,and CO₂ flow rate 220L/?h·kg?.Under this condition,the oil production rate of peanut reached 48.75%.By detection,the quality of peanut oil with the S_pFE-CO₂ extraction was clear and fragrant,the acid value?0.6mg/g?,the peroxide value?3.4mmol/kg?and the content of unsaturated fatty acid?79.586%?were all in line with the level of first-class edible peanut oil standard.Using the experimental data of uniform design as training samples,a neural network prediction model for S_pFE-CO₂ extraction of peanut oil was established.The extraction process was predicted,the relationship between the experimental factors and the oil production rate was analyzed,and the better technological conditions were determined.Results:A 4-9-1 neural network model was established.The prediction value of the oil production was close to the experimental value,and the relative error?absolute value?was less than 2%;The neural network model could predict the trend of peanut oil production under the influence of various parameters.When the extraction pressure was 30MPa,the temperature was 40.5?,the time was 125min,and the CO₂flow rate was 187L/?h·kg?,the expected value of peanut oil production rate was 47.5%.Based on the uniform design method,the extraction of peanut oil by S_bFE-CO₂was studied.The effects of extraction pressure and temperature on the oil yield of peanut were investigated.The results showed that when the temperature of CO₂ was slightly lower than the critical temperature and the pressure was higher than the critical value,the optimum technological conditions were extractionpressure 20MPa,temperature 30?,time 240min,CO₂ flow 200L/?h·kg?,Under this condition,the oil production rate of peanut reached 47.4%.Compared with the extraction pressure and temperature,the former has a greater impact on the oil production rate.