响应面法优化超临界CO_2萃取山葡萄籽油的工艺

Optimization for Supercritical Fluid CO_2 Extraction of Amur Grape (Vitis amurensis Rupr.) Seed Oil by Response Surface Methodology

利用超临界CO2萃取山葡萄籽油。通过单因素试验确定响应面试验因素与中心水平,根据中心复合试验设计(Central Composite Design,CCD)原理,采用四因素五水平的响应面法,以山葡萄籽油萃取率为响应值作响应面,回归分析各因素的显著性和交互作用。结果表明,超临界CO2萃取山葡萄籽油的最佳工艺条件为:山葡萄籽粉碎粒径过40目筛,萃取压力39.2 MPa,萃取温度41℃,静态萃取时间137 min,动态萃取时间251 min,CO2流速为3 L/min。在此工艺条件下,山葡萄籽油的萃取率为18.22%,与预测值18.28%无显著差异。

Amur grape seed oil was extracted by supercritical CO2 extraction （SFE--CO2）. The response surface methodology （RSM） experiment factors and levels were determined by single factor experiments. On the basis of Central Composite Design（CCD）, a 4 factors and 5 levels＇ RSM was applied to get the RSM plot with the extraction rate of amur grape seed oil as the response value after regression analysis. Then the significance and interaction of every factor were analyzed. The results showed that, the optimal oil extract process with SFE-CO2was as follow： the particle size was 40 mesh, extracting pressure was 39.2 MPa, extracting temperature was 41℃, the static extraction time was 137 min, the dynamics extraction time was 251 min, CO2 flow rate was 3 L/min. Under this condition, the actual extraction rate was 18.22%, there was no signifiacant difference between 18.22% and 18.28% （predictive extration rate）.