响应面分析法优化枸杞酒发酵醪的浸渍工艺

Optimization of steeping process for fermented mash of Lycium bararum wine with response surface methodology

以干枸杞为原料,复水浸渍制备枸杞酒发酵醪,以醪中枸杞多糖的含量为衡量浸渍工艺的指标。在单因素试验的基础上,选取浸渍温度、浸渍真空度、料液比(kg/L)、浸渍时间为自变量,多糖含量为响应值,利用Box-Behnken中心组合设计原理和响应面分析法,研究各自变量及其交互作用对多糖含量的影响,模拟得到二次多项式回归方程的预测模型,确定最佳浸渍条件为:浸渍温度62℃、真空度0.06MPa、料液比0.19kg/L、浸渍时间9h。在此工艺条件下,发酵醪枸杞多糖含量为122.86mg/100mL,与理论预测值123.55mg/100mL相比,其相对误差约为0.558%。说明通过响应面优化后得出的方程具有一定的实践指导意义。

Dried Chinese wolfberry as raw material, adding water steeping as the method for Lycium bararum wine fermented mash, with the content of Lycium bararum polysaccharides （LBP） as a measure indicator. Four steeping parameters including steeping time, steeping temperature, solid-liquid ratio and steeping vacuum were optimized using central composite design and response surface methodology based on the single factor investigations for achieving maximum the content of LBP. The interaction of the respective variables and their influence on the content of LBP were studied by using Box-Benhnken central composite design and response surface analysis theory. The simulated quadratic polynomial regression equation of prediction model was also set up. The optimal extraction conditions were as follows： steeping temperature 62℃, steeping vacuum 0.06MPa, and solid-liquid ratio of 0.19kg/L, steeping time 9h. Under these conditions, the average content of LBP was 122.86mg/100mL, compared to the theoretical value 123.55mg/100mL, the relative error of 0.558% which suggested that the practical significance was derived by the optimization by response regression equation.