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中国精品科技期刊2020
陈健旋. 响应面优化提取血柚皮果胶的工艺研究[J]. 食品工业科技, 2015, (14): 282-286. DOI: 10.13386/j.issn1002-0306.2015.14.049
引用本文: 陈健旋. 响应面优化提取血柚皮果胶的工艺研究[J]. 食品工业科技, 2015, (14): 282-286. DOI: 10.13386/j.issn1002-0306.2015.14.049
CHEN Jian-xuan. Optimization of extraction techniques of pectin from blood pomelo peel by response surface methodology[J]. Science and Technology of Food Industry, 2015, (14): 282-286. DOI: 10.13386/j.issn1002-0306.2015.14.049
Citation: CHEN Jian-xuan. Optimization of extraction techniques of pectin from blood pomelo peel by response surface methodology[J]. Science and Technology of Food Industry, 2015, (14): 282-286. DOI: 10.13386/j.issn1002-0306.2015.14.049

响应面优化提取血柚皮果胶的工艺研究

Optimization of extraction techniques of pectin from blood pomelo peel by response surface methodology

  • 摘要: 采用响应面法优化了血柚皮果胶的提取工艺,在单因素实验基础上,选取柠檬酸浓度、提取温度、液料比和提取时间为自变量,果胶得率为响应值,根据Box-Behnken实验设计方法,对提取血柚皮果胶的关键因素参数进行了优化,建立了血柚皮果胶得率的数学模型。结果表明:四个因素对血柚皮果胶得率的影响大小依次为提取温度>柠檬酸浓度>液料比>提取时间;血柚皮果胶提取的最佳工艺参数为:柠檬酸浓度1.5%,提取温度82℃、液料比32∶1m L/g、提取时间99min。在此条件下,血柚皮果胶得率达18.85%,与预测值仅相差0.32%,验证了数学模型的有效性。 

     

    Abstract: In order to optimize the process of extraction of pectin from blood pomelo peel. Based on the singlefactor experiments,Box-Benhnken design involving four factors as independent variables was used to set up a second order quadratic equation and the applicability of the model for the yield of pectin as function of citric acid concentration,temperature,liquid to material,time. Each variable and their interactions effects on the yield were studied by using Design-Expert software. The results showed that the effect order of four factors on the yield of pectin was as follows:extraction temperature,citric acid concentration,extraction temperature,liquid to material and extraction time. And the optimal extraction conditions of pectin from blood pomelo peel were:citric acid concentration 1.5%,temperature 82℃,liquid to material 32∶1m L·g-1,time 99 min. The yield of pectin could be up to 18.85%,under the optimal extraction condition,the relative error was 0.32% compared to the predictive value,which indicated the feasible model fitted well with the experimental data.

     

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