Optimization of extraction of pigment from chestnut shell using response surface methodology

2018 1st International Conference on Environment Prevention and Pollution Control Technology

生态环境科学

Chen, Sijia^1,2,3 ; Qi, Jinghua^1,2,3 ; Pang, Meixia^1,2,3 ; Wang, Fang^1,2,3

Beijing Key Lab. of Agric. Product Detection and Control of Spoilage Organisms and Pesticide Residue, Beijing University of Agriculture, Beijing

102206, China^1

Teaching Group of Food Chemistry, Faculty of Food Science and Engineering, Beijing University of Agriculture, Beijing

102206, China^2

Beijing Innovation Consortium of Swine Research System, Beijing

102202, China^3

关键词: Ethanol concentrations;    Extraction conditions;    Extraction temperatures;    Optimum conditions;    Quadratic regression;    Reaction temperature;    Response surface methodology;    Response surface optimization;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/199/4/042009/pdf
DOI  :  10.1088/1755-1315/199/4/042009

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Chestnut shell was selected as the raw material for extracting brown pigment, and the response surface methodology was utilized for optimizing the extraction conditions. In this paper, the effects of ethanol concentration, extraction temperature and time on the color value were studied and analysed by response surface experiments. The results showed that the quadratic regression model had ideal fitting effect to the experimental results. When the pH value of extraction system was 13, the optimum conditions for color value were consisted of an ethanol concentration of 28wt%, a reaction temperature of 83°C, and a reaction time of 2.3h. The extraction temperature had the greatest influence on the color value of the pigment. The interaction between ethanol concentration and extraction temperature had significant effects on the color value. And the interaction between ethanol concentration and extraction time had no significant effects. The color value of chestnut shell pigment can reach 31.25 ± 0.50 with the response surface optimization.