摘要
采用单因素和Box-Behnken试验,考察微波强度、萃取时间、乙醇体积分数和料液比对蓝莓中花青素萃取率的影响,并分析花青素萃取特性。采用响应面法和遗传算法-神经网络模型2种方式对微波辅助萃取蓝莓中花青素的工艺条件进行优化。结果表明:各因素对花青素萃取率影响均呈现先增加后降低的趋势。响应面法和遗传算法-神经网络模型法相对误差、决定系数R2值分别为2.71%、0.877 3和1.43%、0.904 4,说明遗传算法-神经网络模型比响应面法具有更强的预测和优化能力。最终采用遗传算法-神经网络优化获得微波萃取蓝莓中花青素最佳工艺条件:微波强度155 W/g、萃取时间53 s、乙醇体积分数56%、料液比1∶30(g/mL)。在此条件下,花青素萃取率为85.12%,并且高于响应面优化值83.32%。本研究结果可为食品加工过程中工艺参数优化提供一种有效方法。
The effects of four independent variables,namely microwave intensity,extraction time,ethanol concentration and solid-to-solvent ratio,on the extraction efficiency of anthocyanins from blueberry were investigated by one-factorat-a-time method.Subsequently,these variables were optimized using response surface methodology(RSM)and genetic algorithm-artificial neural network(GA-ANN)based on Box-Behnken design.The results showed that the extraction efficiency increased to a maximum followed a decrease with increasing level of each variable in the experimental range.GA-ANN showed better prediction and optimization abilities than RSM with lower relative error value(1.43%versus 2.71%)and higher determination coefficient R2(0.904 4 versus 0.877 3).The optimal process parameters were obtained by using GA-ANN as follows:microwave intensity 155 W/g,extraction time of 53 s,ethanol concentration 56%and solid-to-solvent ratio 1:30(g/mL).The yield of blueberry anthocyanins extracted was 85.12%,under the optimized conditions,which was higher than that(83.32%)calculated by RSM.The results from this research can provide an effective method for the optimization of process parameters in food processing.
作者
薛宏坤
刘成海
刘钗
徐浩
秦庆雨
沈柳杨
郑先哲
XUE Hongkun;LIU Chenghai;LIU Chai;XU Hao;QIN Qingyu;SHEN Liuyang;ZHENG Xianzhe(College of Engineering,Northeast Agricultural University,Harbin 150030,China)
出处
《食品科学》
EI
CAS
CSCD
北大核心
2018年第16期280-288,共9页
Food Science
基金
国家自然科学基金面上项目(31571848)
关键词
微波萃取
蓝莓
花青素
响应面
神经网络
优化
microwave extraction
blueberry
anthocyanins
response surface methodology
neural network
optimization
