摘要
为提高柑桔副产物的加工综合利用,以超临界CO2萃取精油后的桔子皮为原料提取水溶性膳食纤维,采用响应面实验设计收集实验数据,利用BP神经网络的自学习能力,通过仿真和评估,优化其提取工艺参数。结果表明:BP神经网络技术比响应面分析方法误差小,模型预测准确度高,桔子皮水溶性膳食纤维的最佳提取工艺条件为:温度91.5℃,pH1.60,均质压力30.5MPa,提取时间2.14h,在此条件下,水溶性膳食纤维的得率为28.79%。该研究为柑桔皮水溶性膳食纤维的工业化提取提供一定的技术依据。
In order to obtain optimum condition for soluble dietary fiber from orange peel extracted by fluid CO2,BP Neural Network（BPNN）combined with response surface methodology（RSM）was put forward as a new method to analyze and process test data.The response surface test data could be used by applying the self-learning ability of BPNN.With the help of BPNN,which simulated,evaluated and optimized,the result of experiment showed that BPNN had the less errors than RSM.The optimum conditions for soluble dietary fiber from orange peel were as follows：temperature 91.5℃,pH1.60,homogenized pressure 30.5MPa,and time 2.14h.The experiment was accomplished under optimum conditions,the yield of soluble dietary fiber was 28.79%.In conclusion,BPNN provided a technonical basis for industrialization production of soluble dietary fiber from orange peel.
出处
《食品工业科技》
CAS
CSCD
北大核心
2012年第15期258-262,共5页
Science and Technology of Food Industry
基金
国家科技部科技人员服务企业行动项目(2009GJD20017)
关键词
BP神经网络
水溶性膳食纤维
桔子皮
响应面
均质
BP neural network
soluble dietary fiber
orange peel
response surface
homogenize
