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引用本文:陈斌辉,吕圭源,金伟锋,张佳欢,郑碧莹,任敏霞,吴素香.基于正交设计和BP神经网络-遗传算法多指标综合优化茶叶提取工艺[J].中国现代应用药学,2019,36(10):1223-1228.
CHEN Binhui,LYU Guiyuan,JIN Weifeng,ZHANG Jiahuan,ZHENG Biying,REN Minxia,WU Suxiang.Study on Multi-index Comprehensive Optimization of Tea Extraction Process Based on Orthogonal Design and BP Neural Network Genetic Algorithm[J].Chin J Mod Appl Pharm(中国现代应用药学),2019,36(10):1223-1228.
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基于正交设计和BP神经网络-遗传算法多指标综合优化茶叶提取工艺
陈斌辉, 吕圭源, 金伟锋, 张佳欢, 郑碧莹, 任敏霞, 吴素香
浙江中医药大学药学院, 杭州 311402
摘要:
目的 用正交设计及BP神经网络-遗传算法对茶叶提取工艺进行多指标综合优化。方法 以咖啡因、表没食子儿茶素没食子酸酯(epigallocatechin gallate,EGCG)、表儿茶素没食子酸酯(epicatechin gallate,ECG)为考察指标,在单因素实验的基础上,采用正交设计及BP神经网络-遗传算法优选超声辅助提取茶叶中有效成分的工艺,并对2种方法优选所得的工艺进行验证。结果 正交设计得到的最佳提取条件为乙醇浓度85%、浸提温度80℃、超声时间10 min。工艺验证评分为99.050。BP神经网络-遗传算法得到的最优提取方案为乙醇浓度89%、浸提温度88℃、超声时间13 min,网络预测评分为100.758,工艺验证评分为99.651,相对误差为1.099%。结论 BP神经网络-遗传算法数学模型可用于茶叶中有效成分提取工艺预测和优选,且略优于正交设计。
关键词:  茶叶  BP神经网络  遗传算法  多指标综合评价法  提取工艺
DOI:10.13748/j.cnki.issn1007-7693.2019.10.010
分类号:R284.1
基金项目:
Study on Multi-index Comprehensive Optimization of Tea Extraction Process Based on Orthogonal Design and BP Neural Network Genetic Algorithm
CHEN Binhui, LYU Guiyuan, JIN Weifeng, ZHANG Jiahuan, ZHENG Biying, REN Minxia, WU Suxiang
College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou 311402, China
Abstract:
OBJECTIVE To optimize the extraction process of tea by orthogonal design and BP neural network-genetic algorithm. METHODS Using caffeine, EGCG and ECG as the indexes, based on the single factor experiment, orthogonal design and BP neural network-genetic algorithm were used to optimize the ultrasonic-assisted extraction process of effective components in tea, and these process by the two methods were validated. RESULTS The optimum extraction conditions were 85% ethanol concentration, 80℃ and 10 min ultrasonic time. The validation score was 99.050. The optimum extraction scheme obtained by BP neural network-genetic algorithm was ethanol concentration 89%, extraction temperature 88℃, ultrasonic time 13 min, network prediction score 100.758, process verification score 99.651, relative error 1.099%. CONCLUSION BP neural network-genetic algorithm mathematical model can be used to predict and optimize the extraction process of effective components in tea, and slightly better than orthogonal design.
Key words:  tea  BP neural network  genetic algorithm  multi-index comprehensive evaluation method  extraction process
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