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投稿时间：2020-06-10
投稿时间：2020-06-10
中文摘要: 在单因素试验基础上，采用Box-Behnken 设计响应面和遗传算法-神经网络两种方式对龙牙百合总黄酮提取工艺条件进行优化。结果表明：各因素对提取结果均呈现先上升后下降的趋势，响应面法和遗传算法-神经网络模型法相对误差和决定系数R2 分别为1.19%、0.955 4 和0.72%、0.994 7。经验证，遗传算法-神经网络模型优化结果高于响应面，表明前者具有更强优化能力。最终采用遗传算法-神经网络优化获得提取龙牙百合总黄酮最佳工艺条件为：提取温度73 ℃、提取时间50 min、液固比43∶1（mL/g）、乙醇体积分数53%，在此条件下总黄酮含量为47.17 mg/g，高于响应面预测值46.63 mg/g。
Abstract:Response surface methodology（RSM）of Box-Behnken design and genetic algorithm-neural network（GA-NN）were used on the basis of the single-factor test to optimize the extraction of total flavonoids from Lilium brownii.The results showed that the amount of each extracted factor showed a rising trend followed by a decline.GA-NN showed better prediction and optimization abilities and had a lower relative error rate（0.72%versus 1.19%）and higher determination coefficient R2（0.994 7 versus 0.955 4）than RSM. Accordingly，the optimal conditions determined using GA -NN were as follows：50 min extraction time，53% ethanol concentration，43 ∶1（mL/g）liquid-solid ratio，73 ℃temperature.Under these optimized conditions，the total flavonoid content was 47.17 mg/g，which was higher than the amount obtained using RSM（46.63 mg/g）.
keywords: Lilium brownii total flavonoids response surface methodology artificial neural network genetic algorithm
文章编号：202107017 中图分类号： 文献标志码：
基金项目:湖南省教育厅优秀青年项目（18B427）；邵阳学院研究生科研创新项目（CX2019SY057）；邵阳学院“双一流”建设产学研合作平台（邵院通〔2018〕50 号）
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