基于两种分析方法优化红花中羟基红花黄色素A提取工艺

Optimization of the Extraction Process of Hydroxysafflower Yellow A in Carthamus tinctorius based on Two Analytical Methods

目的:采用响应面设计、遗传-神经网络模型两种分析方法对红花中羟基红花黄色素A进行提取工艺优化。方法:在单因素试验的基础上,采用响应面试验设计法对红花进行提取;HPLC检测提取液中羟基红花黄色素A的含量,计算提取率。再分别采用响应面及遗传-神经网络模型两种分析方法对实验结果进行寻优。结果:响应面分析方法得到的最佳提取工艺为:提取时间:1.6 h、乙醇浓度:48%、提取温度:91℃、液料比:13∶1,该方法下的预测值为1 621.78μg/g,验证试验的平均值为1 604.46μg/g,RSD为0.30%;遗传-神经网络模型处理得到的最优结果为提取时间:0.52 h、乙醇浓度:43%、提取温度:94℃、液料比:5∶1,该模型的预测值为1 884.11μg/g,验证试验的平均值为1 717.43μg/g,RSD为1.41%。结论:遗传-神经网络模型的结果更符合实际,可用于红花中羟基红花黄色素A提取工艺的优化及预测,为单味及复方中药的单成分提取分析提供了参考。

Objective:To optimize the extraction process of hydroxysafflower yellow A in Carthamus tinctorius based on the response surface method and genetic-neural network model.Methods:Based on the single factor test,the response surface experiment design method was adopted and the content of hydroxysafflower yellow A in the extract was determined by HPLC.The extraction rate was calculated.The response surface and genetic-neural network model were used to optimize the experimental results,respectively.Results:The optimum extraction technology in response surface analysis were as follows:extraction time was 1.6 h;ethanol concentration was 48%;extraction temperature was 91 ℃;liquid material ratio was 13∶1.Under the condition,the predicted value was 1 621.78 μg/g;the average of the verification test was 1 604.46 μg/g;the RSD was 0.30%.The best extraction process by genetic-neural network model were as follows:extraction time was 0.52 h;ethanol concentration was 43%;extraction temperature was 94 ℃;liquid material ratio was 5∶1.Under the condition,the prediction value was 1 884.11 μg/g;the average of the verification test was 1 717.43 μg/g and the RSD was 1.41%.Conclusion:The genetic-neural network model is more conformable to optimize the extraction process of hydroxysafflower yellow A in Carthamus tinctorius,which provides a new idea and reference for the single component extraction and analysis of single or compound Chinese medicine.