BP神经网络优化荷叶黄酮提取工艺及黄酮稳定性实验的探索

Optimization of extraction technology of total flavonoids from lotus leaf by BP neural network and exploration of flavonoids stability

以荷叶为原料,采用Box-Behnken响应面设计与神经网络模型相结合的方法优化超声-渗漉协同作用浸提荷叶总黄酮的工艺条件并探讨了影响荷叶黄酮稳定性的因素。结果表明,优化后的提取工艺为乙醇浓度60.2%,料液比1∶39.7g/mL,超声时间80min,渗漉速度3mL/min,渗漉液收集体积为9.2倍,此条件下总黄酮得率为6.87%,与模型预测相对误差仅为1.89%,表明神经网络优化荷叶黄酮提取工艺具有很好的可靠性和实用价值。荷叶黄酮提取液在低温、微酸、避光条件下较稳定,β-环糊精、VC、D-葡萄糖酸内酯均对其均有一定的保护作用。

Box-Behnken response surface design coupled with neural network model was employed as a new method to optimize the conditions for ultrasonic-diacolation-assisted extraction of total fiavonoids from lotus leaf. And the paper also studied the stability of flavonoids. The optimal extraction conditions were ethanol concentration of 60.2%,material-to-liquid ratio of 1：39.7,extraction duration of 80rain,and collecting 9.2 folds of percolate at a rate of 3mL/min. The extraction yield of total flavonoids was 6.87% ,the deviation between observed and predicted values of yield was 1.89% ,which indicated the reliability and practicability in the optimized conditions. It also suggested that the extracted total fiavonoids were more stable at low temperature, low pH value and in dark condition,15-cyclodextrin,vitamin C or D-glucose acid lactone also improved the stability of the total flavonoids.