遗传神经网络用于药物液相色谱分离条件的优化

Application of Genetic Neural Networks in the Separation Optimization of HPLC for Drugs

目的:遗传神经网络用于药物液相色谱分离条件的优化。方法:使用均匀设计法同时考察了离子对试剂浓度、缓冲液浓度和甲醇的体积百分比等液相色谱分离条件对去痛片模拟样品中4种组分分离的影响,采用遗传神经网络方法建立了有效的分离条件预测模型。结果:对遗传神经网络模型所预测的最佳分离条件进行实验,获得了比较满意的分离结果。结论:遗传神经网络可有效地用于药物液相色谱分离条件的优化。

Aim: Apply Genetic Neural Networks to the separation optimization of HPLC for drugs. Methods: Use the uniform designs method to study simultaneously the concentration effect of ion-pair, buffer solution and methanol on the separation optimization of HPLC for four kinds of components in the simulated sample of QuTong tablets, and establish efficient prediction model of separation condition by Genetic Neural Networks. Results: Through the experiment with the optimized separation predicted by Genetic Neural Networks model, satisfied results of separation can be obtained. Conclusions: Genetic Neural networks are suitable to optimize the separation condition of HPLC for drugs.