RETENTION MODELING AND OPTIMIZATION OF pH VALUE AND SOLVENT COMPOSITION IN HPLC USING BACK-PROPAGATION NEURAL NETWORKS AND UNIFORM DESIGN

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step, seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.