Development of gradient elution retention model in ion chromatography by using radial basis function artificial neural networks

This work focuses on problems regarding empirical retention modeling in gradient elution ion chromatography. Traditionally, retention modeling in ion chromatography requires certain assumptions. On the other hand, the use of artificial neural networks provides a promising alternative. In this work, radial basis function artificial neural network were used to model varied inherent non-linear relationship of inorganic anions retention behavior (fluoride, chloride, nitrite, sulphate, bromide, nitrate, and phosphate) with respect to mobile phase parameters (starting time of gradient elution and slope of linear gradient elution curve). The training algorithm of hidden layers was divided into two phase (radial assignment and radial spread) and separately optimized followed by output layer training algorithm optimization (one- or two-phase training algorithm were used, respectively). The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This work shows that radial basis artificial neural networks are found to be a viable retention modeling tool in gradient elution ion chromatography.