engleski

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Gradient elution in ion chromatography offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per unit time. The aim of this work is the development of a suitable artificial neural network gradient elution retention model that can be used in a variety of applications for method development and retention modeling of inorganic anions in ion chromatography. Multi-layer perceptron artificial neural networks were used to model the retention behavior of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to starting time of gradient elution and slope of linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. 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 study shows that developed artificial neural networks are the method of first choice for retention modelling of inorganic anions in ion chromatography.

gradient elution ; artificial neural networks ; retention modelling ; inorganic anions ; ion chromatography

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