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Application of artificial neural networks for gradient elution retention modelling in ion chromatography
Author(s) -
Bolanča Tomislav,
CerjanStefanović Štefica,
Regelja Melita,
Regelja Hrvoje,
Lončarić Sven
Publication year - 2005
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.200400056
Subject(s) - elution , artificial neural network , gradient elution , chemistry , ion chromatography , resolution (logic) , chromatography , gradient descent , biological system , computer science , high performance liquid chromatography , artificial intelligence , biology
Gradient elution in ion chromatography (IC) 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 time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the 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 ANNs are the method of first choice for retention modelling of inorganic anions in IC.