Abstract
Optimisation procedures in chromatography usually exploit "hard" model approaches or methods based on the coupling of experimental design techniques and surface response methods. A powerful alternative has been recently provided by Artificial Neural Networks (ANNs), which allow to obtain "soft" models, not based on the a-priori knowledge of the mechanisms involved in the separation, and permit to model non-linear relationships. Most of ANNs applications in chromatography regard multivariate calibration and prediction or studies on structure-activity relationships. They have also been recently applied to the optimisation of process and mobile phase composition parameters: in these applications they are usually coupled to response surface methods and/or experimental design techniques. This review reports the main applications of ANNs to the optimisation of different separation techniques: high-performance liquidchromatography, ion and gas chromatography, electro-separation methods. A section describing the main experimental designs and the theory of ANNs is also present.
Keywords: Optimisation, Artificial neural networks, Chromatography
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