A three-factor optimisation strategy for micellar liquid chromatography

Abstract

KeyWordsColumn liquid chromatography Micellar liquid chromatography Retention modelling Experimental design Optimisation the difficulty of the separation problem, which saves further experimental work if any of the developed partial optimisations succeeds before developing the threefactor design. In a favourable case, four experiments can be enough. SummaryAn interpretive optimisation methodology for micellar liquid chromatography (MLC) is shown, taking into account pH, surfactant (sodium dodecyl sulphate) and organic modifier (propanol) concentrations. Two objectives are considered: to develop a highly practical straightforward three-factor optimisation for practical MLC, and, in order to avoid unecessary experiments, to link two and three-factor optimisations through a stepwise construction of the experimental design at different pH levels. The whole pH range for an ODS colurnn (from 3 to 7) is covered. The proposed strategy was thoroughly evaluated using the chromatographic data from 81 experimental mobile phases, applied to the separation of a set of solutes exhibiting diverse acidbase behaviour where some of them had partial protonation which is difficult to model. When all the available information was used for modelling the system, the global mean error in the prediction of the retention was 2.5%. When less information was used to achieve a practical number of experiments, the errors increased and depended on the experimental design. A basic design consisting of a 3 x2 z (pH, surfactant, modifier) regular distribution of experiments gave good descriptions, except for those solutes with a partial protonation in the studied pH range. The mean error obtained using a 3 x 22+ 3 learning set (15 experiments) and a test set of 66 experiments was 4 %. A final strategy is proposed which has the advantage of allowing partial optimisations in two factors at constant pH levels, before completing the three-factor experimental design. The sequential strategy adapts retention model and design to