Neural networks in high-performance liquid chromatography optimization:Response surface modeling

The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method (LOOM). Data sets of linear and non-linear response surfaces (capacity factors) were taken from literature. The results show that neural networks offer promising possibilities in HPLC method development. The predictive results were better or comparable to those obtained with linear and non-linear regression models

A chemometric investigation of the selectivity of multisolvent mobile phase systems in RP-HPLC

The selectivity of multisolvent mobile phase systems consisting of water, methanol, acetonitrile and tetrahydrofuran was studied when the solvent strength decreases at a constant ratio of the modifiers. From the retention measurements of six benzene derivatives on an octylsilica column at eleven different mobile phase compositions the relation between the logarithm of the capacity factor of each solute and the mobile phase composition was modeled by a quadratic equation. From the models the capacity factors of the solutes were predicted for 3 binary, 3 ternary and 7 quaternary mobile phase systems when the fraction of water decreases and the ratio of the organic modifiers is kept constant for the tenary and quaternary solvent systems. The selectivity factors, alpha, of five pairs of solutes were calculated from the capacity factors and plotted against the solvent strength of the mobile phase systems. The selectivity remained not constant, but varied with the solvent strength: if the water fraction of a multi-solvent system is changed at a constant ratio of the modifiers, not only the solvent strength, but also the selectivity changes. The consequence of this result for optimization strategies is discussed

Multivariate modelling of the pharmaceutical two-step process of wet granulation and tableting with multiblock partial least squares

The pharmaceutical process of wet granulation and tableting is described as a two-step process. Besides the process variables of both steps and the composition variables of the powder mixture, the physical properties of the intermediate granules are also used to model the crushing strength and disintegration time of pharmaceutical tablets, Multiblock partial least squares (MBPLS) regression is used to model the two-step process, With MBPLS the highly collinear granulate properties can be segregated from the process and composition variables to study separately the influence of both groups of descriptor variables on the tablet properties. This improves the interpretability of an ordinary PLS model. Two different approaches of the MBPLS algorithm are compared for the modelling of the two-step process, One approach suffers severely from correlation between the two descriptor blocks, When the correlation is removed, the approach improves. (C) 1997 John Wiley & Sons, Ltd