Performance comparison of partial least squares-related variable selection methods for quantitative structure retention relationships modelling of retention times in reversed-phase liquid chromatography

The relative performance of six multivariate data analysis methods derived from or combined with partial least squares (PLS) has been compared in the context of quantitative structure-retention relationships (QSRR). These methods include, GA (genetic algorithm)-PLS, Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), iteratively retaining informative variables (IRIV), variable iterative space shrinkage approach (VISSA) and PLS with automated backward selection of predictors (autoPLS). A set of 825 molecular descriptors was computed for 86 suspected sports doping compounds and used for predicting their gradient retention times in reversed-phase liquid chromatography (RPLC). The correlation between molecular descriptors selected by each technique and the retention time was established using the PLS method. All models derived from a selected subset of descriptors outperformed the reference PLS model derived from all descriptors, with very small demands of computational time and effort. A performance comparison indicated great diversity of these methods in selecting the most relevant molecular descriptors, ranging from 28 for CARS to 263 for MC-UVE. While VISSA provided the lowest degree of over-fitting for the training set, CARS demonstrated the best compromise between the prediction accuracy and the number of selected descriptors, with the prediction error of as low as 46 s for the external test set. Only ten descriptors were found to be common for all models, with the characteristics of these descriptors being representative of the retention mechanism in RPLC.