In this paper, we predict the retention of polybrominated diphenyl ethers (PBDEs) in capillary gas-chromatography (GC) within a useful range of separation conditions. In a first stage of this study, quantitative structure-retention relationships (QSRRs) of PBDEs in six stationary phases with different polarity are established. The single-column QSRR models are generated using the retention data of 126 PBDE congeners by multilinear regression (MLR) coupled to genetic algorithm variable selection applied to a large set of theoretical molecular descriptors of different classes. A quite accurate fitting of experimental retentions is obtained for each of the six GC columns adopting five molecular descriptors. In a further step of this work six molecular descriptors were extracted within the set of molecular descriptors (17 variables) involved in the various single-column QSRRs. The selected molecular descriptors are combined with observed retentions of ten representative PBDEs, adopted as descriptors of the GC system. These quantities are considered as the independent variables of a multiple-column retention model able to simultaneously relate GC retention to PBDE molecular structure and kind of column. The quantitative structure/column-retention relationship is established using a multi-layer artificial neural network (ANN) as regression tool. To optimise the ANN model, a validation set is generated by selecting two out of the six calibration columns. Splitting of columns between training and validation sets, as well as selection of PBDE congeners to be used as column descriptors, is performed with the help of a principal component analysis on the retention data. Cross-column predictive performance of the final model is tested on a large external set consisting of retention data of 180 PBDEs collected in four separation conditions different from those considered in model calibration (different columns and/or temperature program).
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