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The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available., (Copyright © 2012 Elsevier B.V. All rights reserved.)

A quantitative structure-retention relationship (QSRR) study has been performed to correlate relative retention times (RRTs) of trimethylsilylated (TMS) anabolic androgenic steroids (AAS) with their molecular characteristics, encoded by the respective descriptors, for the prediction of RRTs of novel molecules, using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). The elucidation of similarities and dissimilarities among the data structures was carried out using principal component analysis (PCA). Successful models were established using multiple linear regression (MLR) and partial least squares (PLS) techniques as a function of topological, three-dimensional (3D) and physicochemical descriptors. The models are useful for the estimation of RRTs of designer steroids for which no analytical data is available.