Original PaperModeling GC-ECD retention times of pentafluorobenzyl derivatives of phenol by using artificial neural networksThe depicted retention times (RTs) of an electron capture detection (ECD) system is predicted for a set of 37 pentafluorobenzyl (PFB) derivatives of phenol in a semipolar column, DB-1701 (14% cyanopropylphenyl and 86% dimethyl-polysiloxane). Among a large number of descriptors, four parameters categorized as electronic, topological, geometric, and hybrid (geometric and topological) descriptors are chosen using stepwise multiple regression technique. Each molecular descriptor in this model was disputed to unfold the relationship between molecular structures and their RTs. The descriptors occurring in the multiple linear regression (MLR) model were considered as inputs for developing the back propagation artificial neural networks (BPANN). The artificial neural network (ANN) model shows superiority over the MLR by decerning 91.9% for different classes of molecules in confusion matrix. This refers to the fact that the retention behaviors of molecules display nonlinear characteristics. The accuracy of 4-4-1 BPANN model was illustrated using leave-one-out (LOO), leave-multiple-out (LMO) cross-validations, and Y-randomization. Moreover, the mean effect of descriptors betrays that descriptor Ss is the most indispensable factor affecting the retention behavior of molecules.

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Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities. K E Y W O R D Spartial least squares, polychlorinated biphenyls, quantitative structure retention relationship, radial basis function neural networks, two-dimensional images