Quantitative structure-retention relationships of pesticides in reversed-phase high-performance liquid chromatography

In this paper, a quantitative structure-retention relationships (QSRR) method is employed to predict the retention behaviour of pesticides in reversed-phase high-performance liquid chromatography (HPLC). A six-parameter nonlinear model is developed by means of a feed-forward artificial neural network (ANN) with back-propagation learning rule. Accurate description of the retention factors of 26 compounds including commonly used insecticides, herbicides and fungicides and some metabolites is successfully achieved. In addition to the acetonitrile content, included to describe composition of the water-acetonitrile mobile phase, the octanol-water partition coefficient (from literature) and four quantum chemical descriptors are considered to account for the effect of solute structure on the retention. These are: the total dipole moment, the mean polarizability, the anisotropy of polarizability and a descriptor of hydrogen bonding ability based on the atomic charges on hydrogen bond donor and acceptor chemical functionalities. The proposed nonlinear QSRR model exhibits a high degree of correlation between observed and computed retention factors and a good predictive performance in wide range of mobile phase composition (40-65%, v/v acetonitrile) that supports its application for the prediction of the chromatographic behaviour of unknown pesticides. A multilinear regression model based on the same six descriptors shows a significantly worse predictive capability