Quantitative structure-retention relationships for mycotoxins and fungal metabolites in LC-MS/MS

Abstract

Quantitative structure-retention relationship (QSRR) models were used to predict the retention time (tR) of mycotoxins and fungal metabolites. Heuristic method and radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear QSRR models, respectively. The optimal QSRR model was developed based on a 5-21-1 RBFNN architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a square of correlation coefficient (R2) of 0.8709 and root mean square error of 1.2892 for the test set. This article provided a useful tool for predicting the tR of other mycotoxins when experiment data are unknown. © 2009 WILEY-VCH Verlag GmbH & Co. KGaA.