Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks.

Quantitative structure-retention relationship (QSRR) models correlating the retention times of fatty acid methyl esters in high resolution capillary gas chromatography and their structures were developed based on non-linear and linear modeling methods. Genetic algorithm (GA) was used for the selection of the variables that resulted in the best-fitted models. Gravitational index (G2), number of cis double bond (NcDB) and number of trans double bond (NtDB) were selected among a large number of descriptors. The selected descriptors were considered as inputs for artificial neural networks (ANNs) with three different weights update functions including Levenberg-Marquardt backpropagation network (LM-ANN), BFGS (Broyden, Fletcher, Goldfarb, and Shanno) quasi-Newton backpropagation (BFG-ANN) and conjugate gradient backpropagation with Polak-Ribiére updates (CGP-ANN). Computational result indicates that the LM-ANN method has better predictive power than the other methods. The model was also tested successfully for external validation criteria. Standard error for the training set using LM-ANN was SE=0.932 with correlation coefficient R=0.996. For the prediction and validation sets, standard error was SE=0.645 and SE=0.445 and correlation coefficient was R=0.999 and R=0.999, respectively. The accuracy of 3-2-1 LM-ANN model was illustrated using leave multiple out-cross validations (LMO-CVs) and Y-randomization.