title: Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks.
abstract: 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.
authors.affiliation: Department of Chemistry, Indian Institute of Technology Roorkee, Roorkee, UA 247667, India. vinodfcy@iitr.ernet.in
authors.forename: Vinod Kumar
authors.initials: VK
authors.lastname: Gupta
authors.forename: Hadi
authors.initials: H
authors.lastname: Khani
authors.forename: Behzad
authors.initials: B
authors.lastname: Ahmadi-Roudi
authors.forename: Shima
authors.initials: S
authors.lastname: Mirakhorli
authors.forename: Ehsan
authors.initials: E
authors.lastname: Fereyduni
authors.forename: Shilpi
authors.initials: S
authors.lastname: Agarwal
mesh_terms: D000465:Algorithms; D002849:Chromatography, Gas / Q000379:methods*; D004952:Esters; D005227:Fatty Acids / Q000097:blood* / Q000737:chemistry*; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors
chemical_list: D004952:Esters; D005227:Fatty Acids