کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1245075 | 969711 | 2011 | 9 صفحه PDF | دانلود رایگان |
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.
Journal: Talanta - Volume 83, Issue 3, 15 January 2011, Pages 1014–1022