Shahpar, M., Esmaeilpoor, S. (2017). Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent. Chemical Methodologies, 1(Issue 2. pp. 87-193), 98-120. doi: 10.22631/chemm.2017.96397.1008
Mehrdad Shahpar; Sharmin Esmaeilpoor. "Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent". Chemical Methodologies, 1, Issue 2. pp. 87-193, 2017, 98-120. doi: 10.22631/chemm.2017.96397.1008
Shahpar, M., Esmaeilpoor, S. (2017). 'Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent', Chemical Methodologies, 1(Issue 2. pp. 87-193), pp. 98-120. doi: 10.22631/chemm.2017.96397.1008
Shahpar, M., Esmaeilpoor, S. Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent. Chemical Methodologies, 2017; 1(Issue 2. pp. 87-193): 98-120. doi: 10.22631/chemm.2017.96397.1008
Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent
2Department of Chemistry, Payame Noor University, P.O. BOX 19395-4697, Tehran, Iran
Receive Date: 25 August 2017,
Accept Date: 08 September 2017
Abstract
A quantitative structure–retention relationship (QSRR), was developed using the genetic algorithm-partial least square (GA-PLS), Kernel partial least square (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) approach for the prediction of the retention time (RT) of doping agents in urine. These retention times are obtained by ultra-high-pressure liquid chromatography–quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). A suitable set of the molecular descriptors was calculated and the important descriptors were selected with the aid of the GA-PLS and GA-KPLS. By comparing the results, GA-KPLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. This model was used for the prediction of the RT values of some of doping agents which were not used in the modeling procedure. This is the first research on the QSRR of doping agents against the RT using the GA-PLS, GA-KPLS and L-M ANN model.
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