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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

Article 2, Volume 1, Issue 2. pp. 87-193, September and October 2017, Page 98-120  XML PDF (996.17 K)
Document Type: Original Article
DOI: 10.22631/chemm.2017.96397.1008
Authors
Mehrdad Shahpar email 1; Sharmin Esmaeilpoor2
1Director of Ilam Petrochemical Company
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 by 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 the doping agents in urine. The values of the retention time were obtained by using 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 by 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 to predict 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.

Graphical Abstract

Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent
Keywords
Doping agents; Ultra-high-pressure liquid chromatogram; QSRR; Genetic algorithms
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