Shahpar, M., Esmaeilpoor, S. (2018). Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols. Asian Journal of Green Chemistry, 2(Issue 2. pp. 85-170), 144-159. doi: 10.22631/ajgc.2018.100313.1023
Mehrdad Shahpar; Sharmin Esmaeilpoor. "Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols". Asian Journal of Green Chemistry, 2, Issue 2. pp. 85-170, 2018, 144-159. doi: 10.22631/ajgc.2018.100313.1023
Shahpar, M., Esmaeilpoor, S. (2018). 'Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols', Asian Journal of Green Chemistry, 2(Issue 2. pp. 85-170), pp. 144-159. doi: 10.22631/ajgc.2018.100313.1023
Shahpar, M., Esmaeilpoor, S. Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols. Asian Journal of Green Chemistry, 2018; 2(Issue 2. pp. 85-170): 144-159. doi: 10.22631/ajgc.2018.100313.1023
Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols
2Department of Chemistry, Payame Noor University, P.O. BOX 19395-4697, Tehran, Iran
Receive Date: 07 October 2017,
Revise Date: 10 December 2017,
Accept Date: 11 December 2018
Abstract
Aniline, phenol, and their derivatives are widely used in industrial chemicals that consequently have a high potential for environmental pollution. Genetic algorithm and partial least square (GA-PLS), kernel partial least square (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between chromatographic retention (log k) and descriptors for modelling the toxicity to fathead minnows of anilines and phenols. Descriptors of GA-PLS model were selected as inputs in L-M ANN model. The described model does not require experimental parameters and potentially provides useful prediction for log k of new compounds. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The stability and prediction ability of L-M ANN model was validated using external test set techniques.