Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols

Shahpar, Mehrdad; Esmaeilpoor, Sharmin

doi:10.22631/ajgc.2018.100313.1023

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	Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols
Article 6, Volume 2, Issue 2. pp. 85-170, Spring 2018, Page 144-159 PDF (846.19 K)
Document Type: Original Research Article
DOI: 10.22631/ajgc.2018.100313.1023
Authors
Mehrdad Shahpar ¹; Sharmin Esmaeilpoor²
¹Director of Ilam Petrochemical Company
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.
Graphical Abstract

Keywords
Ecotoxicity; Environmental hazard; Phenols; Anilines; Quantitative stature retention relationship
Main Subjects
Analytical chemistry


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