TY - JOUR ID - 57478 TI - Quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols JO - Asian Journal of Green Chemistry JA - AJGC LA - en SN - 2588-5839 AU - Shahpar, Mehrdad AU - Esmaeilpoor, Sharmin AD - Director of Ilam Petrochemical Company, Ilam, Iran AD - Department of Chemistry, Payame Noor University, P.O. BOX 19395-4697, Tehran, Iran Y1 - 2018 PY - 2018 VL - 2 IS - 2 SP - 144 EP - 159 KW - Ecotoxicity KW - Environmental hazard KW - Phenols KW - Anilines KW - Quantitative stature retention relationship DO - 10.22631/ajgc.2018.100313.1023 N2 - 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. UR - https://www.ajgreenchem.com/article_57478.html L1 - https://www.ajgreenchem.com/article_57478_12aea88fb1bb4695fd45ac98e69cc9a6.pdf ER -