TY - JOUR ID - 50557 TI - Approach to Chemometrics Models by Artificial Neural Network for Structure: First Applications for Estimation Retention Time of Doping Agent JO - Chemical Methodologies JA - CHEMM LA - en SN - 2645-7776 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 - 2023 PY - 2023 VL - 7 IS - 9 SP - 658 EP - 675 KW - Doping agents KW - Ultra-high-pressure liquid chromatogram KW - QSRR KW - Genetic Algorithms DO - 10.48309/chemm.2023.50557 N2 - 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. UR - https://www.chemmethod.com/article_50557.html L1 - https://www.chemmethod.com/article_50557_3bd617d629afbdebc114e2efa5eafcaf.pdf ER -