Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

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 LevenbergMarquardt 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-offlight 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. KEYWORDS Graphical AbstractApproach to Chemometrics Models … P a g e | 106

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

“…is learning scheme provides a framework for combining existing supervised and unsupervised training algorithms into growing RBF neural networks. e Growing and Pruning-RBF (GAP-RBF) algorithm evaluates the "meaning" of each neuron based on the average contribution of the neuron to the network output through all input data [26]. After the neuron is evaluated, it may remain in the network or be eliminated.…”