Advanced QSRR Modeling of Organic Pollutants in Natural Water and Wastewater in Gas Chromatography Time-of-Flight Mass Spectrometry

Water pollution is a major global problem which requires ongoing evaluation and revision of water resource policy at all levels (international down to individual aquifers and wells. It has been suggested that it is the leading worldwide cause of deaths and diseases, and that it accounts for the deaths of more than 14,000 people daily. Genetic algorithm-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 retention time (RT) and descriptors for 150 organic contaminants in natural water and wastewater which obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC-TOF MS). The L-M ANN model gave a significantly better performance than the other models. This indicates that L-M ANN can be used as an alternative modeling tool for quantitative structure–retention relationship (QSRR) studies.

Keywords:

Water Pollution , Hazardous chemicals , Organic pollutants , Gas Chromatography , Time-of-flight mass spectrometry , chemometrics , Levenberg-Marquardt artificial neural network