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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Chemical Methodologies
- نوع مقاله: Journal Article
- کلمات کلیدی: Gas chromatography,water pollution,Hazardous chemicals,Organic pollutants,Time-of-flight mass spectrometry,chemometrics,Levenberg-Marquardt artificial neural network
- چکیده:
- چکیده انگلیسی: 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.
- انتشار مقاله: 15-07-1396
- نویسندگان: Mehrdad Shahpar,Sharmin Esmaeilpoor
- مشاهده
- جایگاه : پژوهشی
- مجله: Chemical Methodologies
- نوع مقاله: Journal Article
- کلمات کلیدی: Doping agents,Ultra-high-pressure liquid chromatogram,QSRR,Genetic Algorithms
- چکیده:
- چکیده انگلیسی: 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.
- انتشار مقاله: 03-06-1396
- نویسندگان: Mehrdad Shahpar,Sharmin Esmaeilpoor
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Journal of Green Chemistry
- نوع مقاله: Journal Article
- کلمات کلیدی: Ecotoxicity,Environmental hazard,Phenols,Anilines,Quantitative stature retention relationship
- چکیده:
- چکیده انگلیسی: 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.
- انتشار مقاله: 15-07-1396
- نویسندگان: Mehrdad Shahpar,Sharmin Esmaeilpoor
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Journal of Green Chemistry
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Petroleum substances,QSAR
- چکیده:
- چکیده انگلیسی: Life and its extraction fuels climate change. We performed studies upon an extended series of petroleum hydrocarbons, with octanol-water partition coefficients (log Kow), by using the quantitative structure-activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors, resulting in the best-fit models. The partial least squares PLS (PLS) was utilized to construct the linear QSAR model. The best GA-PLS model contains 27 selected descriptors in 10 latent variables space. The R2 and RMSE for training and test sets were (0.827, 0.088) and (0.716, 0.185), respectively. Inspection of the results reveals a higher R2 and lowers the RMSE value parameter for the data set GA-PLS. The GA-PLS linear model has good statistical quality with low prediction error. This is the first research on the QSAR which uses GA-PLS for the presiction octanol-water partition coefficients of some of the environmental toxic of the petroleum substances.
- انتشار مقاله: 12-05-1396
- نویسندگان: Mehrdad Shahpar,Sharmin Esmaeilpoor
- مشاهده