MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM

For the first time, artificial neural network (ANN) and genetic algorithm (GA) have been employed to modeling and optimization of in syringe magnet stirring assisted dispersive liquid-liquid microextraction (IS-MSA-DLLME) method for extraction of cadmium from food samples and determined by flame ato...

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Autores principales:	MOHAMMADZADEH,ALI, RAMEZANI,MAJID
Lenguaje:	English
Publicado:	Sociedad Chilena de Química 2016
Materias:	Artificial Neural Network Genetic Algorithm Cadmium In Syringe Magnet Stirring Assisted Dispersive Liquid-Liquid Microextraction
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072016000200024
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spelling	oai:scielo:S0717-970720160002000242016-09-14MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHMMOHAMMADZADEH,ALIRAMEZANI,MAJID Artificial Neural Network Genetic Algorithm Cadmium In Syringe Magnet Stirring Assisted Dispersive Liquid-Liquid Microextraction For the first time, artificial neural network (ANN) and genetic algorithm (GA) have been employed to modeling and optimization of in syringe magnet stirring assisted dispersive liquid-liquid microextraction (IS-MSA-DLLME) method for extraction of cadmium from food samples and determined by flame atomic absorption spectrometry. Based on one factor at a time optimization method, the different input variables for modeling were chosen as pH of the solution, extraction volume, stirring rate and extraction time. The ANN techniques fitted a model for extraction of cadmium with 8, 0.9988 and 6.4x10-4 neurons, correlation coefficient and mean standard error (MSE), respectively. By using the GA technique, the optimal conditions were achieved at pH 7, extraction volume at 250 μL, stirring rate of 500 rpm and extraction time of 10 min. Under the optimum conditions, the calibration graph was linear over the range of 0.05 - 1.00 μg L-1 and the limits of detection (LOD) were as small as 0.015 μg mL-1. The relative standard deviation was ±2.11% (n = 7) and the enrichment factor was 280. The developed method was successfully applied to the extraction and determination of cadmium in food samples.info:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.61 n.2 20162016-06-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072016000200024en10.4067/S0717-97072016000200024
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Artificial Neural Network Genetic Algorithm Cadmium In Syringe Magnet Stirring Assisted Dispersive Liquid-Liquid Microextraction
spellingShingle	Artificial Neural Network Genetic Algorithm Cadmium In Syringe Magnet Stirring Assisted Dispersive Liquid-Liquid Microextraction MOHAMMADZADEH,ALI RAMEZANI,MAJID MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
description	For the first time, artificial neural network (ANN) and genetic algorithm (GA) have been employed to modeling and optimization of in syringe magnet stirring assisted dispersive liquid-liquid microextraction (IS-MSA-DLLME) method for extraction of cadmium from food samples and determined by flame atomic absorption spectrometry. Based on one factor at a time optimization method, the different input variables for modeling were chosen as pH of the solution, extraction volume, stirring rate and extraction time. The ANN techniques fitted a model for extraction of cadmium with 8, 0.9988 and 6.4x10-4 neurons, correlation coefficient and mean standard error (MSE), respectively. By using the GA technique, the optimal conditions were achieved at pH 7, extraction volume at 250 μL, stirring rate of 500 rpm and extraction time of 10 min. Under the optimum conditions, the calibration graph was linear over the range of 0.05 - 1.00 μg L-1 and the limits of detection (LOD) were as small as 0.015 μg mL-1. The relative standard deviation was ±2.11% (n = 7) and the enrichment factor was 280. The developed method was successfully applied to the extraction and determination of cadmium in food samples.
author	MOHAMMADZADEH,ALI RAMEZANI,MAJID
author_facet	MOHAMMADZADEH,ALI RAMEZANI,MAJID
author_sort	MOHAMMADZADEH,ALI
title	MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
title_short	MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
title_full	MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
title_fullStr	MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
title_full_unstemmed	MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
title_sort	modeling and optimization of in syringe magnet stirring assisted-dispersive liquid-liquid microextraction method for extraction of cadmium from food samples by artificial neural network and genetic algorithm
publisher	Sociedad Chilena de Química
publishDate	2016
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072016000200024
work_keys_str_mv	AT mohammadzadehali modelingandoptimizationofinsyringemagnetstirringassisteddispersiveliquidliquidmicroextractionmethodforextractionofcadmiumfromfoodsamplesbyartificialneuralnetworkandgeneticalgorithm AT ramezanimajid modelingandoptimizationofinsyringemagnetstirringassisteddispersiveliquidliquidmicroextractionmethodforextractionofcadmiumfromfoodsamplesbyartificialneuralnetworkandgeneticalgorithm
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MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUID-LIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM

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