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dc.creatorMohammadzadeh, Ali
dc.creatorRamezani, Majid
dc.date2016-06-10
dc.date.accessioned2019-11-13T15:11:19Z
dc.date.available2019-11-13T15:11:19Z
dc.identifierhttp://www.jcchems.com/index.php/JCCHEMS/article/view/25
dc.identifier.urihttps://revistaschilenas.uchile.cl/handle/2250/112475
dc.descriptionFor 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 6.4×104 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.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherSociedad Chilena de Químicaen-US
dc.relationhttp://www.jcchems.com/index.php/JCCHEMS/article/view/25/26
dc.rightsCopyright (c) 2016 Journal of the Chilean Chemical Societyen-US
dc.sourceJournal of the Chilean Chemical Society; Vol 61 No 2 (2016): Journal of the Chilean Chemical Societyen-US
dc.source0717-9707
dc.source0717-9324
dc.subjectArtificial Neural Networken-US
dc.subjectGenetic Algorithmen-US
dc.subjectCadmiumen-US
dc.subjectIn Syringe Magnet Stirring Assisted Dispersive Liquid-Liquid Microextractionen-US
dc.titleMODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUIDLIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHMen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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