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

Abstract

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 6.4×10 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.