Application of ANN-GA in the Development of a Microwave Assisted Extraction Method for Determination of Multi-elemental Determination in Tea Samples
Mostafa Khajeh and Farahnaz Nooshiravani
Affiliation: Department of Chemistry, University of Zabol, P.O. Box 98615-538, Zabol, Iran.
Keywords: Artificial neural network, copper, flame atomic absorption spectrometry, genetic algorithm, iron, manganese, microwave-
assisted extraction, sensivity analysis, tea, zinc.
In this study, hybrid of artificial neural network-genetic algorithm (ANN-GA) was used for the development of a microwave-assisted extraction method for determination of target element (zinc, copper, iron and manganese) in tea samples using flame atomic absorption spectrometry (FAAS). A multiple response function (Rm) was applied to describe the experimental conditions for simultaneous extraction of the target element. The power, temperature, extraction time and volume of solvents were the input variables, while the Rm was the output. Optimum conditions were 360 W, 103 °C, 27 min and 2.7:7.3 mL for power, temperature, time and volume of nitric acid:hydrogen peroxide (as solvents), respectively. High determination coefficient between the actual and the predicted data by ANN model (R2= 0.983) indicated the goodness of fit. The developed procedure was then applied to the extraction and determination of these elements in the some tea samples.
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