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首页> 外文期刊>Arabian Journal for Science and Engineering >Application of Response Surface Methodology and Genetic Algorithm for Optimization and Determination of Iron in Food Samples by Dispersive Liquid-Liquid Microextraction Coupled UV-Visible Spectrophotometry
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Application of Response Surface Methodology and Genetic Algorithm for Optimization and Determination of Iron in Food Samples by Dispersive Liquid-Liquid Microextraction Coupled UV-Visible Spectrophotometry

机译：响应面法和遗传算法在分散液-液微萃取-紫外可见分光光度法测定和测定食品中铁中的应用

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摘要

A simple and facile method was developed for the determination of trace amount of iron. The method is based on the complex formation between Fe (III) and picrate anion in the presence of piroxicam, as a complexing agent. Dispersive liquid-liquid microextraction (DLLME) was applied to extract the formed ion associate, Fe (III)-piroxicam. The absorbance of the extracted iron in the sedimented phase was measured by UV-Vis spectrophotometry. Two statistical methods of response surface methodology and genetic algorithm (GA) based on artificial neural network (ANN) were employed for prediction and optimization of a four-constituent DLLME. Plackett-Burman design was used for screening the influential parameters including pH, the volume of picrate anion, disperser, and extraction solvents. Central composite design (CCD) was used to obtain the optimum levels in the proposed method. The experimentally obtained data were used to train the GA model. CCD and GA models were compared for their predictive abilities. The result showed that both models have the ability to predict the proposed process, but ANN model is more reliable than CCD. The absorbance of the extracted iron obeys Beer's law in the range of 0.03-0.96 , and the limit of detection of 0.008 and enhancement factor of 88.84 were achieved for the process. The developed procedure was successfully applied to the determination of iron in water samples and two types of common vegetation sample, i.e., tea and mint.
机译：开发了一种简便的方法来测定痕量铁。该方法基于在吡罗昔康作为络合剂存在下，Fe（III）与苦味酸根阴离子之间的络合物形成。应用分散液-液微萃取（DLLME）提取形成的离子缔合体Fe（III）-吡罗昔康。通过UV-Vis分光光度法测量提取的铁在沉淀相中的吸光度。基于响应神经网络的两种统计方法和基于人工神经网络（ANN）的遗传算法（GA）被用于四成分DLLME的预测和优化。 Plackett-Burman设计用于筛选影响参数，包括pH值，苦味酸根阴离子的体积，分散剂和萃取溶剂。中央复合设计（CCD）被用来在所提出的方法中获得最佳水平。实验获得的数据用于训练GA模型。比较了CCD和GA模型的预测能力。结果表明，这两个模型都具有预测所提出的过程的能力，但是ANN模型比CCD更可靠。提取铁的吸光度符合比尔定律在0.03-0.96范围内，该方法的检出限为0.008，增强因子为88.84。所开发的方法已成功地用于测定水样和两种常见植被样品中的铁，即茶和薄荷。

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