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首页> 外文期刊>International journal of environmental analytical chemistry >Homogeneous liquid-liquid microextraction via flotation assistance coupled with gas chromatography-mass spectrometry for determination of myclobutanil in cucumber, tomato, grape, and strawberry using genetic algorithm
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Homogeneous liquid-liquid microextraction via flotation assistance coupled with gas chromatography-mass spectrometry for determination of myclobutanil in cucumber, tomato, grape, and strawberry using genetic algorithm

机译：浮选助剂-气相色谱-质谱联用-液-液微萃取-遗传算法测定黄瓜，番茄，葡萄和草莓中的甲基丁腈

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摘要

Facile and potent homogeneous liquid-liquid microextraction via flotation assistance method (HLLME-FA) combined with gas chromatography-mass spectrometry was proposed for determination of trace amounts of myclobutanil in fruit and vegetable samples. The paramount parameters, such as extraction and homogeneous solvent types and volumes, ionic strength and extraction time were studied. Under optimum conditions, the detection limit of 0.005 ng g(-1), the linear range of 0.05-100 ng g(-1), and the precision of 3.8% were acquired. A three-layer artificial neural network (ANN) model was used with 10 neurons and tan-sigmoid function at hidden layer and a linear transfer function at output layer were developed to predict the process. The results indicated that the proposed ANN model could perfectly predict the process with the mean square error of 0.89%. Then genetic algorithm was utilised to optimise the parameters. The proposed procedure showed satisfactory results for analysis of cucumber, tomato, grape, and strawberry.
机译：提出了一种简便有效的通过浮选辅助方法（HLLME-FA）与气相色谱-质谱联用的均匀液-液微萃取方法，用于水果和蔬菜样品中痕量霉菌丁的测定的方法。研究了最重要的参数，例如萃取和均相溶剂的类型和体积，离子强度和萃取时间。在最佳条件下，检测限为0.005 ng g（-1），线性范围为0.05-100 ng g（-1），精确度为3.8％。使用三层人工神经网络（ANN）模型，其中包含10个神经元，并在隐藏层使用tan乙状结肠功能，并在输出层开发了线性传递函数以预测过程。结果表明，所提出的人工神经网络模型可以完美地预测过程，均方误差为0.89％。然后利用遗传算法对参数进行优化。该方法对黄瓜，番茄，葡萄和草莓的分析显示出令人满意的结果。

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