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Multivariate optimization of microwave-assisted extraction of zinc and copper from soybean

机译:微波辅助从大豆中提取锌和铜的多元优化

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摘要

In this study, a simple and fast microwave-assisted extraction (MAE) method combined with flame atomic absorption spectrometry (FAAS) was developed and optimized for the extraction and determination of zinc and copper in soybean. The optimization of the MAE conditions was conducted using a full factorial design and Box-Behnken matrix. Firstly, the full factorial design was carried out for preliminary evaluation of the significance of the factors, the factors chosen being: irradiation power, temperature, extraction time, and concentration of nitric acid. The results showed that all of the factors were significant. Because of this, a Box-Behnken experimental design was carried out in order to determine the optimum condition. The total 27 experiments were conducted in the study towards the construction of a quadratic model. According to analysis of variance (ANOVA) results, the proposed model can be used to navigate the design space. High regression coefficient between the variables and the response (R2 = 0.97) indicated excellent evaluation of experimental data by polynomial regression model. The method was applied to the determination of zinc and copper in soybean samples.
机译:在这项研究中,开发了一种简单快速的微波辅助萃取(MAE)结合火焰原子吸收光谱法(FAAS)并优化了大豆中锌和铜的萃取和测定方法。 MAE条件的优化是使用全因子设计和Box-Behnken矩阵进行的。首先,进行全因子设计以初步评估这些因素的重要性,所选择的因素是:辐射功率,温度,萃取时间和硝酸浓度。结果表明,所有因素均显着。因此,为了确定最佳条件,进行了Box-Behnken实验设计。在研究中总共进行了27个实验,以构建二次模型。根据方差分析(ANOVA)结果,所提出的模型可用于导航设计空间。变量与响应之间的高回归系数(R 2 = 0.97)表明多项式回归模型对实验数据具有很好的评价。该方法用于大豆样品中锌和铜的测定。

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