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首页> 外文期刊>Applied optics >Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy
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Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy

机译:激光诱导击穿光谱检测污染新鲜叶蔬菜中重金属CD

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摘要

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdown spectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics. (C) 2017 Optical Society of America
机译:在寻求一种新的方法在新鲜的叶形蔬菜中监测有毒重金属残留物中的绿色分析能力,应用激光诱导的击穿光谱(LIB)来证明其在执行这项工作方面的能力。通过优化的Libs实验装置收集在实验室中污染的新鲜蔬菜样品的光谱,并且通过湿法消化后的常规原子吸收光谱获得来自样品的镉(CD)的参考浓度。采用单CD线和Cd浓度的直接校准和CD浓度暴露了这种校准方法的弱点。此外,通过三重CD线作为特征变量,线性校准的准确性可以改善,特别是在预处理的光谱之后。然而,在预测样本中的CD时不够。因此,利用部分最小二乘回归(PLSR)来增强定量分析的稳健性。 PLSR模型的结果表明,CD目标的预测精度可以满足食品安全的确定。本研究提出了LIBS是一种有前途和新兴的方法,用于分析农产品中的有毒组合物,特别是与合适的化学计量学相结合。 (c)2017年光学学会

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  • 来源
    《Applied optics》 |2017年第14期|共6页
  • 作者单位

    Jiangxi Agr Univ Coll Engn Nanchang 330045 Jiangxi Peoples R China;

    Jiangxi Agr Univ Coll Engn Nanchang 330045 Jiangxi Peoples R China;

    Collaborat Innovat Ctr Postharvest Key Technol &

    Nanchang 330045 Jiangxi Peoples R China;

    Jiangxi Agr Univ Coll Engn Nanchang 330045 Jiangxi Peoples R China;

    Jiangxi Agr Univ Coll Engn Nanchang 330045 Jiangxi Peoples R China;

    Jiangxi Agr Univ Coll Engn Nanchang 330045 Jiangxi Peoples R China;

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  • 正文语种 eng
  • 中图分类 应用;
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