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利用激光诱导击穿光谱法检测污染新鲜叶菜中的重金属镉

Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy.

作者信息

Yao Mingyin, Yang Hui, Huang Lin, Chen Tianbing, Rao Gangfu, Liu Muhua

出版信息

Appl Opt. 2017 May 10;56(14):4070-4075. doi: 10.1364/AO.56.004070.

Abstract

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.

摘要

在寻求一种能够对新鲜叶菜类蔬菜中的有毒重金属残留进行绿色分析的新方法时,应用激光诱导击穿光谱法(LIBS)来证明其执行这项工作的能力。通过优化的LIBS实验装置收集实验室污染的新鲜蔬菜样品的光谱,并在湿法消解后通过传统原子吸收光谱法获得样品中镉(Cd)的参考浓度。采用单一Cd谱线强度和Cd浓度进行直接校准暴露了这种校准方法的弱点。此外,以三条Cd谱线作为特征变量,尤其是在对光谱进行预处理后,线性校准的准确性可以略有提高。然而,这在预测样品中的Cd时还不够。因此,利用偏最小二乘回归(PLSR)来增强定量分析的稳健性。PLSR模型的结果表明,Cd目标物的预测准确性能够满足食品安全测定的要求。这项研究表明,LIBS是一种在分析农产品有毒成分方面很有前景的新兴方法,特别是与合适的化学计量学相结合时。

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