J4 ›› 2011, Vol. 49 ›› Issue (03): 554-558.

• 环境科学 • 上一篇    下一篇

UAE-DLLME-SFO-HPLC测定沉积物中的十溴联苯醚

李鱼, 王婷, 胡艳, 王夏娇   

  1. 华北电力大学 能源与环境研究院, 北京 102206
  • 收稿日期:2011-01-13 出版日期:2011-05-26 发布日期:2011-06-15
  • 通讯作者: 李鱼 E-mail:liyuxx@jlu.edu.cn

Determination of Decabrominated Diphenyl Ether inSurficial Sediments Based on UAEDLLMESFOHPLC

LI Yu, WANG Ting, HU Yan, WANG Xiajiao   

  1. Research Academy of Energy and Environmental Studies, North China Electric Power University, Beijing 102206, China
  • Received:2011-01-13 Online:2011-05-26 Published:2011-06-15
  • Contact: LI Yu E-mail:liyuxx@jlu.edu.cn

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摘要：

在正交试验设计基础上, 采用GANN模型及Matlab遗传算法工具箱对超声辅助萃取（ultrasonicassisted extraction, UAE）上浮溶剂固化（solidification of floating organic drop, SFO）分散液液微萃取（dispersive liquidliquid microextraction, DLLME）的萃取条件进行优化, 建立了沉积物中十溴联苯醚的液相色谱测定方法. 结果表明: 所建方法线性范围为2~9 595 ng/g, 相关系数R2=0.999 4, 检出限（S/N=3）及定量限（S/N=10）分别为0.6 ng/g及2.0 ng/g; 在434.4 ng/g质量比下, 方法加标回收率为98.20%（RSD=5.2%, n=3）.

关键词: 十溴联苯醚； 遗传神经网络； 上浮溶剂固化； 分散液液微萃取； 高效液相色谱

Abstract:

A method for the determination of decabrominated diphenyl ether (BDE209) in surficial sediments was developed based on solidification of floating organic drop coupled with ultrasonicassisted dispersive liquidliquid microextraction (UAEDLLMESFO) and genetic algorithm neural network (GANN) model. The established UAEDLLMESFOHPLC method has a wide linear range (2— 9 595 ng/g) with R2=0.999 4. The limit of detection (LOD, S/N=3) and limit of quantitation (LOQ, S/N=10) of the proposed method were 0.6 ng/g and 2.0 ng/g, respectively. The recovery of added BDE209 in the real samples
at BDE209 level of 434.4 ng/g was 98.20% (RSD=5.2%, n=3).

Key words: decabrominated diphenyl ether, genetic algorithm neural network, solidification of floating organic drop, dispersive liquidliquid microextraction, high performance liquid chromatography

中图分类号: 

  • X132
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