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激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

杨晖 黄林 刘木华 陈添兵 王彩虹 姚明印

分析化学2017,Vol.45Issue(2):238-244,7.
分析化学2017,Vol.45Issue(2):238-244,7.DOI:10.11895/j.issn.0253-3820.160679

激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

Detection of Cd in Chinese Cabbage by Laser Induced Breakdown Spectroscopy Coupled with Multivariable Selection

杨晖 1黄林 2刘木华 2陈添兵 3王彩虹 1姚明印2

作者信息

  • 1. 江西农业大学工学院,南昌330045
  • 2. 江西省高校生物光电及应用重点实验室,南昌330045
  • 3. 江西农业大学生物科学与工程学院,南昌330045
  • 折叠

摘要

Abstract

Heavy metal residue in vegetables is a big concern in the whole world.The aim of this work is to explore the effect of multivariable selection on analyzing Cd in Chinese cabbage polluted in lab by collecting the spectra of laser induced breakdown spectroscopy (LIBS) from the samples.At the same time,the actual Cd content in samples was obtained by anodic stripping voltammetry (ASV).The LIBS spectral range in partial least square (PLS) model was screened by standard normal variable transformation (SNV),first derivative (FD),second derivative (SD) and center treatment (CT) for preprocessing spectra and the optimized method was used for the analysis of interval partial least square (iPLS) and synergy interval partial least square (SiPLS).The results indicated that the method of CT was the best as a comparison with PLS,iPLS and SiPLS.And the intervals of wavelength were 214.72-215.82 nm,215.88-216.97 nm and 225.08 -226.35 nm by utilizing the optimized SiPLS.Here the root mean square error of cross validation (RMSECV) between real content and predicted ones was 1.487,the root mean squared error of prediction (RMSEP) was 1.094,the correlation coefficient (R) was 0.9942,and the average relative error (ARE) was 11.60%.The results displayed that LIBS could predict Cd in vegetables by multivariable selection of SiPLS and the accuracy could meet the requirement of rapid and green analysis of Cd in vegetables.

关键词

激光诱导击穿光谱/青菜//变量筛选

Key words

Laser induced breakdown spectroscopy/Chinese cabbage/Cadmiun/Variable screening

引用本文复制引用

杨晖,黄林,刘木华,陈添兵,王彩虹,姚明印..激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究[J].分析化学,2017,45(2):238-244,7.

基金项目

本文系国家自然科学基金项目(Nos.31560482、31460419)、江西省自然科学基金重大科技项目(No.20143ACB21013)、江西省远航工程计划项目(No.20140142)、江西省水稻产业技术体系专家项目(No.JXARS-02)资助 This work was supported by the National Natural Science Foundation of China (Nos.31560482,31460419) (Nos.31560482、31460419)

分析化学

OA北大核心CSCDCSTPCDSCI

0253-3820

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