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Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis

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Abstract

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86–122.20% and relative standard deviation (RSD) of the repeatability of 1.14–4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

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Authors and Affiliations

  1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China

    Min Li, Lu Zhang, Xiaolong Yao & Xingyu Jiang

  2. University of the Chinese Academy of Sciences, Beijing, 100049, China

    Min Li, Xiaolong Yao & Xingyu Jiang

Authors
  1. Min Li
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  2. Lu Zhang
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  3. Xiaolong Yao
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  4. Xingyu Jiang
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Correspondence to Lu Zhang.

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Li, M., Zhang, L., Yao, X. et al. Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis. ANAL. SCI. 33, 1225–1230 (2017). https://doi.org/10.2116/analsci.33.1225

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