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Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression.
Chemosphere. 2015 Jan; 118:187-93.C

Abstract

Multivariate partial least-squares (PLS) method was applied to the quantification of two complex polychlorinated biphenyls (PCBs) commercial mixtures, Aroclor 1254 and 1260, in a soil matrix. PCBs in soil samples were extracted by headspace solid phase microextraction (SPME) and determined by gas chromatography/mass spectrometry (GC/MS). Decachlorinated biphenyl (deca-CB) was used as internal standard. After the baseline correction was applied, four data representations including extracted ion chromatograms (EIC) for Aroclor 1254, EIC for Aroclor 1260, EIC for both Aroclors and two-way data sets were constructed for PLS-1 and PLS-2 calibrations and evaluated with respect to quantitative prediction accuracy. The PLS model was optimized with respect to the number of latent variables using cross validation of the calibration data set. The validation of the method was performed with certified soil samples and real field soil samples and the predicted concentrations for both Aroclors using EIC data sets agreed with the certified values. The linear range of the method was from 10μgkg(-1) to 1000μgkg(-1) for both Aroclor 1254 and 1260 in soil matrices and the detection limit was 4μgkg(-1) for Aroclor 1254 and 6μgkg(-1) for Aroclor 1260. This holistic approach for the determination of mixtures of complex samples has broad application to environmental forensics and modeling.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Zhang M
Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701-2979, USA.
Harrington Pde B
Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701-2979, USA. Electronic address: peter.harrington@ohio.edu.

MeSH

AroclorsCalibrationChlorodiphenyl (54% Chlorine)Equipment DesignGas Chromatography-Mass SpectrometryLeast-Squares AnalysisLimit of DetectionModels, StatisticalMultivariate AnalysisSoil PollutantsSolid Phase Microextraction

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

25216382

Citation

Zhang, Mengliang, and Peter de B. Harrington. "Simultaneous Quantification of Aroclor Mixtures in Soil Samples By Gas Chromatography/mass Spectrometry With Solid Phase Microextraction Using Partial Least-squares Regression." Chemosphere, vol. 118, 2015, pp. 187-93.
Zhang M, Harrington Pde B. Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression. Chemosphere. 2015;118:187-93.
Zhang, M., & Harrington, P. d. e. . B. (2015). Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression. Chemosphere, 118, 187-93. https://doi.org/10.1016/j.chemosphere.2014.08.018
Zhang M, Harrington Pde B. Simultaneous Quantification of Aroclor Mixtures in Soil Samples By Gas Chromatography/mass Spectrometry With Solid Phase Microextraction Using Partial Least-squares Regression. Chemosphere. 2015;118:187-93. PubMed PMID: 25216382.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression. AU - Zhang,Mengliang, AU - Harrington,Peter de B, Y1 - 2014/09/15/ PY - 2014/02/25/received PY - 2014/08/11/revised PY - 2014/08/12/accepted PY - 2014/9/13/entrez PY - 2014/9/13/pubmed PY - 2015/7/17/medline KW - GC/MS KW - Partial least squares KW - Polychlorinated biphenyls KW - Soil KW - Solid phase microextraction SP - 187 EP - 93 JF - Chemosphere JO - Chemosphere VL - 118 N2 - Multivariate partial least-squares (PLS) method was applied to the quantification of two complex polychlorinated biphenyls (PCBs) commercial mixtures, Aroclor 1254 and 1260, in a soil matrix. PCBs in soil samples were extracted by headspace solid phase microextraction (SPME) and determined by gas chromatography/mass spectrometry (GC/MS). Decachlorinated biphenyl (deca-CB) was used as internal standard. After the baseline correction was applied, four data representations including extracted ion chromatograms (EIC) for Aroclor 1254, EIC for Aroclor 1260, EIC for both Aroclors and two-way data sets were constructed for PLS-1 and PLS-2 calibrations and evaluated with respect to quantitative prediction accuracy. The PLS model was optimized with respect to the number of latent variables using cross validation of the calibration data set. The validation of the method was performed with certified soil samples and real field soil samples and the predicted concentrations for both Aroclors using EIC data sets agreed with the certified values. The linear range of the method was from 10μgkg(-1) to 1000μgkg(-1) for both Aroclor 1254 and 1260 in soil matrices and the detection limit was 4μgkg(-1) for Aroclor 1254 and 6μgkg(-1) for Aroclor 1260. This holistic approach for the determination of mixtures of complex samples has broad application to environmental forensics and modeling. SN - 1879-1298 UR - https://www.unboundmedicine.com/medline/citation/25216382/Simultaneous_quantification_of_Aroclor_mixtures_in_soil_samples_by_gas_chromatography/mass_spectrometry_with_solid_phase_microextraction_using_partial_least_squares_regression_ DB - PRIME DP - Unbound Medicine ER -