Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression. (January 2015)

• Record Type: Journal Article
• Authors: Zhang, Mengliang; Harrington, Peter de B.
• Is Part Of: Chemosphere. Volume 118(2015)
• Page Start: 187
• Page End: 193
• Publication Date: 2015-01
• Subjects: GC/MS -- Partial least squares -- Polychlorinated biphenyls -- Soil -- Solid phase microextraction; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2014.08.018
• Languages: English
• ISSNs: 0045-6535

Abstract

Graphical abstract: Highlights: A fast method was developed for the analysis of Aroclor mixtures in soil matrix. Multivariate and univariate partial least-squares were evaluated for quantification. Different extracted ion chromatograms data representations were investigated for quantification. Quantification of Aroclors were performed under automated fashion. 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 10 μg kg −1 to 1000 μg kg −1 for both Aroclor 1254 and 1260 in soil matrices and the detection limit was 4 μg kg −1 for Aroclor 1254 and 6 μg kg −1 for Aroclor 1260. This holistic approach for the determination of mixtures of complex samples has broad application to environmental forensics and modeling.