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Dataset for a semi-target analytical method for quantification of OH-PCBs in environmental samples
Dataset - Data and R-Script   Open access

Dataset for a semi-target analytical method for quantification of OH-PCBs in environmental samples

Panithi Saktrakulkla, Ram C Dhakal, Hans-Joachim Lehmler and Keri C. Hornbuckle
University of Iowa
05/29/2019
DOI: 10.25820/036e-b439
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1-Quantification_of_Unknown_OHPCBs-Readme16.04 kBDownloadView
README Description of data, file organization, and methods. Open Access CC BY-NC V4.0
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Data Repository218.98 kBDownloadView
Data files, R script, and manual to generate quadratic model from R script. Open Access CC BY-NC V4.0
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SubstanceIdentifiers5.14 kBDownloadView
This table contains computed descriptors (identifiers) and database identifiers for chemical substances that are represented in this dataset. The identifiers include the internal (ISRP) name and ID, and may include the following if available: CAS number, DSSTox substance ID, InChl Key, IUPAC name, PubChem CID and Canonical SMILES ID. Open Access CC BY-NC V4.0

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Abstract

Hydroxylated polychlorinated biphenyls (OH-PCBs) are oxidative metabolites of PCBs and residuals found in original Aroclors. OH-PCBs are known to play a role as genotoxicants, carcinogens, and hormone disruptors, and therefore it is important to quantify their presence in human tissues, organisms, and environmental matrices. Of 837 possible mono-OH-PCBs congeners, there are only ~70 methoxylated PCB (MeO-PCB) standards commercially available. Hence, a semi-target analytical method is needed for unknown OH-PCBs. The mass concentrations of these unknowns are sometimes determined by assuming the peak responses of other available compounds. This can bias the results due to the choices and availabilities of standards. To overcome this issue, we investigated the peak responses of all commercially available MeO-PCB standards with gas chromatography (GC) coupled with triple quadrupole (QqQ) mass spectrometry (MS) system, with positive electron impact (EI) ionization at 20 Ð 70 eV in selected ion monitoring (SIM) mode. We found correlations between the relative peak responses (RRFs) and the number of chlorines in the molecules of MeO-PCBs (#Cl). Among the studied models, the quadratic regression of #Cl is the most suitable model in the RRF prediction (RRF = B1^#Cl^2 + B0) when the peak responses are captured at 30 eV. We further demonstrate in different chromatography column and GC-EI-MS system. We found the method and associated model to be sufficiently simple, accurate, and versatile for use in quantifying OH-PCBs in complex environmental samples. The data used in the study is in this dataset. We also provide an R-script to generate the predictive quadratic model and its manual for future study.

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