Critical evaluation of a simple retention time predictor based on LogKow as a complementary tool in the identification of emerging contaminants in water
Date
2015
Authors
Bade, R.
Bijlsma, L.
Sancho, J.V.
Hernandez, F.
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Journal article
Citation
Talanta, 2015; 139:143-149
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Abstract
There has been great interest in environmental analytical chemistry in developing screening methods based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) for emerging contaminants. Using HRMS, compound identification relies on the high mass resolving power and. mass accuracy attainable by these analyzers. When dealing with wide-scope screening, retention time prediction can be a complementary tool for the identification of compounds, and can also reduce tedious data processing when several peaks appear in the extracted ion chromatograms. There are many in silico, Quantitative Structure-Retention Relationship methods available for the prediction of retention time for LC. However, most of these methods use commercial software to predict retention time based on various molecular descriptors. This paper explores the applicability and makes a critical discussion on a far simpler and cheaper approach to predict retention times by using LogKow. The predictor was based on a database of 595 compounds, their respective LogKow values and a chromatographic run time of 18 min. Approximately 95% of the compounds were found within 4.0 min of their actual retention times, and 70% within 2.0 min. A predictor based purely on pesticides was also made, enabling 80% of these compounds to be found within 2.0 min of their actual retention times. To demonstrate the utility of the predictors, they were successfully used as an additional tool in the identification of 30 commonly found emerging contaminants in water. Furthermore, a comparison was made by using different mass extraction windows to minimize the number of false positives obtained.
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Data source: Supplementary material, http://www.sciencedirect.com/science/article/pii/S0039914015001381#appd002
Link to a related website: http://repositori.uji.es/xmlui/bitstream/10234/146573/1/Bade_2015_Cri.pdf, Open Access via Unpaywall
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Copyright 2015 Elsevier B.V.