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2022 (English)In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 414, no 25, p. 7451-7460Article in journal (Refereed) Published
Abstract [en]
Hydroxylated PCBs are an important class of metabolites of the widely distributed environmental contaminants polychlorinated biphenyls (PCBs). However, the absence of authentic standards is often a limitation when subject to detection, identification, and quantification. Recently, new strategies to quantify compounds detected with non-targeted LC/ESI/HRMS based on predicted ionization efficiency values have emerged. Here, we evaluate the impact of chemical space coverage and sample matrix on the accuracy of ionization efficiency-based quantification. We show that extending the chemical space of interest is crucial in improving the performance of quantification. Therefore, we extend the ionization efficiency-based quantification approach to hydroxylated PCBs in serum samples with a retraining approach that involves 14 OH-PCBs and validate it with an additional four OH-PCBs. The predicted and measured ionization efficiency values of the OH-PCBs agreed within the mean error of 2.1 × and enabled quantification with the mean error of 4.4 × or better. We observed that the error mostly arose from the ionization efficiency predictions and the impact of matrix effects was of less importance, varying from 37 to 165%. The results show that there is potential for predictive machine learning models for quantification even in very complex matrices such as serum. Further, retraining the already developed models provides a timely and cost-effective solution for extending the chemical space of the application area.
Keywords
Non-targeted screening, Machine learning, Matrix effect, Ionization efficiency, Quantification, LC, HRMS
National Category
Earth and Related Environmental Sciences Chemical Sciences
Identifiers
urn:nbn:se:su:diva-204764 (URN)10.1007/s00216-022-04096-2 (DOI)000981910500008 ()35507099 (PubMedID)2-s2.0-85129537554 (Scopus ID)
2022-05-192022-05-192025-01-31Bibliographically approved