Estimation of the concentrations of hydroxylated polychlorinated biphenyls in human serum using ionization efficiency prediction for electrospray.

Khabazbashi, Sara; Engelhardt, Josefin; Möckel, Claudia; Weiss, Jana; Kruve, Anneli

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.

POLLUTANTS; MATRIX effect; COMPLEX matrices; MACHINE learning; POLYCHLORINATED biphenyls; FORECASTING

Analytical & Bioanalytical Chemistry, 2022, Vol 414, Issue 25, p7451

1618-2642

Academic Journal

10.1007/s00216-022-04096-2