In-Depth Analysis of the Data from an Interlaboratory Study of Quantitative Non-Target Screening-How Do the...

Non-target screening utilizing liquid chromatography-high-resolution mass spectrometry is increasingly employed for the environmental monitoring of contaminants; however, obtaining quantitative results of detected suspected compounds is challenging; different approaches have been suggested. A recent interlaboratory comparison of quantification approaches showed that the machine learning-based approaches leveraging predicted ionization efficiencies outcompete surrogate standard-based approaches, independent of the method used. In this study, we further analyzed data from the interlaboratory comparison to: (1) evaluate whether the prediction errors could be linked to instrument parameters; and (2) investigate the comparability of response factors (RFs) across different datasets to shed light on the limitations of the instrumental method on the predicted ionization efficiency approach. No specific parameters could be linked to systematic effects on the prediction errors; however, the choice of organic modifier and/or additive type influenced the detection of some compounds. Comparable logRFs across datasets were observed when a linear model was used to project the values to the same scale. Nevertheless, the projected logRF scale was compressed for datasets with low similarity to the anchoring dataset. Moreover, compounds with low logRF showed higher variability across the datasets. The data are freely available and can be interrogated in the developed dashboard.