Estimating LoD‑s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS

Nontargeted LC/ESI/HRMS
aims to detect and identify organic compounds
present in the environment without prior knowledge; however, in practice
no LC/ESI/HRMS method is capable of detecting all chemicals, and the
scope depends on the instrumental conditions. Different experimental
conditions, instruments, and methods used for sample preparation and
nontargeted LC/ESI/HRMS as well as different workflows for data processing
may lead to challenges in communicating the results and sharing data
between laboratories as well as reduced reproducibility. One of the
reasons is that only a fraction of method performance characteristics
can be determined for a nontargeted analysis method due to the lack
of prior information and analytical standards of the chemicals present
in the sample. The limit of detection (LoD) is one of the most important
performance characteristics in target analysis and directly describes
the detectability of a chemical. Recently, the identification and
quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization
efficiency, risk scores, and retention times) have significantly improved
due to employing machine learning. In this work, we hypothesize that
the predicted ionization efficiency could be used to estimate LoD
and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted
method for the detection of suspected chemicals even if analytical
standards are lacking. For this, 221 representative compounds were
selected from the NORMAN SusDat list (S0), and LoD values were determined
by using 4 complementary approaches. The LoD values were correlated
to ionization efficiency values predicted with previously trained
random forest regression. A robust regression was then used to estimate
LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD
values were used for prioritization of the unknown features. Furthermore,
we present LoD values for the NORMAN SusDat list with a reversed-phase
C18 LC method.