Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification

High-resolution mass spectrometry (HRMS)-based suspect
and nontarget
screening has identified a growing number of novel per- and polyfluoroalkyl
substances (PFASs) in the environment. However, without analytical
standards, the fraction of overall PFAS exposure accounted for by
these suspects remains ambiguous. Fortunately, recent developments
in ionization efficiency (IE) prediction using machine
learning offer the possibility to quantify suspects lacking analytical
standards. In the present work, a gradient boosted tree-based model
for predicting log IE in negative mode was trained
and then validated using 33 PFAS standards. The root-mean-square errors
were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the
test set) log IE units. Thereafter, the model was
applied to samples of liver from pilot whales (n = 5; East Greenland)
and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which
contained a significant fraction (up to 70%) of unidentified organofluorine
and 35 unquantified suspect PFASs (confidence level 2–4). IE-based quantification reduced the fraction of unidentified
extractable organofluorine to 0–27%, demonstrating the utility
of the method for closing the fluorine mass balance in the absence
of analytical standards.