Supercritical
fluid chromatography (SFC) is a promising,
sustainable,
and complementary alternative to liquid chromatography (LC) and has
often been coupled with high resolution mass spectrometry (HRMS) for
nontarget screening (NTS). Recent developments in predicting the ionization
efficiency for LC/ESI/HRMS have enabled quantification of chemicals
detected in NTS even if the analytical standards of the detected and
tentatively identified chemicals are unavailable. This poses the question
of whether analytical standard free quantification can also be applied
in SFC/ES/HRMS. We evaluate both the possibility to transfer an ionization
efficiency predictions model, previously trained on LC/ESI/HRMS data,
to SFC/ESI/HRMS as well as training a new predictive model on SFC/ESI/HRMS
data for 127 chemicals. The response factors of these chemicals ranged
over 4 orders of magnitude in spite of a postcolumn makeup flow, expectedly
enhancing the ionization of the analytes. The ionization efficiency
values were predicted based on a random forest regression model from
PaDEL descriptors and predicted values showed statistically significant
correlation with the measured response factors (p< 0.05) with Spearman’s rho of 0.584 and 0.669 for SFC
and LC data, respectively. Moreover, the most significant descriptors
showed similarities independent of the chromatography used for collecting
the training data. We also investigated the possibility to quantify
the detected chemicals based on predicted ionization efficiency values.
The model trained on SFC data showed very high prediction accuracy
with median prediction error of 2.20×, while the model pretrained
on LC/ESI/HRMS data yielded median prediction error of 5.11×.
This is expected, as the training and test data for SFC/ESI/HRMS have
been collected on the same instrument with the same chromatography.
Still, the correlation observed between response factors measured
with SFC/ESI/HRMS and predicted with a model trained on LC data hints
that more abundant LC/ESI/HRMS data prove useful in understanding
and predicting the ionization behavior in SFC/ESI/HRMS.