Ionization efficiency prediction of electrospray ionization mass spectrometry analytes based on molecular fingerprints and cumulative neutral losses.

Quantification is a challenge for non-targeted analysis (NTA) with liquid chromatography-high resolution mass spectrometry (LC-HRMS), due to the lack of analytical standards. Quantification via structure-based predicted ionization efficiency (IE) has been shown to provide the highest accuracy in estimating concentration. However, achieving confident analyte identification is a challenging task, as multiple candidate structures may be likely. This uncertainty in identification limits the reliability of structure-based IE prediction models, since quantification can be severely compromised in cases of wrongly (tentatively) identified chemicals or lack of candidate structures. Here we investigate the possibility of using cumulative neutral losses from fragmentation spectra (i.e. MS2) to predict the logIE. The first model was based on molecular fingerprints and was applied on structurally identified analytes. PubChem fingerprints performed the best with the root-mean-square error (RMSE) of 0.72 logIE for the test set. The second model was based on the MS2 spectrum, expressed as cumulative neutral losses. This approach is applicable to analytes with unknown structures and showed promising results with RMSE of 0.79 logIE for the test set and 0.62 logIE for chromatographic features extracted from LC-HRMS data of tea extracts spiked with pesticides. The prediction models were compiled in a Julia package, which is publicly available on GitHub, and may be used as part of a quantification workflow to estimate concentrations of identified and unidentified compounds in NTA. Scientific contribution: This study expands the possibilities of standard free quantification for HRMS. It aims to provide reliable IE prediction for known substances by robust fingerprint calculation, and more importantly IE prediction for unknown substances using their MS2 fragmentation pattern. These workflows employ minimal method-specific variables, highlighting the tool generalizability.