Non-target analysis combined with high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (ri) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of ri values without the need for the exact structure of the chemicals, with an R^2 of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 ri units for the NORMAN (n=3131) and amide (n=604) test sets, respectively. This fragment-based model showed comparable accuracy in ri prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained a R2 of 0.85 with and RMSE of 67.  Additionally, the combination of this model and conventionally predicted ri values (i.e. via QSRR) resulted in a complete mapping of the chemical space of an LC-HRMS chromatogram. We demonstrated the applicability of this method with a set of wastewater samples.

Mapping the chemical space of LC-HRMS chromatograms via retention indices

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