Resolving the human and environmental exposome is an extremely challenging task due to the complexity of their chemical space (e.g., the number of potential structures). Reversed-phase liquid chromatography (RPLC), particularly C18, coupled to high-resolution mass spectrometry (HRMS) is one of the most dominant techniques for non-targeted analysis (NTA) and can thus be used for mapping the exposome. The NTA approaches, even the most comprehensive available, do not provide any information about the covered chemical spaces. This implies that what was outside of the covered chemical space remains unknown and undetected, which could lead to chemicals of interest (e.g., highly toxic) in samples being undiscoverable for an LC-HRMS method. Another limitation of NTA is the difficulty in confident chemical identification, since multiple compounds may be potential candidates, especially considering the ever-expanding chemical databases.  In this study, a machine learning-based tool was built to assess the coverage of RPLC chemical space. For this purpose, two independent quantitative structure–retention relationship (QSRR)-based models were used to predict both a conventional and a novel type of retention indices (r<sub>i</sub>) for around 100k compounds from the NORMAN database (i.e. SusDat). To assess whether a chemical is analyzable with an RPLC system, we employed a leverage-based and a probability density-based approach. Combining the data gathered from these models, a prediction can be made on whether a compound can be analyzed using a C18 LC column with a certain level of confidence. Overall, the performance assessment showed an accuracy of 92.4% correct classifications and, through this work, a concrete workflow suitable for similar studies was established.

What can be measured with RPLC? A theoretical approach using retention indices

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