Do experimental projection methods outcompete retention time prediction models in non-target screening? A case study on LC/HRMS interlaboratory comparison data

Abstract

Retention time (RT) is essential in evaluating the likelihood of candidate structures in nontarget screening (NTS) with liquid chromatography high resolution mass spectrometry (LC/HRMS). Approaches for estimating the RTs of candidate structures can broadly be divided into projection and prediction methods. The first approach takes advantage of public databases of RTs measured on similar chromatographic systems (CS_source) and projects these to the chromatographic system applied in the NTS (CS_NTS) based on a small set of commonly analyzed chemicals. The second approach leverages machine learning (ML) model(s) trained on publicly available retention time data measured on one or more chromatographic systems (CS_training). Nevertheless, the CS_source and CS_training might differ substantially from CS_NTS. Therefore, it is of interest to evaluate the generalizability of projection models and prediction models in CSs routinely applied in NTS. Here we take advantage of the recent NORMAN interlaboratory comparison where 41 known calibration chemicals and 45 suspects were analyzed to evaluate both the projection and prediction approaches on 37 CSs. The accuracy of both approaches was directly linked to the similarity of the CS, and the pH of the mobile phase and the column chemistry were found to be most impactful. Furthermore, for cases where CS_source and CS_NTS differ substantially but CS_training and CS_NTS are similar, prediction models often performed on par with the projection models. These findings highlight the need to account for the mobile phase and column chemistry in ML model training and select the prediction model for RT.