Systematic selection of mobile phase and column chemistry type can be critical for achieving optimal chromatographic separation, high sensitivity, and low detection limits in liquid chromatography electrospray high resolution mass spectrometry (LC/MS). However, the selection process is challenging for non-targeted screening where the compounds of interest are not preselected nor available for method optimization. To provide general guidance, twenty different mobile phase compositions and four columns were compared for the analysis of 78 compounds with a wide range of physicochemical properties (logP range from -1.46 to 5.48), and analyte sensitivity was compared between methods. The pH, additive type, column, and organic modifier had significant effects on the analyte response factors, and acidic mobile phases (e.g. 0.1% formic acid) yielded highest sensitivity. In some cases, the effect was attributable to the difference in organic modifier content at the time of elution, depending on the mobile phase and column chemistry. Based on these findings, 0.1% formic acid, 0.1% ammonia and 5.0 mM ammonium fluoride were further evaluated for their performance in non-targeted LC/ESI/ HRMS analysis of wastewater treatment plan influent and effluent, using a data dependent MS2 acquisition and two different data processing workflows (MS-DIAL, patRoon 2.1) to compare number of detected features and sensitivity. Both data-processing workflows indicated that 0.1% formic acid yielded the highest number of features in full scan spectrum (MS1), as well as the highest number of features that triggered fragmentation spectra (MS2) when dynamic exclusion was used.

Structural elucidation of compounds detected with liquid chromatography coupled to high resolution mass spectrometry is a challenging and time-consuming step in the workflow of non-targeted analysis and often requires manual validation of the results. Retention time, alongside exact mass, isotope pattern, fragmentation spectra, and collision cross-section, is valuable information for ruling out unlikely structures and increasing the confidence in others. Different approaches to predict retention times have been used previously for reversed phase chromatography and hydrophilic interaction liquid chromatography (HILIC), but application is limited to a small set of mobile phases and gradient profiles. Here, we expand the toolbox available for retention time predictions by developing a random forest regression model for predicting retention times for four column types and twenty mobile phase systems. MultiConditionRT was built using a dataset containing 78 compounds analyzed with C18 reversed phase, mixed mode, HILIC, and biphenyl columns. In addition, different eluent compositions were used: both methanol and acetonitrile were combined with different aqueous phases with pH from 2.1 to 10.0 (formic acid, acetic acid, trifluoroacetic acid, formate, acetate, bicarbonate, and ammonia). The root mean square error (RMSE) of the test set predictions was 1.55 min for C18 reversed phase, 1.79 min for mixed-mode, 1.93 min for HILIC, and 1.56 min for biphenyl column. Additionally, MultiConditionRT can be applied to different gradient profiles with a general additive model-based calibration approach. The approach of MultiConditionRT was validated externally and internally with 356 and 151 compounds respectively, yielding an RMSE of 2.68 and 2.32 min. 324 and 84 of these compounds were not in the dataset used in the model development.

Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C₁₈ LC method.