The seemingly endless pollution of our environment can hardly have escaped anyone’s notice. Detection of chemicals with known or possible health effects are constantly being reported. The method of choice for analyzing environmental samples is liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS). The high resolving power of HRMS makes it possible to distinguish between very small masses. Together with the use of non-targeted screening, that is to analyze the sample without looking for any predetermined target chemicals, the identification of a wide range of contaminants has been made possible. However, to fully assess the risk any chemical poses, it is essential to know at which concentration it is found in the environment. To obtain this quantitative information, the analysis of analytical standards at several concentrations, at the same time as the analysis of the sample is needed to establish a calibration curve, from where the concentration can be determined. On the other hand, including analytical standards in the analysis, when you do not know what you are looking for, is quite the impossible task. As an alternative, the concentrations can be estimated; instead of using the calibration curves of each exact chemical that is tentatively identified, a calibration curve of another, so called calibration or surrogate standard, can be used. In this large interlaboratory study, we have compared five approaches to quantify chemicals in water samples. Three of the approaches use the calibration curve of a structurally similar or a close eluting surrogate standard, while two approaches use machine learning models for concentration estimations. The results clearly shows that the machine learning models provide more accurate concentration estimates, and that continued research is needed to further refine these methods.

A selection from Stockholm University publication database

• Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS

  2021. Louise Malm (et al.). Molecules 26 (12)

  Article

  Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.

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• MultiConditionRT: Predicting liquid chromatography retention time for emerging contaminants for a wide range of eluent compositions and stationary phases

  2022. Amina Souihi (et al.). Journal of Chromatography A 1666

  Article

  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.

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• Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data

  2023. Helen Sepman (et al.). Analytical Chemistry 95 (33), 12329-12338

  Article

  Nontarget analysis by liquid chromatography-high-resolutionmass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation(MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI: FingerID software. The root mean square errors of the training and test sets were 0.55(3.5x) and 0.80 (6.3x) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2x to 6x. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4x, ageometric mean of 4.5x, and a median of 4.0x. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5x, a geometricmean of 5.6x, and a median of 5.2x on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. TheMS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

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• Scientometric review: Concentration and toxicity assessment in environmental non-targeted LC/HRMS analysis

  2023. Helen Sepman (et al.). Trends in Environmental Analytical Chemistry 40

  Article

  Non-targeted screening with LC/HRMS is a go-to approach to discover relevant contaminants in environmental water samples that contain an abundance of chemicals. The rapidly increasing popularity of non-targeted LC/ HRMS screening has initiated development of a diverse set of methods for assessing the concentration and toxicity of the detected chemicals. This review aims to benchmark the trends in the environmental NTS literature with particular focus on (1) methods used for the quantification of tentatively identified chemicals that lack analytical standards, (2) methods for assessing the toxicity of detected chemicals, and (3) methods combining the former into a risk evaluation. Here we provide a scientometric review of these strategies based on the Web of Science referenced papers published between 2019 and 2022. General trends show that quantification and toxicity assessments are widely employed in NTS, reaching 66 % and 45 % over four years, respectively. Simultaneously, only 13 % of the papers covered here combine these results into a risk factor or similar. With this review we aim to highlight the advantages and gaps in the approaches used for concentration and toxicity assessment and provide guidelines for more homogeneous data interrogation and extrapolation.

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