Discovering hazardous pollutants currently relies on the tedious and often inaccurate structural identification step, required for further toxicity and exposure studies. Here, we propose and validate a workflow for prioritizing the detected features prior to structural elucidation. The proposed approach relies on the priority score (environmental concentration divided by lethal concentration) serving as a proxy of the risk and is deduced solely from mass spectrometric data. The workflow integrates two machine learning approaches, MS2Tox and MS2Quant, that predict toxicity and ionizability of unidentified molecular features, respectively, and the relative risk-based ranking of detected features does not require any additional standards to be measured in the same run, allowing the application to digitally frozen data. Validation by using priority score for classifying 23 chemicals with available risk quotient values into high and low risk categories yielded recall value of 0.33 to 0.81, precision of 0.08 to 0.50, and accuracy of 0.52 to 0.81, depending on the acquisition mode and fish species. Applying the developed workflow to wastewater effluent prioritized 13–19% of features with predicted fingerprints as “precautionary risk features” with a risk quotient ≥1 based on the lower limit of the 95% prediction interval. All prioritized features were subject to spectral library matching, with 12% of the features yielding level 2 identification. Features categorized as high-risk were further subject to structural annotation using in silico identification tools SIRIUS+CSI:FingerID and MetFrag. While plausible candidates were suggested, the in silico tools disagreed in the top 10 suggested structures, highlighting structural assignment as a bottleneck in risk estimation. This work investigates the usefulness of applying data-driven feature prioritization prior to identification.

Non-target screening utilizing liquid chromatography–high-resolution mass spectrometry is increasingly employed for the environmental monitoring of contaminants; however, obtaining quantitative results of detected suspected compounds is challenging; different approaches have been suggested. A recent interlaboratory comparison of quantification approaches showed that the machine learning-based approaches leveraging predicted ionization efficiencies outcompete surrogate standard-based approaches, independent of the method used. In this study, we further analyzed data from the interlaboratory comparison to: (1) evaluate whether the prediction errors could be linked to instrument parameters; and (2) investigate the comparability of response factors (RFs) across different datasets to shed light on the limitations of the instrumental method on the predicted ionization efficiency approach. No specific parameters could be linked to systematic effects on the prediction errors; however, the choice of organic modifier and/or additive type influenced the detection of some compounds. Comparable logRFs across datasets were observed when a linear model was used to project the values to the same scale. Nevertheless, the projected logRF scale was compressed for datasets with low similarity to the anchoring dataset. Moreover, compounds with low logRF showed higher variability across the datasets. The data are freely available and can be interrogated in the developed dashboard.

Prioritizing high-risk features is a key step to reduce workload in non-targeted screening (NTS) when identifying environmental contaminants. Machine learning models from the MS2Tox toolbox have shown promise for feature prioritization, but rely heavily on the accuracy of molecular formulas and fingerprint features provided by SIRIUS + CSI:FingerID. In this study, we introduce and evaluate two new approaches—molecular networking (MN) and conformal predictions—to discover unidentified compounds potentially posing endocrine-disrupting activity based on tandem mass spectral similarity. Furthermore, we revised the previously published MS2Tox models, leveraging molecular fingerprints for seven Tox21 Data Challenge endpoints. The fingerprint-based MS2Tox models achieved the lowest false positive rate, 0.35, at 90% recall on the test set, while MN and CP yielded 0.82 and 0.68, respectively. In a case study of transformation products and persistent chemicals in wastewater, these three approaches prioritized 29 features in influent and effluent samples as potentially associated with AhR agonism among 189 LC/HRMS features corresponding to transformation products and persistent chemicals. All candidate structures for prioritized features showed scaffolds related to AhR binding affinity. Three features were identified on level 1, showcasing potential in using combined feature prioritization strategies.

The persistence of antibiotics such as tetracycline in aquatic systems poses severe environmental and health risks by potential antimicrobial resistance. To address this, a hybrid photo-electro-Fenton oxidation system based on MXene-derived electrodes was developed for efficient tetracycline degradation. The integration of photo, electro, and Fenton processes synergistically enhances hydroxyl radical (•OH) generation and charge-carrier separation, ensuring superior removal efficiency. The cathode was synthesized via a Schiff base formation method, which facilitates functionalization of Ti₃C₂T_x MXene with ferrocene, as confirmed by X-ray diffraction, X-ray photoelectron spectroscopy, and UV–visible spectrometry. The Ti₃C₂–TiO₂ photoanode was fabricated by electrochemical oxidation of Ti₃C₂T_x. The photocatalytic properties of anatase TiO₂, when combined with Ti₃C₂T_x, create a Schottky junction that significantly improves charge separation, thereby enhancing the photo-electrocatalytic activity of the system. The hybrid photo-electro-Fenton (PEF) system demonstrates a substantial enhancement in tetracycline removal efficiency (∼90%) compared to unmodified Ti₃C₂T_x-based electrodes (∼46%). Furthermore, the Ti₃C₂–TiO₂ Schottky photoanode showed enhanced removal efficiency over a commercial P25-based photoanode for photocatalytic degradation. Through this hybrid PEF oxidation system, the removal efficiencies achieved above 90% in neutral and acidic pH, indicating significant efficacy for the advanced oxidation process. The transformation products formed during the PEF process were analyzed with liquid chromatography coupled with high-resolution mass spectrometry, showing breakdown of tetracycline and decreasing ecotoxicity with increasing treatment time. Moreover, the MXene-derived electrode system demonstrates stability and consistent degradation performance over numerous cycles, making it a promising material for environmental remediation applications.

If valorization of tops and branches would be realized, there is potential to increase the overall yield from forestry by 50%, which would impact land use change and climate change considerably. Currently, these residues are either incinerated or left to decay in the forests. The main challenge of valorization of tops and branches is their composition: comprising both wood and bark that are mechanically inseparable. Herein, we propose a chemical approach to valorize all components of both wood and bark of tops and branches without prior mechanical separation. By applying a sequential fractionation sequence involving two different catalytic steps, all different parts of the tops and branches have been valorized into well-defined streams and an intact cellulose-rich pulp. It was found that the sequence of processes was important for the overall outcome. We hope that this first report of the valorization of tops and branches will inspire further studies to unlock the full potential of this challenging yet abundant feedstock.

Liquid chromatography electrospray ionization high-resolution mass spectrometry (LC/ESI/HRMS) is frequently employed in nontargeted screening (NTS) due to its high selectivity and sensitivity. However, data interpretation is challenging since the number of chemical standards available for quantification is limited and the response of the chemicals vastly differs depending on their structure and analysis conditions. Therefore, machine learning (ML) models have been utilized to predict ionization efficiency (IE) and enable the quantification of detected chemicals. It has been observed that the error in the predictions is high for chemicals structurally different from the training data. To enlarge the training set and to accurately predict the IE given a limited labeling budget, active learning (AL) is proposed to acquire informative data points from the targeted chemical space. In the current study, four AL approaches (clustering-based, uncertainty-based, mix, and anticlustering) and a baseline approach (random) were evaluated for IE prediction. The RMSE of the IE in the targeted space dropped significantly (up to 0.3 log units) after a single AL iteration, highlighting the necessity of chemical space exploration before ML model execution. Clustering-based AL reduced the RMSE least, while the uncertainty-based AL was inefficient if ten or more chemicals were sampled in one iteration, thereby reducing its practicality. Finally, expanding the chemical space improved the quantification accuracy from a fold error of 4.13× to 2.94× for five natural products in Alpinia officinarum, thereby demonstrating the need for updating the chemical space coverage of the training set.

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

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.

Quantification is a challenge for non-targeted analysis (NTA) with liquid chromatography–high resolution mass spectrometry (LC–HRMS), due to the lack of analytical standards. Quantification via structure-based predicted ionization efficiency (IE) has been shown to provide the highest accuracy in estimating concentration. However, achieving confident analyte identification is a challenging task, as multiple candidate structures may be likely. This uncertainty in identification limits the reliability of structure-based IE prediction models, since quantification can be severely compromised in cases of wrongly (tentatively) identified chemicals or lack of candidate structures. Here we investigate the possibility of using cumulative neutral losses from fragmentation spectra (i.e. MS2) to predict the logIE. The first model was based on molecular fingerprints and was applied on structurally identified analytes. PubChem fingerprints performed the best with the root-mean-square error (RMSE) of 0.72 logIE for the test set. The second model was based on the MS2 spectrum, expressed as cumulative neutral losses. This approach is applicable to analytes with unknown structures and showed promising results with RMSE of 0.79 logIE for the test set and 0.62 logIE for chromatographic features extracted from LC-HRMS data of tea extracts spiked with pesticides. The prediction models were compiled in a Julia package, which is publicly available on GitHub, and may be used as part of a quantification workflow to estimate concentrations of identified and unidentified compounds in NTA.

Scientific contribution: This study expands the possibilities of standard free quantification for HRMS. It aims to provide reliable IE prediction for known substances by robust fingerprint calculation, and more importantly IE prediction for unknown substances using their MS2 fragmentation pattern. These workflows employ minimal method-specific variables, highlighting the tool generalizability.