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

Quantification Approaches in Non-Target LC/ESI/HRMS Analysis: An Interlaboratory Comparison

Nontargeted screening (NTS) utilizing liquid chromatography electrospray ionization high-resolution mass spectrometry (LC/ESI/HRMS) is increasingly used to identify environmental contaminants. Major differences in the ionization efficiency of compounds in ESI/HRMS result in widely varying responses and complicate quantitative analysis. Despite an increasing number of methods for quantification without authentic standards in NTS, the approaches are evaluated on limited and diverse data sets with varying chemical coverage collected on different instruments, complicating an unbiased comparison. In this interlaboratory comparison, organized by the NORMAN Network, we evaluated the accuracy and performance variability of five quantification approaches across 41 NTS methods from 37 laboratories. Three approaches are based on surrogate standard quantification (parent-transformation product, structurally similar or close eluting) and two on predicted ionization efficiencies (RandFor-IE and MLR-IE). Shortly, HPLC grade water, tap water, and surface water spiked with 45 compounds at 2 concentration levels were analyzed together with 41 calibrants at 6 known concentrations by the laboratories using in-house NTS workflows. The accuracy of the approaches was evaluated by comparing the estimated and spiked concentrations across quantification approaches, instrumentation, and laboratories. The RandFor-IE approach performed best with a reported mean prediction error of 15× and over 83% of compounds quantified within 10× error. Despite different instrumentation and workflows, the performance was stable across laboratories and did not depend on the complexity of water matrices.

Chemical profiling of organic contaminants in rural surface waters combining target and non-target LC-HRMS/MS analysis

The pollution of natural waters by contaminants of emerging concern (CECs) is one of the pressing problems due to their global distribution and potential negative effects on the environment and human health. In rural areas with lower population density and limited industrial development, less contamination is expected. However, the lack of wastewater treatment plants (WWTPs) or their poor removal efficiency can lead to significant input of pollutants. In this context, 11 streams of rural areas in the Guadalquivir River basin, southeast of Spain, were studied over two years to obtain an overview of the origin and distribution of contaminants. A target method using solid-phase extraction and liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) was developed for the analysis of 316 compounds in surface waters. A total of 78 target analytes were detected, comprising pesticides, pharmaceuticals, personal care products (PCPs), transformation products (TPs), and industrial chemicals. The flame retardant tributyl phosphate (16-3572 ng L-1) was detected in all samples, followed by caffeine (30-8090 ng L-1) and the analgesic tramadol (3-1493 ng L-1). The target approach was combined with a non-target analysis (NTA) strategy to obtain an overall perspective of the chemical profile of unexpected or unknown compounds in the samples. Up to 79 contaminants were tentatively identified, and 12 of them were finally confirmed with standards. Most of the contaminants determined by NTA were pharmaceuticals and their TPs. The results indicated that most of CECs have an urban origin despite traditional agriculture is the main economic activity in this region. Moreover, the absence of WWTPs in small towns is significant, as contamination levels at these sites were comparable to or higher than those in larger populations with sewage treatments.

Prioritization, Identification, and Quantification of Emerging Contaminants in Recycled Textiles Using Non-Targeted and Suspect Screening Workflows by LC-ESI-HRMS

Recycled textiles are becoming widely available to consumers as manufacturers adopt circular economy principles to reduce the negative impact of garment production. Still, the quality of the source material directly impacts the final product, where the presence of harmful chemicals is of utmost concern. Here, we develop a risk-based suspect and non-targeted screening workflow for the detection, identification, and prioritization of the chemicals present in consumer-based recycled textile products after manufacture and transport. We apply the workflow to characterize 13 recycled textile products from major retail outlets in Sweden. Samples were extracted and analyzed by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). In positive and negative ionization mode, 20,119 LC-HRMS features were detected and screened against persistent, mobile, and toxic (PMT) as well as other textile-related chemicals. Six substances were matched with PMT substances that are regulated in the European Union (EU) with a Level 2/3 confidence. Forty-three substances were confidently matched with textile-related chemicals reported for use in Sweden. For estimating the relative priority score, aquatic toxicity and concentrations were predicted for 7416 features with tandem mass spectra (MS2) and used to rank the non-targeted features. The top 10 substances were evaluated due to elevated environmental risk linked to the recycling process and potential release at end-of-life.

ICP-MS As a Contributing Tool to Nontarget Screening (NTS) Analysis for Environmental Monitoring

Due to the increasing number of chemicals released into the environment, nontarget screening (NTS) analysis is a necessary tool for providing comprehensive chemical analysis of environmental pollutants. However, NTS workflows encounter challenges in detecting both known and unknown pollutants with common chromatography high-resolution mass spectrometry (HRMS) methods. Identification of unknowns is hindered by limited elemental composition information, and quantification without identical reference standards is prone to errors. To address these issues, we propose the use of inductively coupled plasma mass spectrometry (ICP-MS) as an element-specific detector. ICP-MS can enhance the confidence of compound identification and improve quantification in NTS due to its element-specific response and unambiguous chemical composition information. Additionally, mass balance calculations for individual elements (F, Br, Cl, etc.) enable assessment of total recovery of those elements and evaluation of NTS workflows. Despite its benefits, implementing ICP-MS in NTS analysis and environmental regulation requires overcoming certain shortcomings and challenges, which are discussed herein.

Sequential Anaerobic-Aerobic Treatment Enhances Sulfamethoxazole Removal: From Batch Cultures to Observations in a Large-Scale Wastewater Treatment Plant

Sulfamethoxazole (SMX) passes through conventional wastewater treatment plants (WWTPs) mainly unaltered. Under anoxic conditions sulfate-reducing bacteria can transform SMX but the fate of the transformation products (TPs) and their prevalence in WWTPs remain unknown. Here, we report the anaerobic formation and aerobic degradation of SMX TPs. SMX biotransformation was observed in nitrate- and sulfate-reducing enrichment cultures. We identified 10 SMX TPs predominantly showing alterations in the heterocyclic and N4-arylamine moieties. Abiotic oxic incubation of sulfate-reducing culture filtrates led to further degradation of the major anaerobic SMX TPs. Upon reinoculation under oxic conditions, all anaerobically formed TPs, including the secondary TPs, were degraded. In samples collected at different stages of a full-scale municipal WWTP, anaerobically formed SMX TPs were detected at high concentrations in the primary clarifier and digested sludge units, where anoxic conditions were prevalent. Contrarily, their concentrations were lower in oxic zones like the biological treatment and final effluent. Our results suggest that anaerobically formed TPs were eliminated in the aerobic treatment stages, consistent with our observations in batch biotransformation experiments. More generally, our findings highlight the significance of varying redox states determining the fate of SMX and its TPs in engineered environments.

Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS

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.

Unravelling psychoactive substances and their metabolites and transformation products: High-Resolution Mass Spectrometry approaches for comprehensive target and suspect screening in wastewater

Untangling the consumption rates of psychiatric drugs and their metabolites/ transformation products-(TPs) through wastewater gains attention lately. However, the potential environmental impact caused by their release remains ambiguous. As it follows, the monitoring of this class of pharmaceuticals as well as the evaluation of their potential toxicity is a matter of high concern. In the light of the above, here, wastewater samples, were collected in a 1-year and a half sampling campaign (2020-2021) and were further subjected to solid phase extraction. A Q Exactive Focus Orbitrap mass analyzer was employed for the analysis of the samples. For the data curation, except of the monitoring of targets, a comprehensive suspect screening workflow was developed and slightly optimized based on a lab made HRMS database for the investigation of legally or illegally prescribed psychiatric drugs and their relevant metabolites/TPs in influents and effluents. Carbamazepine and amisulpride were quantified at the highest mean concentrations 243 and 225 ng/L respectively, in influents. In effluents, the highest mean concentrations were calculated for carbamazepine (180 ng/L) and venlafaxine (117 ng/L). The implementation of suspect screening approach enhanced the comprehensiveness of analysis by detecting 29 compounds not included in the target list. O-Desmethylvenlafaxine was the predominant metabolite in influents presenting a mean concentration equal to 87 ng/L while the same pattern was also noticed in effluents where the mean concentration was up to 91 ng/L. From the group of suspect compounds for which no analytical standards were available, the predominant compounds with detection frequency 100 % were norephedrine and codeine in influents while in effluents, oxazepam was detected in 81 % of the analyzed samples. Finally, in silico and mathematical tools were employed for the assessment of the risk posed to environmental systems. Most of the detected compounds present high risk in all trophic levels.

Quantitative Non-targeted Screening to Profile Micropollutants in Sewage Sludge Used for Agricultural Field Amendments

A considerable number of micropollutants from human activities enter the wastewater network for removal. However, at the wastewater treatment plant (WWTP), some proportion of these compounds is retained in the sewage sludge (biosolids), and due to its high content of nutrients, sludge is widely applied as an agricultural fertilizer and becomes a means for the micropollutants to be introduced to the environment. Accordingly, a holistic semiquantitative nontarget screening was performed on sewage sludges from five different WWTPs using nanoflow liquid chromatography coupled to high-resolution Orbitrap mass spectrometry. Sixty-one inorganic elements were measured using inductively coupled plasma mass spectrometry. Across all sludges, the nontarget analysis workflow annotated >21,000 features with chemical structures, and after strict prioritization and filtering, 120 organic micropollutants with diverse chemical structures and applications such as pharmaceuticals, pesticides, flame retardants, and industrial and natural compounds were identified. None of the tested sludges were free from organic micropollutants. Pharmaceuticals contributed the largest share followed by pesticides and natural products. The predicted concentration of identified contaminants ranged between 0.2 and 10,881 ng/g dry matter. Through quantitative nontarget analysis, this study comprehensively demonstrated the occurrence of cocktails of micropollutants in sewage sludges.

Occurrence and risk assessment of pesticides and their transformation products related to olive groves in surface waters of the Guadalquivir river basin

Pesticides are considered one of the main sources of contamination of surface waters, especially in rural areas highly influenced by traditional agricultural practices. The objective of this work was to evaluate the impact caused by pesticides and their transformation products (TPs) related to olive groves in surface waters with strong agricultural pressure. 11 streams were monitored during four sampling campaigns over 2 years. A solid-phase extraction, followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) analysis was used in the quantitative target approach, with more than 70 validated compounds. Target method was combined with a suspect screening strategy involving more than 500 pesticides and TPs, using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) to identify additional pesticides and TPs out of the scope of analysis. A total of 43 different compounds were detected with the target method. The herbicide MCPA was present in all samples and at the highest concentration (1260 ng L-1), followed by the fungicide carbendazim (1110 ng L-1), and the herbicide chlorotoluron (706 ng L-1). The suspect screening strategy revealed the presence of 7 compounds out of the target analysis (1 pesticide and 6 TPs). 6 analytes were confirmed with the analytical standards. Semi-quantification results revealed that TPs exhibited higher concentrations than their corresponding parent compounds, indicating higher persistency. Some small streams showed a comparable number of pesticides and concentrations to the most polluted large river. The determined pesticide and TPs concentrations represented an estimated environmental hazard in almost all sampling sites under study. This work underscores the importance of including pesticide TPs and small streams impacted by extensive agricultural activities in water quality monitoring programs.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.

A critical review of workplace drug testing methods for old and new psychoactive substances: Gaps, advances, and perspectives

Workplace drug testing (WDT) is essential to prevent drug abuse disorders among the workforce because it can impair work performance and safety. However, WDT is limited by many challenges, such as urine adulteration, specimen selection, and new psychoactive substances (NPS). This review examined the issues related to WDT. Various scientific databases were searched for articles on WDT for drug detection published between 1986 (when WDT started) and January 2024. The review discussed the history, importance, and challenges of WDT, such as time of specimen collection/testing, specimen adulteration, interference in drug testing, and detection of NPS. It evaluated the best methods to detect NPS in forensic laboratories. Moreover, it compared different techniques that can enhance WDT, such as immunoassays, targeted mass spectrometry, and nontargeted mass spectrometry. These techniques can be used to screen for known and unknown drugs and metabolites in biological samples. This review assessed the strengths and weaknesses of such techniques, such as their validation, identification, library search, and reference standards. Furthermore, this review contrasted the benefits and drawbacks of different specimens for WDT and discussed studies that have applied these techniques for WDT. WDT remains the best approach for preventing drug abuse in the workplace, despite the challenges posed by NPS and limitations of the screening methods. Nontargeted techniques using high-resolution liquid chromatography-mass spectrometry (MS)/gas chromatography-tandem MS can improve the detection and identification of drugs during WDT and provide useful information regarding the prevalence, trends, and toxicity of both traditional and NPS drugs. Finally, this review suggested that WDT can be improved by using a combination of techniques, multiple specimens, and online library searches in case of new NPS as well as by updating the methods and databases to include new NPS and metabolites as they emerge. To the best of the author's knowledge, this is the first review to address NPS as an issue in WDT and its application and propose the best methods to detect these substances in the workplace environment.

Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans

The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.

Identification, semi-quantification and risk assessment of contaminants of emerging concern in Flemish indoor dust through high-resolution mass spectrometry

Indoor dust can contribute substantially to human exposure to known and contaminants of emerging concern (CECs). Novel compounds with high structural variability and different homologues are frequently discovered through screening of the indoor environment, implying that constant monitoring is required. The present study aimed at the identification and semi-quantification of CECs in 46 indoor dust samples collected in Belgium by liquid chromatography high-resolution mass spectrometry. Samples were analyzed applying a targeted and suspect screening approach; the latter based on a suspect list containing >4000 CECs. This allowed the detection of a total of 55 CECs, 34 and 21 of which were identified with confidence level (CL) 1/2 or CL 3, respectively. Besides numerous known contaminants such as di(2-ethylhexyl) phthalate (DEHP), di(2-ethylhexyl) adipate (DEHA) or tris(2-butoxyethyl) phosphate (TBOEP) which were reported with detection frequencies (DFs) > 90%, several novel CECs were annotated. These included phthalates with differing side chains, such as decyl nonyl and decyl undecyl phthalate detected with DFs >80% and identified through the observation of characteristic neutral losses. Additionally, two novel organophosphate flame retardants not previously described in indoor dust, i.e. didecyl butoxyethoxyethyl phosphate (DDeBEEP) and bis(butoxyethyl) butyl phosphate (BBEBP), were identified. The implementation of a dedicated workflow provided semi-quantitative concentrations for a set of suspects. Such data obtained for novel phthalates were in the same order of magnitude as the concentrations observed for legacy phthalates indicating their high relevance for human exposure. From the semi-quantitative data, estimated daily intakes and resulting hazard quotients (HQs) were calculated to estimate the exposure and potential health effects. Neither of the obtained HQ values exceeded the risk threshold, indicating no expected adverse health effects.

HRMS-based suspect screening of pharmaceuticals and their transformation products in multiple environmental compartments: An alternative to target analysis?

The comprehensive monitoring of pharmaceutically active compounds (PhACs) in the environment is challenging given the myriad of substances continuously discharged, the increasing number of new compounds being produced (and released), or the variety of the associated human metabolites and transformation products (TPs). Approaches such as high-resolution mass spectrometry (HRMS)-based suspect analysis have emerged to overcome the drawbacks of classical target analytical methods, e.g., restricted chemical coverage. In this study, we assess the readiness of HRMS-based suspect screening to replace or rather complement target methodologies by comparing the performance of both approaches in terms of i) detection of PhACs in various environmental samples (water, sediments, biofilm, fish plasma, muscle and liver) in a field study; ii) PhACs (semi)quantification and iii) prediction of their environmental risks. Our findings revealed that target strategies alone significantly underestimate the variety of PhACs potentially impacting the environment. However, relying solely on suspect strategies can misjudge the presence and risk of low-level but potentially risky PhACs. Additionally, semiquantitative approaches, despite slightly overestimating concentrations, can provide a realistic overview of PhACs concentrations. Hence, it is recommended to adopt a combined strategy that first evaluates suspected threats and subsequently includes the relevant ones in the established target methodologies.

(Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms

This study investigated the presence and human hazards associated with pesticides and other anthropogenic chemicals identified in kale grown in urban and rural environments. Pesticides and related compounds (i.e., surfactants and metabolites) in kale samples were evaluated using a nontargeted data acquisition for targeted analysis method which utilized a pesticide mixture containing >1,000 compounds for suspect screening and quantification. We modeled population-level exposures and assessed noncancer hazards to DEET, piperonyl butoxide, prometon, secbumeton, terbumeton, and spinosyn A using nationally representative estimates of kale consumption across life stages in the US. Our findings indicate even sensitive populations (e.g., pregnant women and children) are not likely to experience hazards from these select compounds were they to consume kale from this study. However, a strictly nontargeted chemical analytical approach identified a total of 1,822 features across all samples, and principal component analysis revealed that the kale chemical composition may have been impacted by agricultural growing practices and environmental factors. Confidence level 2 compounds that were ≥5 times more abundant in the urban samples than in rural samples (p < 0.05) included chemicals categorized as "flavoring and nutrients" and "surfactants" in the EPA's Chemicals and Products Database. Using the US-EPA's Cheminformatics Hazard Module, we identified that many of the nontarget compounds have predicted toxicity scores of "very high" for several end points related to human health. These aspects would have been overlooked using traditional targeted analysis methods, although more information is needed to ascertain whether the compounds identified through nontargeted analysis are of environmental or human health concern. As such, our approach enabled the identification of potentially hazardous compounds that, based on their hazard assessment score, merit follow-up investigations.

Analysis of tryptophan metabolites and related compounds in human and murine tissue: development and validation of a quantitative and semi-quantitative method using high resolution mass spectrometry

This study explores the metabolic differences between human and murine plasma in addition to differences between murine subcutaneous and visceral white adipose tissue. A quantitative and semi-quantitative targeted method was developed and validated for this purpose. The quantitative method includes tryptophan and its metabolites in addition to tyrosine, phenylalanine, taurine, B vitamins, neopterin, cystathionine and hypoxanthine. While the semi-quantitative method includes; 3-indoleacetic acid, 5-hydroxyindoleacetic acid, acetylcholine, asymmetric dimethylarginine, citrulline and methionine. Sample preparation was based on protein precipitation, while quantification was conducted using ultrahigh-performance liquid chromatography coupled to a quadrupole Orbitrap tandem mass spectrometer with electrospray ionization in the parallel reaction monitoring (PRM) mode. The low limit of quantification for all metabolites ranged from 1 to 200 ng mL-1. Matrix effects and recoveries for stable isotope labelled internal standards were evaluated, with most having a coefficient of variation (CV) of less than 15%. Results showed that a majority of the analytes passed both the intra- and interday precision and accuracy criteria. The comparative analysis of human and murine plasma metabolites reveals species-specific variations within the tryptophan metabolic pathway. Notably, murine plasma generally exhibits elevated concentrations of most compounds in this pathway, with the exceptions of kynurenine and quinolinic acid. Moreover, the investigation uncovers noteworthy metabolic disparities between murine visceral and subcutaneous white adipose tissues, with the subcutaneous tissue demonstrating significantly higher concentrations of tryptophan, phenylalanine, tyrosine, and serotonin. The findings also show that even a semi-quantitative method can provide comparable results to quantitative methods from other studies and be effective for assessing metabolites in a complex sample. Overall, this study provides a robust platform to compare human and murine metabolism, providing a valuable insight to future investigations.

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration "surrogates," which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from "global" chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using "global" surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using "expert-selected" surrogates (n = 3) instead of "global" surrogates (n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases.

Novel PFAS-specific monitoring approach for highly impacted surface waters

In regulatory environmental monitoring programs, only a very small fraction of the vast number of per- and polyfluoroalkyl substances (PFAS) are investigated by target analysis. Therefore, non-target analysis (NTA) studies are increasingly conducted to detect unknown or unnoticed PFAS. These studies are often based on a few grab samples. Thus, discontinuously emitted PFAS from industrial batch processes might be easily overlooked. To address this deficiency and obtain in-depth information on the occurrence and temporal trend of PFAS in surface water impacted by treated industrial waste water, a comprehensive target and NTA study was implemented for 29 months. Elevated PFAS concentrations with up to 10.8 μg L-1 were detected in the river water by target analysis. In addition to PFAS target analysis, the water samples were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). Data processing strategies and various filtering steps were applied to prioritize PFAS. Substances were identified by comparing data to available internal and external PFAS suspect lists, a fragment ion and neutral loss list, and spectral libraries. Several compounds were unequivocally identified based on reference standards. Fifty-five PFAS were (tentatively) identified using NTA. Of those, 43 could be assigned to 13 different homologous series. Partly fluorinated short-chain carboxylic acids (H-PFCA) and sulfonic acids (H-PFSA) were predominantly found in addition to perfluoroalkyl carboxylic acids (PFCA) and the alkyl ether carboxylic acid DONA. To the best of our knowledge, 12 PFAS were reported in surface water for the first time. Signal intensities of individual PFAS and signal ratios varied widely over time, which may indicate batch operations leading to discontinuous emission. Results and insights from this screening approach on PFAS can be used to optimize forthcoming surface water monitoring programs by including newly identified PFAS and selecting appropriate sampling intervals.

High-Resolution Mass Spectrometry Screening of Quaternary Ammonium Compounds (QACs) in Dust from Homes and Various Microenvironments in South China

Despite their ubiquitous use, information regarding the presence of quaternary ammonium compounds (QACs) in various microenvironments remains scarce and only a small subset of QACs has been monitored using targeted chemical analysis. In this study, a total of 111 dust samples were collected from homes and various public settings in South China during the COVID-19 pandemic and were analyzed for traditional and emerging QACs using high-resolution mass spectrometry. The total traditional QAC concentrations in residential dust (∑traditional QAC, sum of 18 traditional QACs) ranged from 13.8 to 150 μg/g with a median concentration of 42.2 μg/g. Twenty-eight emerging QACs were identified in these samples, and the composition of ∑emerging QAC (sum of emerging QACs) to ∑QAC (sum of traditional and emerging QACs) ranged from 19 to 42% across various microenvironments, indicating the widespread existence of emerging QACs in indoor environments. Additionally, dust samples from cinemas exhibited higher ∑QAC concentrations compared to homes (medians 65.9 μg/g vs 58.3 μg/g, respectively), indicating heavier emission sources of QACs in these places. Interestingly, significantly higher ∑QAC concentrations were observed in dust from the rooms with carpets than those without (medians 65.6 μg/g vs 32.6 μg/g, p < 0.05, respectively). Overall, this study sheds light on the ubiquitous occurrence of QACs in indoor environments in South China.

Assessment for the data processing performance of non-target screening analysis based on high-resolution mass spectrometry

Non-target screening (NTS) based on high-resolution mass spectrometry (HRMS) is considered one of the most comprehensive approaches for the characterization of contaminants of emerging concern (CECs) in a complex sample. This study evaluated the performance of NTS in aquatic environments (including peak picking, database matching, product identification, semi-quantification, etc.) based on a self-developed data processing method using 38 glucocorticoids as testing compounds. Data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes were used for obtaining the MS2 information for in-house or online database matching. Results indicate that DDA and DIA mode have their own advantages and can complement each other. The quantification method based on LC-HRMS has shown the potential to provide a fast and acceptable result for testing compounds. Finally, a matrix spike analysis was carried out on 66 CECs across different usage categories in wastewater, surface water, and seawater matrix samples, together with a case study performed for characterizing the whole contaminants in a Pearl River sample, to better illustrate the application potential of NTS workflow and the credibility of NTS outcomes. This study provides a foundation for novel applications of HRMS data by NTS workflow to identify and quantify CECs in complex systems.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

The unusual suspects: Screening for persistent, mobile, and toxic plastic additives in plastic leachates

Plastic additives are a diverse group of chemical compounds added to plastic products to give them their unique physical-chemical properties. Persistent, mobile, and toxic (PMT) plastic additives are a highly polar, environmentally stable sub-group of plastic additives with a variety of uses in plastic products. Due to their mobility into water, they can pose a significant long-term risk to the aquatic environment. Despite the potential threat, PMT plastic additives remain largely unregulated and under-studied. Notably, there is a need for dedicated analytical methodology and leaching studies to determine their potential emission from plastic products. Here we present an optimized leaching protocol and novel instrumental analysis method for the screening of 124 PMT plastic additives registered for use in Canada using high performance liquid chromatography with quantitative time-of-flight mass spectrometry (HPLC-QToF-MS). The analytical method covered a log Kow/Dow range between 0.21 and 6.02, which covered 72% of the PMT plastic additives used in Canada. A total of 52 PMT plastic additive suspects were leached in the optimization experiments, 44 of which were unique based on accurate mass and retention time. The conditions that resulted in the greatest numbers of PMT plastic additives leached were lake water, UV light exposure, and a timeframe of approximately 30 days. The analytical and leaching methods presented here offer new tools to study PMT plastic additives and assess their leaching in an environmentally relevant matrix, which can inform monitoring, threat assessment, and regulatory efforts moving forward.

Non-targeted and suspect screening analysis using ion exchange chromatography-Orbitrap tandem mass spectrometry reveals polar and very mobile xenobiotics in Danish drinking water

Non-targeted and suspect screening analysis is gaining approval across the scientific and regulatory community to monitor the chemical status in the environment and thus environmental quality. These holistic screening analyses provides the means to perform suspect screening and go beyond to discover previously undescribed chemical pollutants in environmental samples. In a case study, we developed and optimized a high-resolution tandem mass spectrometry platform hyphenated with anion exchange chromatography to screen drinking water samples in Denmark. The optimized non-targeted screening method was able to detect anionic and polar compounds and was successfully applied to drinking water from two drinking water facilities. Following a data analysis pipeline optimization, anionic pesticide residues and other environmental contaminants were detected at confidence identification level 1 such as dimethachlor ESA, mecoprop, and dichlorprop in drinking water. In addition to these three substances, it was possible to detect another 1662 compounds, of which 97 were annotated at confidence identification level 2. More research is urgently needed to health risk prioritize the detected substances and to determine their concentrations.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

Mobile phase and column chemistry selection for high sensitivity non-targeted LC/ESI/HRMS screening of water

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.

Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

Electrospray Ionization Efficiency Predictions and Analytical Standard Free Quantification for SFC/ESI/HRMS

Supercritical fluid chromatography (SFC) is a promising, sustainable, and complementary alternative to liquid chromatography (LC) and has often been coupled with high resolution mass spectrometry (HRMS) for nontarget screening (NTS). Recent developments in predicting the ionization efficiency for LC/ESI/HRMS have enabled quantification of chemicals detected in NTS even if the analytical standards of the detected and tentatively identified chemicals are unavailable. This poses the question of whether analytical standard free quantification can also be applied in SFC/ES/HRMS. We evaluate both the possibility to transfer an ionization efficiency predictions model, previously trained on LC/ESI/HRMS data, to SFC/ESI/HRMS as well as training a new predictive model on SFC/ESI/HRMS data for 127 chemicals. The response factors of these chemicals ranged over 4 orders of magnitude in spite of a postcolumn makeup flow, expectedly enhancing the ionization of the analytes. The ionization efficiency values were predicted based on a random forest regression model from PaDEL descriptors and predicted values showed statistically significant correlation with the measured response factors (p < 0.05) with Spearman's rho of 0.584 and 0.669 for SFC and LC data, respectively. Moreover, the most significant descriptors showed similarities independent of the chromatography used for collecting the training data. We also investigated the possibility to quantify the detected chemicals based on predicted ionization efficiency values. The model trained on SFC data showed very high prediction accuracy with median prediction error of 2.20×, while the model pretrained on LC/ESI/HRMS data yielded median prediction error of 5.11×. This is expected, as the training and test data for SFC/ESI/HRMS have been collected on the same instrument with the same chromatography. Still, the correlation observed between response factors measured with SFC/ESI/HRMS and predicted with a model trained on LC data hints that more abundant LC/ESI/HRMS data prove useful in understanding and predicting the ionization behavior in SFC/ESI/HRMS.

Antimicrobial Transformation Products in the Aquatic Environment: Global Occurrence, Ecotoxicological Risks, and Potential of Antibiotic Resistance

The global spread of antimicrobial resistance (AMR) is concerning for the health of humans, animals, and the environment in a One Health perspective. Assessments of AMR and associated environmental hazards mostly focus on antimicrobial parent compounds, while largely overlooking their transformation products (TPs). This review lists antimicrobial TPs identified in surface water environments and examines their potential for AMR promotion, ecological risk, as well as human health and environmental hazards using in silico models. Our review also summarizes the key transformation compartments of TPs, related pathways for TPs reaching surface waters and methodologies for studying the fate of TPs. The 56 antimicrobial TPs covered by the review were prioritized via scoring and ranking of various risk and hazard parameters. Most data on occurrences to date have been reported in Europe, while little is known about antibiotic TPs in Africa, Central and South America, Asia, and Oceania. Occurrence data on antiviral TPs and other antibacterial TPs are even scarcer. We propose evaluation of structural similarity between parent compounds and TPs for TP risk assessment. We predicted a risk of AMR for 13 TPs, especially TPs of tetracyclines and macrolides. We estimated the ecotoxicological effect concentrations of TPs from the experimental effect data of the parent chemical for bacteria, algae and water fleas, scaled by potency differences predicted by quantitative structure-activity relationships (QSARs) for baseline toxicity and a scaling factor for structural similarity. Inclusion of TPs in mixtures with their parent increased the ecological risk quotient over the threshold of one for 7 of the 24 antimicrobials included in this analysis, while only one parent had a risk quotient above one. Thirteen TPs, from which 6 were macrolide TPs, posed a risk to at least one of the three tested species. There were 12/21 TPs identified that are likely to exhibit a similar or higher level of mutagenicity/carcinogenicity, respectively, than their parent compound, with tetracycline TPs often showing increased mutagenicity. Most TPs with increased carcinogenicity belonged to sulfonamides. Most of the TPs were predicted to be mobile but not bioaccumulative, and 14 were predicted to be persistent. The six highest-priority TPs originated from the tetracycline antibiotic family and antivirals. This review, and in particular our ranking of antimicrobial TPs of concern, can support authorities in planning related intervention strategies and source mitigation of antimicrobials toward a sustainable future.

Identification and characterization of quaternary ammonium compounds in Flemish indoor dust by ion-mobility high-resolution mass spectrometry

Quaternary ammonium compounds (QACs) are a class of surfactants commonly used in disinfecting and cleaning products. Their use has substantially increased during the COVID-19 pandemic leading to increasing human exposure. QACs have been associated with hypersensitivity reactions and an increased risk of asthma. This study introduces the first identification, characterization and semi-quantification of QACs in European indoor dust using ion-mobility high-resolution mass spectrometry (IM-HRMS), including the acquisition of collision cross section values (^DTCCS_N2) for targeted and suspect QACs. A total of 46 indoor dust samples collected in Belgium were analyzed using target and suspect screening. Targeted QACs (n = 21) were detected with detection frequencies ranging between 4.2 and 100 %, while 15 QACs showed detection frequencies > 90 %. Semi-quantified concentrations of individual QACs showed a maximum of 32.23 µg/g with a median ∑QAC concentration of 13.05 µg/g and allowed the calculation of Estimated Daily Intakes for adults and toddlers. Most abundant QACs matched the patterns reported in indoor dust collected in the United States. Suspect screening allowed the identification of 17 additional QACs. A dialkyl dimethyl ammonium compound with mixed chain lengths (C16:C18) was characterized as a major QAC homologue with a maximum semi-quantified concentration of 24.90 µg/g. The high detection frequencies and structural variabilities observed call for more European studies on potential human exposure to these compounds. For all targeted QACs, drift tube IM-HRMS derived collision cross section values (^DTCCS_N2) are reported. Reference ^DTCCS_N2 values allowed the characterization of CCS-m/z trendlines for each of the targeted QAC classes. Experimental CCS-m/z ratios of suspect QACs were compared with the CCS-m/z trendlines. The alignment between the two datasets served as an additional confirmation of the assigned suspect QACs. The use of the 4bit multiplexing acquisition mode with consecutive high-resolution demultiplexing confirmed the presence of isomers for two of the suspect QACs.

Optimisation and implementation of QuEChERS-based sample preparation for identification and semi-quantification of 694 targeted contaminants in honey, jam, jelly, and syrup by UHPLC-Q/ToF high-resolution mass spectrometry

A screening and semi-quantitative method was developed for the analysis of 694 various contaminants in honey, jam, jelly and syrup samples by ultrahigh-performance liquid chromatography and quadrupole time-of-flight mass spectrometry. Sample preparation, which was optimised using split factorial design, was based on acetate-buffered version of QuEChERS, followed by a clean-up step and a concentration step to enhance sensitivity of analytes. The method was validated according to SANTE/11312/2021 guidelines. The screening detection and limits of identification were established as being less than or equal to 0.05 mg.kg^-1 for 89% and 74% of the contaminants, respectively. The validated screening method was applied to 50 concentrated sugary products. Overall, 46% of the samples were positive to pesticide residues. Most of the positive samples (78%) contained mixtures of pesticide residues. Three time-and-cost saving convenient strategies suitable for high-throughput analysis were proposed for the targeted semi-quantification of the previously contaminants identified in samples.

Green alga Chlamydomonas reinhardtii can effectively remove diclofenac from the water environment - A new perspective on biotransformation

The use of unicellular algae to remove xenobiotics (including drugs) from wastewaters is one of the rapidly developing areas of environmental protection. Numerous data indicate that for efficient phycoremediation three processes are important, i.e. biosorption, bioaccumulation, and biotransformation. Although biosorption and bioaccumulation do not raise any serious doubts, biotransformation is more problematic since its products can be potentially more toxic than the parent compounds posing a threat to organisms living in a given environment, including organisms that made this transformation. Thus, two questions need to be answered before the proper algae strain is chosen for phycoremediation, namely what metabolites are produced during biotransformation, and how resistant is the analyzed strain to a mixture of parent compound and metabolites that appear over the course of culture? In this work, we evaluated the remediation potential of the model green alga Chlamydomonas reinhardtii in relation to non-steroidal anti-inflammatory drugs (NSAIDs), as exemplified by diclofenac. To achieve this, we analysed the susceptibility of C. reinhardtii to diclofenac as well as its capability to biosorption, bioaccumulation, and biotransformation of the drug. We have found that even at a relatively high concentration of diclofenac the algae maintained their vitality and were able to remove (37.7%) DCF from the environment. A wide range of phase I and II metabolites of diclofenac (38 transformation products) was discovered, with many of them characteristic rather for animal and bacterial biochemical pathways than for plant metabolism. Due to such a large number of detected products, 18 of which were not previously reported, the proposed scheme of diclofenac transformation by C. reinhardtii not only significantly contributes to broadening the knowledge in this field, but also allows to suggest possible pathways of degradation of xenobiotics with a similar structure. It is worth pointing out that a decrease in the level of diclofenac in the media observed in this study cannot be fully explained by biotransformation (8.4%). The mass balance analysis indicates that other processes (total 22%), such as biosorption, a non-extractable residue formation, or complete decomposition in metabolic cycles can be involved in the diclofenac disappearance, and those findings open the prospects of further research.

Enrichment and analysis methods for trace dissolved organic carbon in reverse osmosis effluent: A review

Reverse osmosis (RO) is an essential unit for producing high-quality ultrapure water. The increasingly severe water shortage and water quality deterioration result in reclaimed water as an alternative source for ultrapure water production. However, when using reclaimed water as water sources, the dissolved organic carbon (DOC) in RO permeate exhibits higher concentration and more sophisticated components than when using clean water sources, thus affecting the effluent quality of ultrapure water and the effectiveness of subsequent treatment processes. To optimize the treatment processes, it is crucial to analyze the components of DOC. This review summarizes the enrichment and analysis methods of trace organic matter, and provides recommendations for the analysis and characterization of DOC in RO permeate. The study summarizes the operating conditions and enrichment properties of different enrichment methods, including solid-phase extraction, liquid-liquid extraction, purge-and-trap, lyophilization and rotary evaporation for low-concentration organic compounds, compares the applicability and limitations of different enrichment methods, and proposes the principles for the selection of enrichment methods. In this review, we discuss the application of mass spectrometry (including Fourier transform ion cyclotron resonance mass spectrometry) in the analysis of DOC components, and focus on data processing as the key procedure in analysis of DOC in RO permeate. Despite the advantages of mass spectrometry, an applicable workflow and open-source database are required to improve the reliability of the analysis. The treatability properties of DOC are suggested to be determined by analyzing the component characteristics or in combination with common removal techniques. This study provides theoretical support for a comprehensive analysis of DOC in RO permeates to improve the removal effect.

Tryptophan and Kynurenine Pathway Metabolites and Psychoneurological Symptoms Among Breast Cancer Survivors

BACKGROUND

Among breast cancer survivors, pain, fatigue, depression, anxiety, and sleep disturbance are common psychoneurological symptoms that cluster together. Inflammation-induced activation of the tryptophan-kynurenine metabolomic pathway may play an important role in these symptoms.

AIMS

This study investigated the relationship between the metabolites involved in the tryptophan-kynurenine pathway and psychoneurological symptoms among breast cancer survivors.

DESIGN

Cross-sectional study.

SETTING

Participants were recruited at the oncology clinic at the University of Illinois Hospital & Health Sciences System.

PARTICIPANTS/SUBJECTS

79 breast cancer survivors after major cancer treatment.

METHODS

We assessed psychoneurological symptoms with the PROMIS-29 and collected metabolites from fasting blood among breast cancer survivors after major cancer treatment, then analyzed four major metabolites involved in the tryptophankynurenine pathway (tryptophan, kynurenine, kynurenic acid, and quinolinic acid). Latent profile analysis identified subgroups based on the five psychoneurological symptoms. Mann-Whitney U tests and multivariable logistic regression compared targeted metabolites between subgroups.

RESULTS

We identified two distinct symptom subgroups (low, 81%; high, 19%). Compared with participants in the low symptom subgroup, patients in the high symptom subgroup had higher BMI (p = .024) and were currently using antidepressants (p = .008). Using multivariable analysis, lower tryptophan levels (p = .019) and higher kynurenine/tryptophan ratio (p = .028) were associated with increased risk of being in the high symptom subgroup after adjusting for BMI and antidepressant status.

CONCLUSION

The tryptophan-kynurenine pathway and impaired tryptophan availability may contribute to the development of psychoneurological symptoms.

Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses

Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 μg/L), valsartan (14.4 μg/L), and ibuprofen (12.7 μg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia

BACKGROUND

Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology.

OBJECTIVES

We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures.

METHODS

A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia.

RESULTS

A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes.

CONCLUSIONS

These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

Thorough investigation of non-volatile substances extractible from inner coatings of metallic cans and their occurrence in the canned vegetables

Since the decline of the use of bisphenol A, the chemistry of the varnishes and coatings which are applied to the inner surfaces of metallic food contact materials is poorly documented. We hypothesised that can coatings are now diverse and bring forth various non-intentionally added substances (NIAS) to be described. Investigating complex components such as NIAS requires demanding non-targeted approaches. We investigated the coatings of 12 vegetable cans from the French market. More than 125 substances were pinpointed, among them 84 oligoester combinations from 8 diols and 4 diacids. Thus, oligoesters were the dominant family. Additives such as epoxidised soybean oil, bisphenol A diglycidyl ether and benzoguanamine derivatives and phenol-formaldehyde oligomers were also identified. A software for exploring databases of theoretical combinations of polyester and phenol-formaldehyde resin components (NIAS-db 1.0) was made available. The stepwise organic synthesis of native and deuterated combinations of neopentyl glycol and isophthalic acid (4 and 8 units, linear and cyclic) enabled a higher confidence level and monitoring in vegetable extracts. Migration of oligoesters averaged 330 µg/kg in the drained vegetables (43-1600 µg/kg). This study sheds light on the need to fulfil a proper risk assessment on this NIAS family (exposure and hazard characterisation).

Thorough Investigation of the Phenolic Profile of Reputable Greek Honey Varieties: Varietal Discrimination and Floral Markers Identification Using Liquid Chromatography–High-Resolution Mass Spectrometry

Honey is a highly consumed commodity due to its potential health benefits upon certain consumption, resulting in a high market price. This fact indicates the need to protect honey from fraudulent acts by delivering comprehensive analytical methodologies. In this study, targeted, suspect and non-targeted metabolomic workflows were applied to identify botanical origin markers of Greek honey. Blossom honey samples (n = 62) and the unifloral fir (n = 10), oak (n = 24), pine (n = 39) and thyme (n = 34) honeys were analyzed using an ultra-high-performance liquid chromatography hybrid quadrupole time-of-flight mass spectrometry (UHPLC-q-TOF-MS) system. Several potential authenticity markers were revealed from the application of different metabolomic workflows. In detail, based on quantitative targeted analysis, three blossom honey markers were found, namely, galangin, pinocembrin and chrysin, while gallic acid concentration was found to be significantly higher in oak honey. Using suspect screening workflow, 12 additional bioactive compounds were identified and semi-quantified, achieving comprehensive metabolomic honey characterization. Lastly, by combining non-targeted screening with advanced chemometrics, it was possible to discriminate thyme from blossom honey and develop binary discriminatory models with high predictive power. In conclusion, a holistic approach to assessing the botanical origin of Greek honey is presented, highlighting the complementarity of the three applied metabolomic approaches.

Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.

Uncertainty estimation strategies for quantitative non-targeted analysis

Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure–property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/.

Characterization of new psychoactive substances by integrating benchtop NMR to multi-technique databases

New psychoactive substances (NPS) have become a serious threat for public health due to their ability to be sold in the street or on internet. NPS are either derived from commercial drugs which are misused (recreational rather than medical use) or whose structure is slightly modified. To regulate NPS, it is essential to accurately characterize them, either to recognize molecules that were previously identified or to quickly elucidate the structure of unknown ones. Most approaches rely on the determination of the exact mass obtained by high‐resolution mass spectrometry requiring expensive equipment. This motivated us to develop a workflow in which the elucidation is assisted with databases and does not need the exact mass. This workflow combines 1D and 2D NMR measurements performed on a benchtop spectrometer with IR spectroscopy, for creating a multi‐technique database to characterize pure and mixed NPS. The experimental database was created with 57 entries mostly coming from seizures, mainly cathinones, cannabinoids, amphetamines, arylcyclohexylamines, and fentanyl. A blind validation of the workflow was carried out on a set of six unknown seizures. In the first three cases, AF, AB‐FUBINACA, and a mixture of 2C‐I and 2C‐E could be straightforwardly identified with the help of their reference spectra in the database. The two next samples were elucidated for the first time with the help of the database to reveal NEK and MPHP substances. Finally, a precise quantification of each characterized NPS was obtained in order to track NPS trafficking networks.

Estimation of the concentrations of hydroxylated polychlorinated biphenyls in human serum using ionization efficiency prediction for electrospray

Hydroxylated PCBs are an important class of metabolites of the widely distributed environmental contaminants polychlorinated biphenyls (PCBs). However, the absence of authentic standards is often a limitation when subject to detection, identification, and quantification. Recently, new strategies to quantify compounds detected with non-targeted LC/ESI/HRMS based on predicted ionization efficiency values have emerged. Here, we evaluate the impact of chemical space coverage and sample matrix on the accuracy of ionization efficiency-based quantification. We show that extending the chemical space of interest is crucial in improving the performance of quantification. Therefore, we extend the ionization efficiency-based quantification approach to hydroxylated PCBs in serum samples with a retraining approach that involves 14 OH-PCBs and validate it with an additional four OH-PCBs. The predicted and measured ionization efficiency values of the OH-PCBs agreed within the mean error of 2.1 × and enabled quantification with the mean error of 4.4 × or better. We observed that the error mostly arose from the ionization efficiency predictions and the impact of matrix effects was of less importance, varying from 37 to 165%. The results show that there is potential for predictive machine learning models for quantification even in very complex matrices such as serum. Further, retraining the already developed models provides a timely and cost-effective solution for extending the chemical space of the application area.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

Spotlight on mass spectrometric non-target screening analysis: Advanced data processing methods recently communicated for extracting, prioritizing and quantifying features

Non-target screening of trace organic compounds complements routine monitoring of water bodies. So-called features need to be extracted from the raw data that preferably represent a chemical compound. Relevant features need to be prioritized and further be interpreted, for instance by identifying them. Finally, quantitative data is required to assess the risks of a detected compound. This review presents recent and noteworthy contributions to the processing of non-target screening (NTS) data, prioritization of features as well as (semi-) quantitative methods that do not require analytical standards. The focus lies on environmental water samples measured by liquid chromatography, electrospray ionization and high-resolution mass spectrometry. Examples for fully-integrated data processing workflows are given with options for parameter optimization and choosing between different feature extraction algorithms to increase feature coverage. The regions of interest-multivariate curve resolution method is reviewed which combines a data compression alternative with chemometric feature extraction. Furthermore, prioritization strategies based on a confined chemical space for annotation, guidance by targeted analysis and signal intensity are presented. Exploiting the retention time (RT) as diagnostic evidence for NTS investigations is highlighted by discussing RT indexing and prediction using quantitative structure-retention relationship models. Finally, a seminal technology for quantitative NTS is discussed without the need for analytical standards based on predicting ionization efficiencies.