An assessment of quality assurance/quality control efforts in high resolution mass spectrometry non-target workflows for analysis of environmental samples

Target and non-target screening of biomarkers in wastewater: towards a unique analytical methodology for sample preparation

This study aims to optimize a single preparation methodology based on solid-phase extraction (SPE) that could fit both target and non-target screening of organic biomarkers in raw wastewater, allowing the cross-comparison of results obtained from a same dataset. The efficiency of SPE sorbents used alone (HLB) or in combination in a multilayer cartridge was evaluated based on (i) the extraction recovery and matrix effect in environmental samples (surface water and wastewater) for a list of biomarkers (pharmaceuticals, licit and illicit drugs, artificial sweeteners, isoprostanes, polyphenols) and (ii) a number of detected features and their intensity in HRMS. The selected method uses a combination of three SPE sorbents mixed together (HLB, X-AW and X-CW) and seems to take full advantage of each, providing satisfactory validation parameters (recovery, instrumental limit of detection, linearity range and limit of quantification) over a large range of physico-chemical properties while ensuring promising results for non-target screening applications. Of the 65 targeted compounds, nearly all of them (47) were detected in wastewater influent samples with concentration above the limit of quantification, while at the same time over 10 000 features were recorded according to the high resolution mass spectrometry (HRMS) fingerprint, holding out the promise that a common protocol for these two analyses, with their very contrasting constraints and objectives, is possible.

Assessment of emerging and persistent contaminants in an anthropogenic-impacted watershed: Application using targeted, non-targeted, and in vitro bioassay techniques

Emerging and persistent contaminants (EPC) pose a significant challenge to water quality monitoring efforts. Effect-based monitoring (EBM) techniques provide an efficient and systematic approach in water quality monitoring, but they tend to be resource intensive. In this study, we investigated the EPC distribution for various land uses using target analysis (TA) and non-target screening (NTS) and in vitro bioassays, both individually and integrated, in the upper Ping River Catchment, northern Thailand. Our findings of NTS showed that urban areas were the most contaminated of all land use types, although agriculture sites had high unexpected pollution levels. We evaluated the reliability of NTS data by comparing it to TA and observed varying inconsistencies likely due to matrix interferences and isobaric compound interferences. Integrating NTS with in vitro bioassays for a thorough analysis posed challenges, primary due to a scarcity of concentration data for key compounds, and potentially additive or non-additive effects of mixture samples that could not be accounted for. While EBM approaches place emphasis on toxic sites, this study demonstrated the importance of considering non-bioactive sites that contain toxic compounds with antagonistic effects that may go undetected by traditional monitoring approaches. The present work emphasizes the importance of improving NTS workflows and ensuring high-quality EBM analyses in future water quality monitoring programs.

Development of a robust non-targeted analysis approach for fast identification of endocrine disruptors and their metabolites in human urine for exposure assessment

Endocrine disrupting chemicals are of concern because of possible human health effects, thus they are frequently included in biomonitoring studies. Current analytical methods are focused on known chemicals and are incapable of identifying or quantifying other unknown chemicals and their metabolites. Non-targeted analysis (NTA) methods are advantageous since they allow for broad chemical screening, which provides a more comprehensive characterization of human chemical exposure, and can allow elucidation of metabolic pathways for unknown chemicals. There are still many challenges associated with NTA, which can impact the results obtained. The chemical space, i.e., the group of known and possible compounds within the scope of the method, must clearly be defined based on the sample preparation, as this is critical in identifying chemicals with confidence. Data acquisition modes and mobile phase additives used with liquid chromatography coupled to high-resolution mass-spectrometry can affect the chemicals ionized and structural identification based on the spectral quality. In this study, a sample preparation method was developed using a novel clean-up approach with CarbonS cartridges, for endocrine-disrupting chemicals in urine, including new bisphenol A analogues and benzophenone-based UV filters, like methyl bis (4-hydroxyphenyl acetate). The study showed that data dependent acquisition (DDA) had a lower identification rate (40%) at low spiking levels, i.e., 1 ng/mL, compared to data independent acquisition (DIA) (57%), when Compound Discoverer was used. In DDA, more compounds were identified using Compound Discoverer, with an identification rate of 95% when ammonium acetate was compared to acetic acid (82%) as a mobile phase additive. TraceFinder software had an identification rate of 53% at 1 ng/mL spiking level using the DDA data, compared to 40% using the DIA data. Using the developed method, 2,4 bisphenol F was identified for the first time in urine samples. The results show how NTA can provide human exposure information for risk assessment and regulatory action but standardized reporting of procedures is needed to ensure study results are reproducible and accurate. His Majesty the King in Right of Canada, as represented by the Minister of Health, 2024.

Exploring the chemical subspace of RPLC: A data driven approach

BACKGROUND

The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC.

RESULTS

We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace.

SIGNIFICANCE AND NOVELTY

The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

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.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Combining Nontargeted Analysis with Computer-Based Hazard Comparison Approaches to Support Prioritization of Unregulated Organic Contaminants in Biosolids

Biosolids are a byproduct of wastewater treatment that can be beneficially applied to agricultural land as a fertilizer. While U.S. regulations limit metals and pathogens in biosolids intended for land applications, no organic contaminants are currently regulated. Novel techniques can aid in detection, evaluation, and prioritization of biosolid-associated organic contaminants (BOCs). For example, nontargeted analysis (NTA) can detect a broad range of chemicals, producing data sets representing thousands of measured analytes that can be combined with computational toxicological tools to support human and ecological hazard assessment and prioritization. We combined NTA with a computer-based tool from the U.S. EPA, the Cheminformatics Hazard Comparison Module (HCM), to identify and prioritize BOCs present in U.S. and Canadian biosolids (n = 16). Four-hundred fifty-one features were detected in at least 80% of samples, with identities of 92 compounds confirmed or assigned probable structures. These compounds were primarily categorized as endogenous compounds, pharmaceuticals, industrial chemicals, and fragrances. Examples of top prioritized compounds were p-cresol and chlorophene, based on human health end points, and fludioxonil and triclocarban, based on ecological health end points. Combining NTA results with hazard comparison data allowed us to prioritize compounds to be included in future studies of the environmental fate and transport of BOCs.

Metabolic picture of microbial interaction: chemical crosstalk during co-cultivation between three dominant genera of bacteria and fungi in medicinal plants rhizosphere

INTRODUCTION

Microbial communities affect several aspects of the earth's ecosystem through their metabolic interaction. The dynamics of this interaction emerge from complex multilevel networks of crosstalk. Elucidation of this interaction could help us to maintain the balance for a sustainable future.

OBJECTIVES

To investigate the chemical language among highly abundant microbial genera in the rhizospheres of medicinal plants based on the metabolomic analysis at the interaction level.

METHODS

Coculturing experiments involving three microbial species: Aspergillus (A), Trichoderma (T), and Bacillus (B), representing fungi (A, T) and bacteria (B), respectively. These experiments encompassed various interaction levels, including dual cultures (AB, AT, TB) and triple cultures (ATB). Metabolic profiling by LC-QTOFMS revealed the effect of interaction level on the productivity and diversity of microbial specialized metabolites.

RESULTS

The ATB interaction had the richest profile, while the bacterial profile in the monoculture condition had the lowest. Two native compounds of the Aspergillus genus, aspergillic acid and the dipeptide asperopiperazine B, exhibited decreased levels in the presence of the AT interaction and were undetectable in the presence of bacteria during the interaction. Trichodermarin N and Trichodermatide D isolated from Trichoderma species exclusively detected during coexistence with bacteria (TB and ATB). These findings indicate that the presence of Bacillus activates cryptic biosynthetic gene clusters in Trichoderma. The antibacterial activity of mixed culture extracts was stronger than that of the monoculture extracts. The TB extract exhibited strong antifungal activity compared to the monoculture extract and other mixed culture treatments.

CONCLUSION

The elucidation of medicinal plant microbiome interaction chemistry and its effect on the environment will also be of great interest in the context of medicinal plant health Additionally, it sheds light on the content of bioactive constituents, and facilitating the discovery of novel antimicrobials.

Low temperature acclimation of electroactive microorganisms may be an effective strategy to enhance the toxicity sensing performance of microbial fuel cell sensors

Microbial fuel cell (MFC) sensing is a promising method for real-time detection of water biotoxicity, however, the low sensing sensitivity limits its application. This study adopted low temperature acclimation as a strategy to enhance the toxicity sensing performance of MFC biosensor. Two types of MFC biosensors were started up at low (10 °C) or warm (25 °C) temperature, denoted as MFC-Ls and MFC-Ws respectively, using Pb2+ as the toxic substance. MFC-Ls exhibited superior sensing sensitivities towards Pb2+ compared with MFC-Ws at both low (10 °C) and warm (25 °C) operation temperatures. For example, the inhibition rate of voltage of MFC-Ls was 22.81 % with 1 mg/L Pb2+ shock at 10 °C, while that of MFC-Ws was only 5.9 %. The morphological observation showed the anode biofilm of MFC-Ls had appropriate amount of extracellular polymer substances, thinner thickness (28.95 μm for MFC-Ls and 41.58 μm for MFC-Ws) and higher proportion of living cells (90.65 % for MFC-Ls and 86.01 % for MFC-Ws) compared to that of MFC-Ws. Microbial analysis indicated the enrichment of psychrophilic electroactive microorganisms and cold-active enzymes as well as their sensitivity to Pb2+ shock was the foundation for the effective operation and good performance of MFC-Ls biosensors. In conclusion, low temperature acclimation of electroactive microorganisms enhanced not only the sensitivity but also the temperature adaptability of MFC biosensors.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Neighbour-induced changes in root exudation patterns of buckwheat results in altered root architecture of redroot pigweed

Roots are crucial in plant adaptation through the exudation of various compounds which are influenced and modified by environmental factors. Buckwheat root exudate and root system response to neighbouring plants (buckwheat or redroot pigweed) and how these exudates affect redroot pigweed was investigated. Characterising root exudates in plant-plant interactions presents challenges, therefore a split-root system which enabled the application of differential treatments to parts of a single root system and non-destructive sampling was developed. Non-targeted metabolome profiling revealed that neighbour presence and identity induces systemic changes. Buckwheat and redroot pigweed neighbour presence upregulated 64 and 46 metabolites, respectively, with an overlap of only 7 metabolites. Root morphology analysis showed that, while the presence of redroot pigweed decreased the number of root tips in buckwheat, buckwheat decreased total root length and volume, surface area, number of root tips, and forks of redroot pigweed. Treatment with exudates (from the roots of buckwheat and redroot pigweed closely interacting) on redroot pigweed decreased the total root length and number of forks of redroot pigweed seedlings when compared to controls. These findings provide understanding of how plants modify their root exudate composition in the presence of neighbours and how this impacts each other's root systems.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

From sound check to encore: A journey through high-resolution mass spectrometry-based food analyses and metabolomics

This manuscript presents a comprehensive review of high-resolution mass spectrometry in the field of food analysis and metabolomics. We have followed the historical evolution of metabolomics, its associated techniques and technologies, and its increasing role in food science and research. The review provides a critical comparison and synthesis of tentative identification guidelines proposed for over 15 years, offering a condensed resource for researchers in the field. We have also examined a wide range of recent metabolomics studies, showcasing various methodologies and highlighting key findings as a testimony of the versatility of the field and the possibilities it offers. In doing so, we have also carefully provided a compilation of the software tools that may be employed in this type of studies. The manuscript also explores the prospects of high-resolution mass spectrometry and metabolomics in food science. By covering the history, guidelines, applications, and tools of metabolomics, this review attempts to become a comprehensive guide for researchers in a rapidly evolving field.

Analytical techniques for screening of cannabis and derivatives from human hair specimens

Cannabis and associated substances are some of the most frequently abused drugs across the globe, mainly due to their anxiolytic and euphorigenic properties. Nowadays, the analysis of hair samples has been given high importance in forensic and analytical sciences and in clinical studies because they are associated with a low risk of infection, do not require complicated storage conditions, and offer a broad window of non-invasive detection. Analysis of hair samples is very easy compared to the analysis of blood, urine, and saliva samples. This review places particular emphasis on methodologies of analyzing hair samples containing cannabis, with a special focus on the preparation of samples for analysis, which involves screening and extraction techniques, followed by confirmatory assays. Through this manuscript, we have presented an overview of the available literature on the screening of cannabis using mass spectroscopy techniques. We have presented a detailed overview of the advantages and disadvantages of this technique, to establish it as a suitable method for the analysis of cannabis from hair samples.

Reproducibility in nontarget screening (NTS) of environmental emerging contaminants: Assessing different HLB SPE cartridges and instruments

Non-targeted screening (NTS) methods are integral in environmental research for detecting emerging contaminants. However, their efficacy can be influenced by variations in hydrophilic-lipophilic balance (HLB) solid phase extraction (SPE) cartridges and high-resolution mass spectrometry (HRMS) instruments across different laboratories. In this study, we scrutinized the influence of five HLB SPE cartridges (Nano, Weiqi, CNW, Waters, and J&K) and four LC-HRMS platforms (Agilent, Waters, Thermo, and AB SCIEX) on the identification of emerging environmental contaminants. Our results demonstrate that 87.6 % of the target compounds and over 59.6 % of the non-target features were consistently detected across all tested HLB cartridges, with an overall 71.2 % universally identified across the four LC-HRMS systems. Discrepancies in detection rates were primarily attributable to variations in retention time stability, mass stability of precursors and fragments, system cleanliness affecting fold change and p-values, and fragment response. These findings confirm the necessity of refining parameter criteria for NTS. Moreover, our study confirms the efficacy of the PyHRMS tool in analyzing and processing data from multiple instrumental platforms, reinforcing its utility for multi-platform NTS. Overall, our findings underscore the reliability and robustness of NTS methods in identifying potential water contaminants, while also highlighting factors that may influence these outcomes.

Comparison of high-resolution mass spectrometry acquisition methods for the simultaneous quantification and identification of per- and polyfluoroalkyl substances (PFAS)

Simultaneous identification and quantification of per- and polyfluoroalkyl substances (PFAS) were evaluated for three quadrupole time-of-flight mass spectrometry (QTOF) acquisition methods. The acquisition methods investigated were MS-Only, all ion fragmentation (All-Ions), and automated tandem mass spectrometry (Auto-MS/MS). Target analytes were the 25 PFAS of US EPA Method 533 and the acquisition methods were evaluated by analyte response, limit of quantification (LOQ), accuracy, precision, and target-suspect screening identification limit (IL). PFAS LOQs were consistent across acquisition methods, with individual PFAS LOQs within an order of magnitude. The mean and range for MS-Only, All-Ions, and Auto-MS/MS are 1.3 (0.34-5.1), 2.1 (0.49-5.1), and 1.5 (0.20-5.1) pg on column. For fast data processing and tentative identification with lower confidence, MS-Only is recommended; however, this can lead to false-positives. Where high-confidence identification, structural characterisation, and quantification are desired, Auto-MS/MS is recommended; however, cycle time should be considered where many compounds are anticipated to be present. For comprehensive screening workflows and sample archiving, All-Ions is recommended, facilitating both quantification and retrospective analysis. This study validated HRMS acquisition approaches for quantification (based upon precursor data) and exploration of identification workflows for a range of PFAS compounds.

Influence of extraction windows for data-independent acquisition on feature annotation during suspect screening

Non-target analysis (NTA) using high-resolution mass spectrometry is becoming a useful approach to screen for suspect and unknown chemicals. For comprehensive analyses, data-independent acquisition (DIA), like Sequential Windowed Acquisition of all THeoretical Mass Spectra (SWATH-MS) on Sciex instruments, is necessary, usually followed by library matching for feature annotation. The choice of parameters, such as acquisition window number and size, may influence the comprehensiveness of the suspect features detected. The goal of this study was to assess how mass spectrometric DIA settings may influence the ability to obtain confident annotations and identifications of features in environmental (river water, passive sample extract (PSE)), wastewater (unpreserved and acidified) and biological (urine) sample matrices. Each matrix was analysed using 11 different MS methods, with 5-15 variable size acquisition windows. True positive (TP) annotation (i.e., matching experimental and library spectra) rates were constant for PSE (40%) and highest for urine (18%), wastewater (34% and 36%, unpreserved and acidified, respectively) and river water (8%) when using higher numbers of windows (15). The number of annotated features was highest for PSE (12%) and urine (8.5%) when using more acquisition windows (9 and 14, respectively). Less complex matrices (based on average total ion chromatogram intensities) like river water, unpreserved and acidified wastewater have higher annotation rates (7.5%, 8% and 13.2%, respectively) when using less acquisition windows (5-6), indicating matrix dependency of optimum settings. Library scores varied widely for correct (scores between 6 and 100) as well as incorrect annotations (scores between 2 and 100), making it hard to define specific ideal cut-off values. Results highlight the need for properly curated libraries and careful optimization of SWATH-MS and other DIA methods for each individual matrix, finding the best ratio of total annotations to true positive, (i.e., correct) annotations to achieve best NTA results.

New PFASs Identified in AFFF Impacted Groundwater by Passive Sampling and Nontarget Analysis

Monitoring contamination from per- and polyfluoroalkyl substances (PFASs) in water systems impacted by aqueous film-forming foams (AFFFs) typically addresses a few known PFAS groups. Given the diversity of PFASs present in AFFFs, current analytical approaches do not comprehensively address the range of PFASs present in these systems. A suspect-screening and nontarget analysis (NTA) approach was developed and applied to identify novel PFASs in groundwater samples contaminated from historic AFFF use. A total of 88 PFASs were identified in both passive samplers and grab samples, and these were dominated by sulfonate derivatives and sulfonamide-derived precursors. Several ultrashort-chain (USC) PFASs (≤C3) were detected, 11 reported for the first time in Australian groundwater. Several transformation products were identified, including perfluoroalkane sulfonamides (FASAs) and perfluoroalkane sulfinates (PFASis). Two new PFASs were reported (((perfluorohexyl)sulfonyl)sulfamic acid; m/z 477.9068 and (E)-1,1,2,2,3,3,4,5,6,7,8,8,8-tridecafluorooct-6-ene-1-sulfonic acid; m/z 424.9482). This study highlights that several PFASs are overlooked using standard target analysis, and therefore, the potential risk from all PFASs present is likely to be underestimated.

Transformation products of antibacterial drugs in environmental water: Identification approaches based on liquid chromatography-high resolution mass spectrometry

In recent years, the presence of antibiotics in the aquatic environment has caused increasing concern for the possible consequences on human health and ecosystems, including the development of antibiotic-resistant bacteria. However, once antibiotics enter the environment, mainly through hospital and municipal discharges and the effluents of wastewater treatment plants, they can be subject to transformation reactions, driven by both biotic (e.g. microorganism and mammalian metabolisms) and abiotic factors (e.g. oxidation, photodegradation, and hydrolysis). The resulting transformation products (TPs) can be less or more active than their parent compounds, therefore the inclusion of TPs in monitoring programs should be mandatory. However, only the reference standards of a few known TPs are available, whereas many other TPs are still unknown, due to the high diversity of possible transformation reactions in the environment. Modern high-resolution mass spectrometry (HRMS) instrumentation is now ready to tackle this problem through suspect and untargeted screening approaches. However, for handling the large amount of data typically encountered in the analysis of environmental samples, these approaches also require suitable processing workflows and accurate tandem mass spectra interpretation. The compilation of a suspect list containing the possible monoisotopic masses of TPs retrieved from the literature and/or from laboratory simulated degradation experiments showed unique advantages. However, the employment of in silico prediction tools could improve the identification reliability. In this review, the most recent strategies relying on liquid chromatography-HRMS for the analysis of environmental TPs of the main antibiotic classes were examined, whereas TPs formed during water treatments or disinfection were not included.

Quantitative Nontarget Analysis of CECs in Environmental Samples Can Be Improved by Considering All Mass Adducts

Quantitative nontarget analysis (qNTA) for liquid chromatography coupled to high-resolution mass spectrometry enables a more comprehensive assessment of environmental samples. Previous studies have shown that correlations between a compound's ionization efficiency and a range of molecular descriptors can predict the compound's concentration within a factor of 5. In this study, the qNTA approach was further improved by considering all mass adducts instead of only the protonated ion. The model was based on a quantitative structure-property relationship (QSPR), including 216 contaminants of emerging concern (CECs), of which 80 exhibited adduct formation that accounted for >10% of the total peak intensity. When all mass adducts were included, the test set coefficient of determination improved to Q2 = 0.855 compared to Q2 = 0.670 when only the protonated ions were considered (test set median RF error factor 1.6). The inclusion of all adducts was also important to transfer the RF QSPR model reliably. It was assumed that RF variations are sequence-dependent; therefore, a second QSPR model for the prediction of the transferability factor was built for each sequence. For validation, samples were analyzed up to two years apart. The median prediction fold change was 1.74 for analytical standards (63 compounds) and 2.4 for enriched wastewater effluent samples (41 compounds), with 80% of the compounds predicted within a fold change of 2.4 and 3.3, respectively. The model was also validated on a second instrument, where 80% of the 26 compounds in wastewater effluent were predicted within a factor of 3.8.

Is Nontarget Analysis Ready for Regulatory Application? Influence of Peak-Picking Algorithms on Data Analysis

The use of peak-picking algorithms is an essential step in all nontarget analysis (NTA) workflows. However, algorithm choice may influence reliability and reproducibility of results. Using a real-world data set, the aim of this study was to investigate how different peak-picking algorithms influence NTA results when exploring temporal and/or spatial trends. For this, drinking water catchment monitoring data, using passive samplers collected twice per year across Southeast Queensland, Australia (n = 18 sites) between 2014 and 2019, was investigated. Data were acquired using liquid chromatography coupled to high-resolution mass spectrometry. Peak picking was performed using five different programs/algorithms (SCIEX OS, MSDial, self-adjusting-feature-detection, two algorithms within MarkerView), keeping parameters identical whenever possible. The resulting feature lists revealed low overlap: 7.2% of features were picked by >3 algorithms, while 74% of features were only picked by a single algorithm. Trend evaluation of the data, using principal component analysis, showed significant variability between the approaches, with only one temporal and no spatial trend being identified by all algorithms. Manual evaluation of features of interest (p-value <0.01, log fold change >2) for one sampling site revealed high rates of incorrectly picked peaks (>70%) for three algorithms. Lower rates (<30%) were observed for the other algorithms, but with the caveat of not successfully picking all internal standards used as quality control. The choice is therefore currently between comprehensive and strict peak picking, either resulting in increased noise or missed peaks, respectively. Reproducibility of NTA results remains challenging when applied for regulatory frameworks.

PBT assessment of chemicals detected in effluent of wastewater treatment plants by suspected screening analysis

Wastewater treatment plants (WWTPs) are the major sources of contaminants discharged into downstream water bodies. Profiling the contaminants in effluent of WWTPs is crucial to assess the potential eco-risks toward downstream organisms. To this end, this study investigated the contaminants in effluent of 10 WWTPs locating in 10 cities of Yangtze River delta region of China by suspected screening analysis. Further, the persistence, bioaccumulation, toxicity (PBT) and the characteristics sub-structures of PBT-like chemicals were analyzed. Totally, 704 chemicals including 155 chemical products, 31 food additives, 52 natural substances, 112 personal care products, 123 pesticides, 192 pharmaceuticals, 17 hormones and 22 others were found. The results of PBT analysis suggested that 42 chemicals (5.97% among the detected chemicals in WWTPs) were with PBT property. Among them, 31 contaminants were not reported previously. 9 characteristics sub-structures (N-methyleneisobutylamine, 1-naphthaldehyde, 2,3,3-trimethylcyclohexene, cyclohexanol, N-sec-butyl-n-propylamine, (5E)-2,6-dimethylocta-1,5-diene, 2-ethylphenol, pentadecane and 6-methoxyhexane) were found for PBT-like chemicals. The sub-structures of highly linear alkyl partially explained the significantly higher PBT score for personal care products. Present study provides fundamental information on PBT properties of contaminants in effluent of WWTPs, which will benefit to prioritize contaminants with high concerns in effluent of WWTPs.

What is in the fish? Collaborative trial in suspect and non-target screening of organic micropollutants using LC- and GC-HRMS

A collaborative trial involving 16 participants from nine European countries was conducted within the NORMAN network in efforts to harmonise suspect and non-target screening of environmental contaminants in whole fish samples of bream (Abramis brama). Participants were provided with freeze-dried, homogenised fish samples from a contaminated and a reference site, extracts (spiked and non-spiked) and reference sample preparation protocols for liquid chromatography (LC) and gas chromatography (GC) coupled to high resolution mass spectrometry (HRMS). Participants extracted fish samples using their in-house sample preparation method and/or the protocol provided. Participants correctly identified 9-69 % of spiked compounds using LC-HRMS and 20-60 % of spiked compounds using GC-HRMS. From the contaminated site, suspect screening with participants' own suspect lists led to putative identification of on average ∼145 and ∼20 unique features per participant using LC-HRMS and GC-HRMS, respectively, while non-target screening identified on average ∼42 and ∼56 unique features per participant using LC-HRMS and GC-HRMS, respectively. Within the same sub-group of sample preparation method, only a few features were identified by at least two participants in suspect screening (16 features using LC-HRMS, 0 features using GC-HRMS) and non-target screening (0 features using LC-HRMS, 2 features using GC-HRMS). The compounds identified had log octanol/water partition coefficient (KOW) values from -9.9 to 16 and mass-to-charge ratios (m/z) of 68 to 761 (LC-HRMS and GC-HRMS). A significant linear trend was found between log KOW and m/z for the GC-HRMS data. Overall, these findings indicate that differences in screening results are mainly due to the data analysis workflows used by different participants. Further work is needed to harmonise the results obtained when applying suspect and non-target screening approaches to environmental biota samples.

Predicting the Diagnostic Information of Tandem Mass Spectra of Environmentally Relevant Compounds Using Machine Learning

Acquisition and processing of informative tandem mass spectra (MS2) is crucial for numerous applications, including library-based (tentative) identification, feature prioritization, and prediction of chemical and toxicological characteristics. However, for environmentally relevant compounds, approaches to automatically assess the quality of the MS2 spectra are missing. This work focused on developing a machine learning-based approach to automatically evaluate the diagnostic information of MS2 spectra (e.g., number, distribution, and intensity of diagnostic fragments) of environmentally relevant compounds analyzed with electrospray ionization. For this, approximately 1400 MS2 spectra of 204 environmental contaminants, acquired with different collision energies using liquid chromatography coupled to high-resolution mass spectrometry, were used to train a random forest classifier to distinguish between spectra providing good or poor diagnostic information. Prior to training, validation, and testing, spectra were manually labeled based on criteria such as number, intensity, range of fragments present, molecular ion intensity, and noise levels. Subsequently, feature engineering and selection were applied to retrieve relevant variables from raw MS2 spectra as inputs for the classifier. The optimal set of features based on model performances was selected and used to train a final model, which showed an accuracy of 84%, a precision of 88%, and a recall of 75%. Results show that the combination of selected features and the machine learning model used here can effectively distinguish between MS2 spectra providing good or poor diagnostic information according to the defined criteria. The developed model has the potential to improve a broad range of applications that rely on MS2 data.

An integrated non-targeted and targeted analysis approach for identification of semi-volatile organic compounds in indoor dust

Household dust contains a wide variety of semi-volatile organic compounds (SVOCs) that may pose health risks. We developed a method integrating non-targeted analysis (NTA) and targeted analysis (TA) to identify SVOCs in indoor dust. Based on a combined use of gas and liquid chromatography with high-resolution mass spectrometry, an automated, time-efficient NTA workflow was developed, and high accuracy was observed. A total of 128 compounds were identified at confidence level 1 or 2 in NIST standard reference material dust (SRM 2585). Among them, 113 compounds had not been reported previously, and this suggested the value of NTA in characterizing contaminants in dust. Additionally, TA was done to avoid the loss of trace compounds. By integrating data obtained from the NTA and TA approaches, SVOCs in SRM 2585 were prioritized based on exposure and chemical toxicity. Six of the top 20 compounds have never been reported in SRM 2585, including melamine, dinonyl phthalate, oxybenzone, diheptyl phthalate, drometrizole, and 2-phenylphenol. Additionally, significant influences of analytical instruments and sample preparation on NTA results were observed. Overall, the developed method provided a powerful tool for identifying SVOCs in indoor dust, which is necessary to obtain a more complete understanding of chemical exposures and risks in indoor environments.

Microbial volatile organic compounds as novel indicators of anaerobic digestion instability: Potential and challenges

The wide application of anaerobic digestion (AD) technology is limited by process fluctuations. Thus, process monitoring based on screening state parameters as early warning indicators (EWI) is a top priority for AD facilities. However, predicting anaerobic digester stability based on such indicators is difficult, and their threshold values are uncertain, case-specific, and sometimes produce conflicting results. Thus, new EWI should be proposed to integrate microbial and metabolic information. These microbial volatile organic compounds (mVOCs) including alkanes, alkenes, alkynes, aromatic compounds are produced by microorganisms (bacteria, archaea and fungi), which might serve as a promising diagnostic tool for environmental monitoring. Moreover, mVOCs diffuse in both gas and liquid phases and are considered the language of intra kingdom microbial interactions. Herein, we highlight the potential of mVOCs as EWI for AD process instability, including discussions regarding characteristics and sources of mVOCs as well as sampling and determination methods. Furthermore, existing challenges must be addressed, before mVOCs profiling can be used as an early warning system for diagnosing AD process instability, such as mVOCs sampling, analysis and identification. Finally, we discuss the potential biotechnology applications of mVOCs and approaches to overcome the challenges regarding their application.

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.

qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry

Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.

Multidimensional chromatography in environmental analysis: Comprehensive two-dimensional liquid versus gas chromatography

Analysis of complex environmental matrices poses an extreme challenge for analytical chemists due to the vast number of known and unknown compounds, with very diverse chemical and physical properties. The need for a holistic characterisation of this complexity has sparked the development of effective tools to unravel the chemical composition of such environmental samples. Multidimensional chromatographic methods, namely comprehensive two-dimensional (2D) gas and liquid chromatography (GC × GC and LC × LC, respectively), coupled to different detection systems have emerged as powerful tools with the capability to address this challenge. While GC × GC has steadily gained popularity in environmental analysis, LC × LC is surprisingly less attractive in this research field. This critical review article explores the potential reasons why LC × LC is not the dominant technique used in environmental analysis as compared to GC × GC, while simultaneously highlighting the quite unique role of LC × LC for the target and untargeted analysis of complex environmental matrices. The possible combinations of stationary phases, the important role of the interfacing valve as the heart of an LC × LC assembly, the existing optimization strategies for improving the separation power in the 2D chromatographic space, and the need for user-friendly mathematical tools for multidimensional data handling are also discussed. Finally, a set of practical measures are suggested to increase the use and secure the success of LC × LC in environmental analysis.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Novel Per- and Polyfluoroalkyl Substances Discovered in Cattle Exposed to AFFF-Impacted Groundwater

The leaching of per- and polyfluoroalkyl substances (PFASs) from Australian firefighting training grounds has resulted in extensive contamination of groundwater and nearby farmlands. Humans, farm animals, and wildlife in these areas may have been exposed to complex mixtures of PFASs from aqueous film-forming foams (AFFFs). This study aimed to identify PFAS classes in pooled whole blood (n = 4) and serum (n = 4) from cattle exposed to AFFF-impacted groundwater and potentially discover new PFASs in blood. Thirty PFASs were identified at various levels of confidence (levels 1a-5a), including three novel compounds: (i) perfluorohexanesulfonamido 2-hydroxypropanoic acid (FHxSA-HOPrA), (ii) methyl((perfluorohexyl)sulfonyl)sulfuramidous acid, and (iii) methyl((perfluorooctyl)sulfonyl)sulfuramidous acid, belonging to two different classes. Biotransformation intermediate, perfluorohexanesulfonamido propanoic acid (FHxSA-PrA), hitherto unreported in biological samples, was detected in both whole blood and serum. Furthermore, perfluoroalkyl sulfonamides, including perfluoropropane sulfonamide (FPrSA), perfluorobutane sulfonamide (FBSA), and perfluorohexane sulfonamide (FHxSA) were predominantly detected in whole blood, suggesting that these accumulate in the cell fraction of blood. The suspect screening revealed several fluoroalkyl chain-substituted PFAS. The results suggest that targeting only the major PFASs in the plasma or serum of AFFF-exposed mammals likely underestimates the toxicological risks associated with exposure. Future studies of AFFF-exposed populations should include whole-blood analysis with high-resolution mass spectrometry to understand the true extent of PFAS exposure.

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

Analytical approaches for screening of per- and poly fluoroalkyl substances in food items: A review of recent advances and improvements

Per and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals characterized by their ubiquitous nature in all environmental compartments which makes them of increasing concern due to their persistence, bioaccumulation, and toxicity (PBT). Several instrumental methodologies and separation techniques have been identified in the literature for the detection and quantification of PFAS in environmental samples. In this review, we have identified and compared common separation techniques adopted for the extraction of PFAS in food items, and analytical methodologies for identification and quantification of PFAS in food items of plant and animal origin, highlighting recent advances in tandem techniques for the high selectivity and separation of PFAS related compounds as well as knowledge gaps and research needs on current analytical methodologies.

Practical application guide for the discovery of novel PFAS in environmental samples using high resolution mass spectrometry

BACKGROUND

The intersection of the topics of high-resolution mass spectrometry (HRMS) and per- and polyfluoroalkyl substances (PFAS) bring together two disparate and complex subjects. Recently non-targeted analysis (NTA) for the discovery of novel PFAS in environmental and biological media has been shown to be valuable in multiple applications. Classical targeted analysis for PFAS using LC-MS/MS, though growing in compound coverage, is still unable to inform a holistic understanding of the PFAS burden in most samples. NTA fills at least a portion of this data gap.

OBJECTIVES

Entrance into the study of novel PFAS discovery requires identification techniques such as HRMS (e.g., QTOF and Orbitrap) instrumentation. This requires practical knowledge of best approaches depending on the purpose of the analyses. The utility of HRMS applications for PFAS discovery is unquestioned and will likely play a significant role in many future environmental and human exposure studies.

METHODS/RESULTS

PFAS have some characteristics that make them standout from most other chemicals present in samples. Through a series of tell-tale PFAS characteristics (e.g., characteristic mass defect range, homologous series and characteristic fragmentation patterns), and case studies different approaches and remaining challenges are demonstrated.

IMPACT STATEMENT

The identification of novel PFAS via non-targeted analysis using high resolution mass spectrometry is an important and difficult endeavor. This synopsis document will hopefully make current and future efforts on this topic easier to perform for novice and experienced alike. The typical time devoted to NTA PFAS investigations (weeks to months or more) may benefit from these practical steps employed.

Advanced mass spectrometry profiling of phenolic and minerals compounds in herbal beverages

The global pandemic of COVID-19 has led to an increased interest in herbal infusions as natural remedies since 2020. This has also heightened the need for controlling the composition of these dietary supplements to ensure consumer health and prevent food fraud. In the present work, various mass spectrometry techniques were used to analyze the organic and inorganic composition of 23 herbal infusion samples. UHPLC-ESI-QTOF-MS was used to determine target, suspect, and nontarget polyphenolic compounds. Thus, 8 phenolic compounds were identified in the target analysis and additionally, 80 extra-compounds were identified through suspect and nontargeted screening. ICP-MS was used to monitor the metals released during tea leaf infusion, providing a complete mineral composition of each sample. Principal Component Analysis (PCA) and Discriminant Analysis (DA) were utilized to identify relevant compounds for differentiating and grouping the samples, thus serving as specific markers to detect potential food fraud.

Novel nontarget LC-HRMS-based approaches for evaluation of drinking water treatment

A conventional evaluation methodology for drinking water pollution focuses on analysing hundreds of compounds, usually by liquid chromatography-tandem mass spectrometry. High-resolution mass spectrometry allows comprehensive evaluation of all detected signals (compounds) based on their elemental composition, intensity, and numbers. We combined target analysis of 192 emerging micropollutants with nontarget (NT) full-scan/MS/MS methods to describe the impact of treatment steps in detail and assess drinking water treatment efficiency without compound identification. The removal efficiency based on target analytes ranged from - 143 to 97%, depending on the treatment section, technologies, and season. The same effect calculated for all signals detected in raw water by the NT method ranged between 19 and 65%. Ozonation increased the removal of micropollutants from the raw water but simultaneously caused the formation of new compounds. Moreover, ozonation byproducts showed higher persistence than products formed during other types of treatment. We evaluated chlorinated and brominated organics detected by specific isotopic patterns within the developed workflow. These compounds indicated anthropogenic raw water pollution but also potential treatment byproducts. We could match some of these compounds with libraries available in the software. We can conclude that passive sampling combined with nontargeted analysis shows to be a promising approach for water treatment control, especially for long-term monitoring of changes in technology lines because passive sampling dramatically reduces the number of samples and provides time-weighted average information for 2 to 4 weeks.

InSpectra - A platform for identifying emerging chemical threats

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data

Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.

PFAS on atmospheric aerosol particles: a review

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of concern to human health. These synthetic chemicals are in widespread use for consumer products, firefighting foams, and industrial applications. They have been detected all over the globe, including at remote locations distant from any possible point sources. One mechanism for long-range transport of PFAS is through sorption to aerosol particles in the atmosphere. PFAS can be transferred from the sea surface to sea spray aerosol particles through wave breaking and bubble bursting, and PFAS emitted to the atmosphere in the gas phase can sorb to particulate matter through gas-particle partitioning. Here we present a comprehensive review of global measurements of PFAS on ambient particulate matter dating back to the first reports from the early 2000s. We summarize findings for the historically important C8 species, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS), including detection of isomers and size-segregated measurements, as well as studies of newer and emerging PFAS. We conclude that long-term monitoring of PFAS on particulate matter should be expanded to include more measurement sites in under-sampled regions of the world and that further non-targeted work to identify novel PFAS structures is needed as PFAS manufacturing and regulations continue to evolve.

Identification and trend analysis of organic cationic contaminants via non-target screening in suspended particulate matter of the German rivers Rhine and Saar

Non-target screening of suspended particulate matter (SPM), collected from the German rivers Rhine and Saar, was conducted with the goal of identifying organic, permanent cationic contaminants and of estimating their temporal trends over an extended period. Therefore, annual composite samples of SPM, provided by the German Environmental Specimen Bank, were extracted and analyzed with high resolution LC-QToF-MS/MS. To facilitate the identification of substances belonging to the class "permanent cations", prioritization methods were applied utilizing the physicochemical properties of these compounds. These methods include both interactions of the analyte molecules with cation exchange resins and analyzing mass deviations when changing from non-deuterated to deuterated mobile phase solvents during LC-MS analysis. By applying both methods in a combined approach, 123 of the initially detected 2695 features were prioritized, corresponding to a 95% data reduction. This led to the identification of 22 permanent cationic species. The organic dyes Basic Yellow 28 and Fluorescent Brightener 363 as well as two quaternary ammonium compounds (QACs) were detected in environmental samples for the first time to best of or knowledge. The other compounds include additional QACs, as well as quaternary tri-phenylphosphonium compounds (QPC/TPP). In addition to identification, we determined temporal trends of all compounds over a period of 13 years and assessed their ecotoxicological relevance based on estimated concentrations. The two QACs oleyltrimethylammonium and eicosyltrimethylammonium show significant increasing trends in the Rhine SPM and maximum concentrations in the Saar SPM of about 900 and 1400 µg/kg, respectively. In the case of the dyes, constant trends have been observed at the end of the studied period, but also maximum concentrations of 400 µg/kg for Basic Yellow 28 in 2006 and 1000 µg/kg for Fluorescent Brightener 363 in 2015, potentially indicating a strong ecotoxicological risk.

Influence of data acquisition modes and data analysis approaches on non-targeted analysis of phthalate metabolites in human urine

Humans are often exposed to phthalates and their alternatives, on account of their widespread use in PVC as plasticizers, which are associated with harmful human effects. While targeted biomonitoring provides quantitative information for exposure assessment, only a small portion of phthalate metabolites has been targeted. This results in a knowledge gap in human exposure to other unknown phthalate compounds and their metabolites. Although the non-targeted analysis (NTA) approach is capable of screening a broad spectrum of chemicals, there is a lack of harmonized workflow in NTA to generate reproducible data within and between different laboratories. The objective of this study was to compare two different NTA data acquisition modes, the data-dependent (DDA) and independent (DIA) acquisition (DDA), as well as two data analysis approaches, based on diagnostic ions and Compound Discoverer software for the prioritization of candidate precursors and identification of unknown compounds in human urine. Liquid chromatography coupled to high-resolution mass spectrometry was used for sample analysis. The combination of three-diagnostic-ion extraction and DDA data acquisition was able to improve data filtering and data analysis for prioritizing phthalate metabolites. With DIA, 25 molecular features were identified in human urine, while 32 molecular features were identified in the same urine samples using DDA data. The number of molecular features identified with level 1 confidence was 11 and 9 using DIA and DDA data, respectively. The study demonstrated that besides sample preparation, the impact of data acquisition must be taken into account when developing a NTA method and a consistent protocol for evaluating such an impact is necessary.

Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.

Making use of apex predator sample collections: an integrated workflow for quality assured sample processing, analysis and digital sample freezing of archived samples

Using monitoring data from apex predators for chemicals risk assessment can provide important information on bioaccumulating as well as biomagnifying chemicals in food webs. A survey among European institutions involved in chemical risk assessment on their experiences with apex predator data in chemical risk assessment revealed great interest in using such data. However, the respondents indicated that constraints were related to expected high costs, lack of standardisation and harmonised quality criteria for exposure assessment, data access, and regulatory acceptance/application. During the Life APEX project, we demonstrated that European sample collections (i.e. environmental specimen banks (ESBs), research collection (RCs), natural history museums (NHMs)) archive a large variety of biological samples that can be readily used for chemical analysis once appropriate quality assurance/control (QA/QC) measures have been developed and implemented. We therefore issued a second survey on sampling, processing and archiving procedures in European sample collections to derive key quality QA/QC criteria for chemical analysis. The survey revealed great differences in QA/QC measures between ESBs, NHMs and RCs. Whereas basic information such as sampling location, date and biometric data were mostly available across institutions, protocols to accompany the sampling strategy with respect to chemical analysis were only available for ESBs. For RCs, the applied QA/QC measures vary with the respective research question, whereas NHMs are generally less aware of e.g. chemical cross-contamination issues. Based on the survey we derived key indicators for assessing the quality of biota samples that can be easily implemented in online databases. Furthermore, we provide a QA/QC workflow not only for sampling and processing but also for the chemical analysis of biota samples. We focussed on comprehensive analytical techniques such as non-target screening and provided insights into subsequent storage of high-resolution chromatograms in online databases (i.e. digital sample freezing platform) to ultimately support chemicals risk assessment.

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals' super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.

Non-targeted screening of volatile organic compounds in a museum in China Using GC-Orbitrap mass spectrometry

Non-targeted analysis (NTA) was used in identifying volatile organic compounds (VOCs) in a museum in China with the gas chromatograph (GC)-Orbitrap-mass spectrometer (MS). Approximately 230 VOCs were detected, of which 117 were observed at 100% frequency across all sampling sites. Although some were common in indoor environments, most of the detected VOCs were rarely reported in previous studies on museum environments. Some of the detected VOCs were found to be associated with the materials used in furnishings and the chemicals applied in conservation treatment. Spearman's correlation analysis showed that several classes of VOCs were well correlated, suggesting their common sources. Compared with compounds in outdoor air, indoor VOCs had a lower level of unsaturation and more portions of chemically reduced compounds. Hierarchical cluster analysis (HCA) were performed. The results suggested that the sampling adsorbents chosen may have a large impact and that a single type of adsorbent may not be sufficient to cover a wide range of compounds in NTA studies. The MonoTrap adsorbent containing octadecylsilane (ODS) and activated carbon (AC) is suitable for aliphatic polar compounds that contain low levels of oxygen, whereas the MonoTrap ODS and silica gel are good at sampling aliphatic and aromatic hydrocarbons with limited polarity. Principle component analysis (PCA) showed that the indoor VOCs changed significantly at different times in the museum; this may have been caused by the removal of artifacts and refurbishment of the gallery between sampling events. A comparison with compounds identified by chamber emission tests showed that decorative materials may have been one of the main sources of indoor VOCs in the museum. The VOCs identified in the present study are likely to be present in other similar museums; therefore, further examination may be warranted of their potential impacts on cultural heritage artifacts, museum personnel, and visitors.

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.