Examining NTA performance and potential using fortified and reference house dust as part of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT)

Innovative non-targeted screening approach using High-resolution mass spectrometry for the screening of organic chemicals and identification of specific tracers of soil and dust exposure in children

Environmental contamination through direct contact, ingestion and inhalation are common routes of children's exposure to chemicals, in which through indoor and outdoor activities associated with common hand-to-mouth, touching objects, and behavioral tendencies, children can be susceptible and vulnerable to organic contaminants in the environment. The objectives of this study were the screening and identification of a wide range of organic contaminants in indoor dust, soil, food, drinking water, and urine matrices (N = 439), prioritizing chemicals to assess children's environmental exposure, and selection of unique tracers of soil and dust ingestion in young children by non-targeted analysis (NTA) using Q-Exactive Orbitrap followed data processing by the Compound Discoverer (v3.3, SP2). Chemical features were first prioritized based on their predominant abundance (peak area>500,000), detection frequency (in >50% of the samples), available information on their uses and potential toxicological effects. Specific tracers of soil and dust exposure in children were selected in this study including Tripropyl citrate and 4-Dodecylbenzenesulfonic acid. The criteria for selection of the tracers were based on their higher abundance, detection frequency, unique functional uses, measurable amounts in urine (suitable biomarker), and with information on gastrointestinal absorption, metabolism, and excretion, and were further confirmed by authentic standards. We are proposing for the first time suitable unique tracers for dust ingestion by children.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

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.

Burning questions: Current practices and critical gaps in evaluating removal of per- and polyfluoroalkyl substances (PFAS) during pyrolysis treatments of biosolids

Concerns surrounding potential health and environmental impacts of per- and polyfluoroalkyl substances (PFAS) are growing at tremendous rates because adverse health impacts are expected with trace-level exposures. Extreme measures are required to mitigate potential PFAS contamination and minimize exposures. Extensive PFAS use results in the release of diverse PFAS species from domestic, industrial, and municipal effluents to wastewater, which partition to biosolids throughout secondary treatment. Biosolids generated during municipal wastewater treatment are a major environmental source of PFAS due to prevailing disposal practices as fertilizers. Pyrolysis is emerging as a viable, scalable technology for PFAS removal from biosolids while retaining nutrients and generating renewable, raw materials for energy generation. Despite early successes of pyrolysis in PFAS removal, significant unknowns remain about PFAS and transformation product fates in pyrolysis products and emissions. Applicable PFAS sampling methods, analytical workflows, and removal assessments are currently limited to a subset of high-interest analytes and matrices. Further, analysis of exhaust gases, particulate matter, fly ashes, and other pyrolysis end-products remain largely unreported or limited due to cost and sampling limitations. This paper identifies critical knowledge gaps on the pyrolysis of biosolids that must be addressed to assess the effectiveness of PFAS removal during pyrolysis treatment.

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.

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.

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).

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.

Demonstrating the Use of Non-targeted Analysis for Identification of Unknown Chemicals in Rapid Response Scenarios

Several thousand intentional and unintentional chemical releases occur annually in the U.S., with the contents of almost 30% being of unknown composition. When targeted methods are unable to identify the chemicals present, alternative approaches, including non-targeted analysis (NTA) methods, can be used to identify unknown analytes. With new and efficient data processing workflows, it is becoming possible to achieve confident chemical identifications via NTA in a timescale useful for rapid response (typically 24-72 h after sample receipt). To demonstrate the potential usefulness of NTA in rapid response situations, we have designed three mock scenarios that mimic real-world events, including a chemical warfare agent attack, the contamination of a home with illicit drugs, and an accidental industrial spill. Using a novel, focused NTA method that utilizes both existing and new data processing/analysis methods, we have identified the most important chemicals of interest in each of these designed mock scenarios in a rapid manner, correctly assigning structures to more than half of the 17 total features investigated. We have also identified four metrics (speed, confidence, hazard information, and transferability) that successful rapid response analytical methods should address and have discussed our performance for each metric. The results reveal the usefulness of NTA in rapid response scenarios, especially when unknown stressors need timely and confident identification.

Evaluation of a rapid, generic human gestational dose model

Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.

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.

Monitoring of Environmental Contaminants in Mixed-Use Watersheds Combining Targeted and Nontargeted Analysis with Passive Sampling

Understanding the environmental fate, transport, and occurrence of pesticides and pharmaceuticals in aquatic environments is of utmost concern to regulators. Traditionally, monitoring of environmental contaminants in surface water has consisted of liquid chromatography-tandem mass spectrometry analyses for a set of targeted compounds in discrete samples. These targeted approaches are limited by the fact that they only provide information on compounds within a target list present at the time and location of sampling. To address these limitations, there has been considerable interest in suspect screening and nontargeted analysis (NTA), which allow for the detection of all ionizable compounds in the sample with the added benefit of data archiving for retrospective mining. Even though NTA can detect a large number of contaminants, discrete samples only provide a snapshot perspective of the chemical disposition of an aquatic environment at the time of sampling, potentially missing episodic events. We evaluated two types of passive chemical samplers for nontargeted analysis in mixed-use watersheds. Nontargeted data were processed using MS-DIAL to screen against our in-house library and public databases of more than 1300 compounds. The data showed that polar organic chemicals integrative samplers (POCIS) were able to capture the largest number of analytes with better reproducibility than organic compound-diffusive gradients in thin film (o-DGT), resulting from the greater amount of binding sorbent. We also showed that NTA combined with passive sampling gives a more representative picture of the contaminants present at a given site and enhances the ability to identify the nature of point and nonpoint pollution sources and ecotoxicological impacts. Environ Toxicol Chem 2022;41:1131-1143. © 2021 Her Majesty the Queen in Right of Canada Environmental Toxicology and Chemistry © 2021 SETAC. Reproduced with the permission of the Minister of Agriculture and Agri-Food Canada.

Assessing Reliability of Non-targeted High-Resolution Mass Spectrometry Fingerprints for Quantitative Source Apportionment in Complex Matrices

Effective management of contaminated sites requires differentiating and deconvoluting contaminant source impacts in complex environmental systems. The existing source apportionment approaches that use targeted analyses of preselected indicator chemicals are limited whenever target analytes are below the detection limits or derived from multiple sources. However, non-targeted analyses that leverage high-resolution mass spectrometry (HRMS) yield rich datasets that deeply characterize sample-specific chemical compositions, providing additional potential end-members for source differentiation and apportionment. Previous work demonstrated that HRMS fingerprints can define sample uniqueness and support accurate, quantitative source concentration estimates. Here, using two aqueous film-forming foams as representative complex sources, we assessed the qualitative fidelity and quantitative accuracy of HRMS source fingerprints in increasingly complex background matrices. Across all matrices, HRMS-derived source concentration estimates were 0.81 ± 0.11-fold and 0.64 ± 0.24-fold of actual in samples impacted solely by analytical matrix effects (MEs) or by sample processing recovery and analytical MEs, respectively. Isotopic internal standards were not easily paired to individual unidentified non-target features, but bulk internal standard-based abundance corrections improved apportionment accuracy in higher matrix samples (to 0.90 ± 0.12-fold of actual) and/or informed concentration estimate relative errors. HRMS fingerprint mining could identify, based on the dilution behavior, effective individual chemical end-members across 16 homologous series. Although method development is needed, the results further demonstrate the potential applications of non-targeted HRMS data for source apportionment and other quantitative outcomes.

Prevalence and Implications of Per- and Polyfluoroalkyl Substances (PFAS) in Settled Dust

PURPOSE OF REVIEW

Per- and polyfluoroalkyl substances (PFAS) are a family of more than 7,000 fluorinated compounds. The carbon-fluorine bond of PFAS provides desirable hydrophobic and oleophobic properties and stability that has led to widespread usage in consumer products and industrial applications. The strength of the carbon-fluorine bond also prevents appreciable degradation once released into the environment. Consequently, various household products can release volatile and nonvolatile PFAS into the indoor environment that often concentrate in dust. We discuss the diversity of PFAS in settled dust, emission sources of these chemicals, changes in PFAS profiles in dust over the past century, and the implications for human health.

RECENT FINDINGS

Sources of PFAS found in dust include building materials and furnishings and consumer products used in typical indoor spaces. Daycares and workplaces are emphasized as locations with widespread exposure due to the presence of treated carpeting and industrial-strength cleaners. Comparison and interpretation of findings across studies are complicated by the different ways in which PFAS are screened across studies. We further discuss recent developments in non-targeted software for the comprehensive annotation of PFAS in indoor dust and emphasize the need for comprehensive and harmonized analytical workflows. We highlight the detection and diversity of PFAS in settled dust collected from various indoor spaces, including locations with vulnerable subpopulations. There are opportunities for future research to leverage settled dust as a sentinel environmental matrix to evaluate the link between inhalation and ingestion routes of PFAS exposure to adverse health.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.

An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group

Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to 'omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Thus far, BP4NTA group members have aimed to establish a consensus on NTA-related terms and concepts and to create consistency in reporting practices by providing resources on a public Web site, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.

Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis

With the increasing availability of high-resolution mass spectrometers, suspect screening and non-targeted analysis are becoming popular compound identification tools for environmental researchers. Samples of interest often contain a large (unknown) number of chemicals spanning the detectable mass range of the instrument. In an effort to separate these chemicals prior to injection into the mass spectrometer, a chromatography method is often utilized. There are numerous types of gas and liquid chromatographs that can be coupled to commercially available mass spectrometers. Depending on the type of instrument used for analysis, the researcher is likely to observe a different subset of compounds based on the amenability of those chemicals to the selected experimental techniques and equipment. It would be advantageous if this subset of chemicals could be predicted prior to conducting the experiment, in order to minimize potential false-positive and false-negative identifications. In this work, we utilize experimental datasets to predict the amenability of chemical compounds to detection with liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). The assembled dataset totals 5517 unique chemicals either explicitly detected or not detected with LC-ESI-MS. The resulting detected/not-detected matrix has been modeled using specific molecular descriptors to predict which chemicals are amenable to LC-ESI-MS, and to which form(s) of ionization. Random forest models, including a measure of the applicability domain of the model for both positive and negative modes of the electrospray ionization source, were successfully developed. The outcome of this work will help to inform future suspect screening and non-targeted analyses of chemicals by better defining the potential LC-ESI-MS detectable chemical landscape of interest.

Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Organic pollutants can be identified by comparing their electron ionization (EI) mass spectra with those in libraries or obtained from authentic standards. Nevertheless, libraries are incomplete; standards may be unavailable or too costly, or their synthesis may be too time-consuming. This study evaluates the performance of quantum chemical electron ionization mass spectrometry (QCEIMS) vis-à-vis competitive fragmentation modeling (CFM) for suspect screening and unknown identification. EI mass spectra of 35 compounds, including halogenated organics, organophosphorus flame retardants (OPFRs), and disinfection byproducts were computed. Computational results were compared with EI mass spectra compiled in the NIST Library as well as collision-induced dissociation (CID) mass spectra obtained from radical cations M^•+ generated by charge-exchange atmospheric pressure chemical ionization (APCI). The results indicate that QCEIMS performs equivalently or better than CFM. Average match factors between computed and experimental (NIST) EI mass spectra were 656 vs 503 for the halogenated organics, and on average, QCEIMS predicted 55% of the products generated by CID vs 17% predicted by CFM. QCEIMS predicted 37% of the OPFR CID products whereas CFM predicted 29%. QCEIMS performed comparably to a commercial combinatorial fragmentation method for suspect screening of a dust sample, identifying 19/20 targets. Examples of unknown pollutants, whose reference spectra were unavailable at the time of discovery, are also presented. The computational results suggest that QCEIMS can help guide the analyst in obtaining authentic standards and raise the possibility that, with advances in computing, an unknown may eventually be confirmed in hours as opposed to the days or months required to obtain authentic standards.

patRoon: open source software platform for environmental mass spectrometry based non-target screening

Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

A Proposed Quality Control Standard Mixture and Its Uses for Evaluating Nontargeted and Suspect Screening LC/HR-MS Method Performance

Nontargeted (NTA) and suspect screening analyses (SSA) aim to detect and identify unknown compounds of interest from a given sample. The complexity and diversity of NTA and SSA methodologies necessitate the use of a comprehensive quality control standard mixture to determine if methods are fit for purpose, but to our knowledge, such a standard has not been developed that can be used by multiple disciplines, nor is one readily available. This work describes the development and analysis of a proposed nontargeted standard/quality control mixture for NTA and SSA applications using liquid chromatography/electrospray ionization-high resolution-mass spectrometry. Considerations in its development included achieving diversity of compounds with respect to elemental composition, molecular weight, retention time, and ionization in positive and/or negative ion modes, which resulted in the inclusion of 89 compounds. The utility of the standard mixture was applied on our own NTA and SSA workflows where sample preparation efficiency and potential sources of error due to instrumental and data processing methods were evaluated. Some areas in need of improvement were identified, such as hydrophilic compound detection and molecular formula generation for compounds containing fluorine. However, our overall methodology was found to be fit for purpose and we were able to establish thresholds to increase reliability and throughput of reported results.