The chemical space for non-target analysis

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.

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).

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.

LC-HRMS based method for suspect/non-targeted screening for biomarkers of chemical exposure in human urine

Every day we are exposed to a cocktail of anthropogenic compounds many of which are biologically active and capable of inducing negative effects. The simplest way to monitor contaminants in a population is via human biomonitoring (HBM), however conventional targeted approaches require foreknowledge of chemicals of concern, often have compound specific extractions and provide information only for those compounds. This study developed an extraction process for human biomarkers of interest (BoE) in urine that is less compound specific. Combining this with an ultra-high resolution mass spectrometer capable of operating in full scan, and a suspect and non-targeted analysis (SS/NTA) approach, this method provides a more holistic characterization of human exposure. Sample preparation development was based on enzymatically hydrolysed urine spiked with 34 native standards and extracted by solid-phase extraction (SPE). HRMS data was processed by MzMine2 and 80% of standards were identified in the final data matrix using typical NTA data processing procedures.

From target analysis to suspect and non-target screening of endocrine-disrupting compounds in human urine

In the present work, a target analysis method for simultaneously determining 24 diverse endocrine-disrupting compounds (EDCs) in urine (benzophenones, bisphenols, parabens, phthalates and antibacterials) was developed. The target analysis approach (including enzymatic hydrolysis, clean-up by solid-phase extraction and analysis by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS)) was optimized, validated and applied to volunteers’ samples, in which 67% of the target EDCs were quantified. For instance, benzophenone-3 (0.2–13 ng g⁻¹), bisphenol A (7.7–13.7 ng g⁻¹), methyl 3,5-dihydroxybenzoate (8–254 ng g⁻¹), mono butyl phthalate (2–17 ng g⁻¹) and triclosan (0.3–9 ng g⁻¹) were found at the highest concentrations, but the presence of other analogues was detected as well. The developed target method was further extended to suspect and non-target screening (SNTS) by means of LC coupled to high-resolution MS/MS. First, well-defined workflows for SNTS were validated by applying the previously developed method to an extended list of compounds (83), and then, to the same real urine samples. From a list of approximately 4000 suspects, 33 were annotated at levels from 1 to 3, with food additives/ingredients and personal care products being the most abundant ones. In the non-target approach, the search was limited to molecules containing S, Cl and/or Br atoms, annotating 4 pharmaceuticals. The results from this study showed that the combination of the lower limits of detection of MS/MS and the identification power of high-resolution MS/MS is still compulsory for a more accurate definition of human exposome in urine samples.

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.

Evidence of complementarity between targeted and non-targeted analysis based on liquid and gas-phase chromatography coupled to mass spectrometry for screening halogenated persistent organic pollutants in environmental matrices

This study explored the complementarity between targeted (TS) and non-targeted screening (NTS) based on liquid and gas-phase chromatography coupled to (high-resolution) mass spectrometry (LC-/GC-(HR)MS) for the comprehensive characterization of organohalogen fingerprints within a set of Lake Ontario lake trout samples. The concentrations of 86 legacy, emerging and novel halogenated compounds (HCs), were determined through 4 TS approaches involving no less than 6 hyphenated systems. In parallel, an innovative NTS strategy, involving both LC and GC-Q-Orbitrap, was implemented to specifically highlight halogenated signals. Non-targeted HRMS data were processed under the HaloSeeker software based on Cl and Br isotopic ratio and mass defect to extend the screening to unsuspected and unknown HCs. A total of 195 halogenated mass spectral features were characterized in the Lake Ontario lake trout, including well known HCs (PCBs, PBDEs, PBBs, DDT and their degradation products), emerging HCs (novel brominated flame retardants, short-, medium- and long-chain chlorinated paraffins) or suggested molecular formula (mainly polychlorinated ones). Among the 122 HCs highlighted by TS, only 21 were identified by NTS. These results fueled a discussion on the potential and limitations of both approaches, and the current position of NTS within environmental and health monitoring programs.

Characterizing the Chemical Profile of Biological Decline in Stormwater-Impacted Urban Watersheds

Chemical contamination is an increasingly important conservation issue in urban runoff-impacted watersheds. Regulatory and restoration efforts typically evaluate limited conventional parameters and pollutants. However, complex urban chemical mixtures contain hundreds to thousands of organic contaminants that remain unidentified, unregulated, and poorly understood. This study aimed to develop broadly representative metrics of water quality impairment corresponding to previously documented biological degradation along gradients of human impacts. Stream samples (n = 65, baseflow/rainfall conditions, 2017-2018) were collected from 15 regional watersheds (Puget Sound, WA, USA) across an urbanization gradient defined by landscape characteristics. Surface water chemical composition characterized via non-targeted high-resolution mass spectrometry (7068 detections) was highly correlated with landscape-based urbanization gradient (p < 0.01) and season (p < 0.01). Landscape-scale changes in chemical composition closely aligned with two anchors of biological decline: coho salmon (Oncorhynchus kisutch) mortality risk (p < 0.001) and loss of stream macroinvertebrate diversity and abundance (p < 0.001). We isolated and identified 32 indicators for urban runoff impacts and corresponding receiving water ecological health, including well-known anthropogenic contaminants (e.g., caffeine, organophosphates, vehicle-derived chemicals), two related environmental transformation products, and a novel (methoxymethyl)melamine compound. Outcomes support data-directed selection of next-generation water quality indicators for prioritization and evaluation of watershed management efforts intended to protect aquatic ecosystems.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.

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.

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.

An optimized LC-HRMS untargeted metabolomics workflow for multi-matrices investigations in the three-spined stickleback

Environmental metabolomics has become a growing research field to understand biological and biochemical perturbations of organisms in response to various abiotic or biotic stresses. It focuses on the comprehensive and systematic analysis of a biologic system’s metabolome. This allows the recognition of biochemical pathways impacted by a stressor, and the identification of some metabolites as biomarkers of potential perturbations occurring in a body. In this work, we describe the development and optimization of a complete reliable methodology based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) for untargeted metabolomics studies within a fish model species, the three-spined stickleback (Gasterosteus aculeatus). We evaluated the differences and also the complementarities between four different matrices (brain, gills, liver and whole fish) to obtain metabolome information. To this end, we optimized and compared sample preparation and the analytical method, since the type and number of metabolites detected in any matrix are closely related to these latter. For the sample preparation, a solid-liquid extraction was performed on a low quantity of whole fish, liver, brain, or gills tissues using combinations of methanol/water/heptane. Based on the numbers of features observed in LC-HRMS and on the responses of analytical standards representative of different metabolites groups (amino acids, sugars…), we discuss the influence of the nature, volume, and ratio of extraction solvents, the sample weight, and the reconstitution solvent. Moreover, the analytical conditions (LC columns, pH and additive of mobile phases and ionization modes) were also optimized so as to ensure the maximum metabolome coverages. Thus, two complementary chromatographic procedures were combined in order to cover a broader range of metabolites: a reversed phase separation (RPLC) on a C18 column followed by detection with positive ionization mode (ESI+) and a hydrophilic interaction chromatography (HILIC) on a zwitterionic column followed by detection with negative ionization mode (ESI-). This work provides information on brain, gills, liver, vs the whole body contribution to the stickleback metabolome. These information would help to guide ecotoxicological and biomonitoring studies.

Nontargeted Analysis Study Reporting Tool: A Framework to Improve Research Transparency and Reproducibility

Non-targeted analysis (NTA) workflows using mass spectrometry are gaining popularity in many disciplines, but universally accepted reporting standards are nonexistent. Current guidance addresses limited elements of NTA reporting-most notably, identification confidence-and is insufficient to ensure scientific transparency and reproducibility given the complexity of these methods. This lack of reporting standards hinders researchers' development of thorough study protocols and reviewers' ability to efficiently assess grant and manuscript submissions. To overcome these challenges, we developed the NTA Study Reporting Tool (SRT), an easy-to-use, interdisciplinary framework for comprehensive NTA methods and results reporting. Eleven NTA practitioners reviewed eight published articles covering environmental, food, and health-based exposomic applications with the SRT. Overall, our analysis demonstrated that the SRT provides a valid structure to guide study design and manuscript writing, as well as to evaluate NTA reporting quality. Scores self-assigned by authors fell within the range of peer-reviewer scores, indicating that SRT use for self-evaluation will strengthen reporting practices. The results also highlighted NTA reporting areas that need immediate improvement, such as analytical sequence and quality assurance/quality control information. Although scores intentionally do not correspond to data/results quality, widespread implementation of the SRT could improve study design and standardize reporting practices, ultimately leading to broader use and acceptance of NTA data.

Recent Developments Toward Integrated Metabolomics Technologies (UHPLC-MS-SPE-NMR and MicroED) for Higher-Throughput Confident Metabolite Identifications

Metabolomics has emerged as a powerful discipline to study complex biological systems from a small molecule perspective. The success of metabolomics hinges upon reliable annotations of spectral features obtained from MS and/or NMR. In spite of tremendous progress with regards to analytical instrumentation and computational tools, < 20% of spectral features are confidently identified in most untargeted metabolomics experiments. This article explores the integration of multiple analytical instruments such as UHPLC-MS/MS-SPE-NMR and the cryo-EM method MicroED to achieve large-scale and confident metabolite identifications in a higher-throughput manner. UHPLC-MS/MS-SPE allows for the simultaneous automated purification of metabolites followed by offline structure elucidation and structure validation by NMR and MicroED. Large-scale study of complex metabolomes such as that of the model plant legume Medicago truncatula can be achieved using an integrated UHPLC-MS/MS-SPE-NMR metabolomics platform. Additionally, recent developments in MicroED to study structures of small organic molecules have enabled faster, easier and precise structure determinations of metabolites. A MicroED small molecule structure elucidation workflow (e.g., crystal screening, sample preparation, data collection and data processing/structure determination) has been described. Ongoing MicroED methods development and its future scope related to structure elucidation of specialized metabolites and metabolomics are highlighted. The incorporation of MicroED with a UHPLC-MS/MS-SPE-NMR instrumental ensemble offers the potential to accelerate and achieve higher rates of metabolite identification.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Measurement(s)	chemical • drinking water
Technology Type(s)	high resolution mass spectrometry • non-target analysis • Interlaboratory
Factor Type(s)	method
Sample Characteristic - Environment	laboratory environment

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665

Recent advances in extraction technologies for recovery of bioactive compounds derived from fruit and vegetable waste peels: A review

Fruits and vegetables are the most important commodities of trade value among horticultural produce. They are utilized as raw or processed, owing to the presence of health-promoting components. Significant quantities of waste are produced during fruits and vegetables processing that are majorly accounted by waste peels (∼90-92%). These wastes, however, are usually exceptionally abundant in bioactive molecules. Retrieving these valuable compounds is a core objective for the valorization of waste peel, besides making them a prevailing source of beneficial additives in food and pharmaceutical industry. The current review is focused on extraction of bioactive compounds derived from fruit and vegetable waste peels and highlights the supreme attractive conventional and non-conventional extraction techniques, such as microwave-assisted, ultrasound assisted, pulsed electric fields, pulsed ohmic heating, pressurized liquid extraction, supercritical fluid extraction, pressurized hot water, high hydrostatic pressure, dielectric barrier discharge plasma extraction, enzyme-assisted extraction and the application of "green" solvents say as well as their synergistic effects that have been applied to recover bioactive from waste peels. Superior yields achieved with non-conventional technologies were identified to be of chief interest, considering direct positive economic consequences. This review also emphasizes leveraging efficient, modern extraction technologies for valorizing abundantly available low-cost waste peel, to achieve economical substitutes, whilst safeguarding the environment and building a circular economy. It is supposed that the findings discussed though this review might be a valuable tool for fruit and vegetable processing industry to imply an economical and effectual sustainable extraction methods, converting waste peel by-product to a high added value functional product.

Current Applications of Magnetic Nanomaterials for Extraction of Mycotoxins, Pesticides, and Pharmaceuticals in Food Commodities

Environmental pollutants, such as mycotoxins, pesticides, and pharmaceuticals, are a group of contaminates that occur naturally, while others are produced from anthropogenic sources. With increased research on the adverse ecological and human health effects of these pollutants, there is an increasing need to regularly monitor their levels in food and the environment in order to ensure food safety and public health. The application of magnetic nanomaterials in the analyses of these pollutants could be promising and offers numerous advantages relative to conventional techniques. Due to their ability for the selective adsorption, and ease of separation as a result of magnetic susceptibility, surface modification, stability, cost-effectiveness, availability, and biodegradability, these unique magnetic nanomaterials exhibit great achievement in the improvement of the extraction of different analytes in food. On the other hand, conventional methods involve longer extraction procedures and utilize large quantities of environmentally unfriendly organic solvents. This review centers its attention on current applications of magnetic nanomaterials and their modifications in the extraction of pollutants in food commodities.

Statistics of the Popularity of Chemical Compounds in Relation to the Non-Target Analysis

The idea of popularity/abundance of chemical compounds is widely used in non-target chemical analysis involving environmental studies. To have a clear quantitative basis for this idea, frequency distributions of chemical compounds over indicators of their popularity/abundance are obtained and discussed. Popularity indicators are the number of information sources, the number of chemical vendors, counts of data records, and other variables assessed from two large databases, namely ChemSpider and PubChem. Distributions are approximated by power functions, special cases of Zipf distributions, which are characteristic of the results of human/social activity. Relatively small group of the most popular compounds has been denoted, conventionally accounting for a few percent (several million) of compounds. These compounds are most often explored in scientific research and are practically used. Accordingly, popular compounds have been taken into account as first analyte candidates for identification in non-target analysis.

JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics

Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.

Identification of unknowns in industrial wastewater using offline 2D chromatography and non-target screening

Industrial wastewater is characterised by a complex composition of trace organic compounds (TrOC) in a difficult matrix. The identification of unknown pollutants is of high interest. On the one hand to ensure protection of the environment by elucidating contaminations and on the other hand to protect the biological treatment step in the wastewater treatment plant (WWTP). Due to the high variability of the matrix, the identification of compounds of interest is very time consuming and often unsuccessful. To overcome this limitation, a prioritisation method was developed to identify so called 'known unknowns', i.e. compounds frequently detected but not identified, as prioritised compounds in industrial wastewater. The method based on an offline two-dimensional (offline 2D) liquid chromatography (LC) approach with ultra violet (UV) detection in the first and high-resolution mass spectrometry (HRMS) in the second dimension. As a proof of concept, an identification process of one 'known unknown' is described. The compound was identified as a dichlorodinitrophenol isomer by retention time in two dimensions, UV spectrum, exact mass, mass fragmentation and ¹H- NMR. As prioritisation method, the offline 2D LC in combination with non-target analysis provides a powerful workflow to determine tentative structures of unknown organic compounds in industrial wastewater.

Omics Forecasting: Predictive Calculations Permit the Rapid Interpretation of High-Resolution Mass Spectral Data from Complex Mixtures

For some complex mixtures, chromatographic techniques are insufficient to separate the large numbers of compounds present. In addition, these mixtures often contain compounds with similar or identical molecular masses and shared fragmentation transitions. Advancements in mass spectrometry have provided more and more detailed molecular profiles with significant increases in resolution. This has led to a capacity to distinguish a very large number of compounds in complex mixtures, providing overwhelming data sets. The approach of calculating molecular formulas from a mass list has become more and more problematic as the number of signals has increased exponentially, to the point that it has become impossible to manually interpret the thousands of mass signals. The current approach is to calculate a list of possible formulas that fall within a specific mass error of the observed signal. Then, one must look for possible structures that can be derived from each entry on the list of formulas. However, an alternative approach is to anticipate the possible structures of a particular set of compounds, such as red wine pigments, and then compare the ion signals against a predicted list. To that end, starting with known wine pigment types, we have generated a set of expected wine pigment variants based on known derivatives of condensed tannin oligomers, anthocyanins, and fermentation products. After the ability to distinguish compounds by mass spectrometry was accounted for, over 1 million results were generated consisting of known and anticipated wine pigments. A comparison with a small sample of wine phenolic fractions show a large number of matches, suggesting that this approach may be helpful.

How do cancer-sniffing dogs sort biological samples? Exploring case-control samples with non-targeted LC-Orbitrap, GC-MS, and immunochemistry methods

Early identification of disease onset is regarded as an important factor for successful medical intervention. However, cancer and other long-term latency diseases are rare and may take years to manifest clinically. As such, there are no gold standards with which to immediately validate proposed preclinical screening methodologies. There is evidence that dogs can sort samples reproducibly into yes/no categories based on case-control training, but the basis of their decisions is unknown. Because dogs are sniffing air, the distinguishing chemicals must be either in the gas-phase or attached to aerosols and/or airborne particles. Recent biomonitoring research has shown how to extract and analyze semi- and non-volatile compounds from human breath in exhaled condensates and aerosols. Further research has shown that exhaled aerosols can be directly collected on standard hospital-style olefin polypropylene masks and that these masks can be used as a simple sampling scheme for canine screening. In this article, detailed liquid chromatography-high resolution mass spectrometry (LC-HR-MS) with Orbitrap instrumentation and gas chromatography-mass spectrometry (GC-MS) analyses were performed on two sets of masks sorted by consensus of a four-dog cohort as either cancer or control. Specifically, after sorting by the dogs, sample masks were cut into multiple sections and extracted for LC-MS and GC-MS non-targeted analyses. Extracts were also analyzed for human cytokines, confirming the presence of human aerosol content above levels in blank masks. In preliminary evaluations, 345 and 44 high quality chemical features were detected by LC-MS and GC-MS analyses, respectively. These features were used to develop provisional orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models to determine if the samples classified as cancer (case) or non-cancer (control) by the dogs could be separated into the same groups using analytical instrumentation. While the OPLS-DA model for the LC-HR-MS data was able to separate the two groups with statistical significance, although weak explanatory power, the GC-MS model was not found to be significant. These results suggest that the dogs may rely on the less volatile compounds from breath aerosol that were analyzed by LC-HR-MS than the more volatile compounds observed by GC-MS to sort mask samples into groups. These results provide justification for more expansive studies in the future that aim to characterize specific chemical features, and the role(s) of these features in maintaining homeostatic biological processes.

Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment

High resolution mass spectrometry (HRMS) analyses provide expansive chemical characterizations of environmental samples. To date, most research efforts have developed tools to expedite labor- and time-intensive contaminant identification efforts. However, even without chemical identity, the richness of nontarget HRMS data sets represents a significant opportunity to chemically differentiate samples and delineate source contributions. To develop this potential, we evaluated the use of unidentified HRMS detections to define sample uniqueness and provide additional statistical resolution for quantitative source apportionment, overcoming a critical limitation of existing approaches based on targeted contaminants. By creating a series of sample mixtures that mimic pollution sources in a representative watershed, we assessed the fidelity of HRMS source fingerprints during dilution and mixing. This approach isolated 8-447 nontarget compounds per sample for source apportionment and yielded accurate source concentration estimates (between 0.82 and 1.4-fold of actual values), even in multisource systems with <1% source contributions. Furthermore, we mined the nontarget data to identify five source-specific chemical end-members amenable to apportionment. While additional development studies are needed to fully evaluate the myriad factors affecting method accuracy and capabilities, this study provides a conceptual foundation for novel applications of nontarget HRMS data to confidently distinguish and quantify source impacts in complex systems.

Compositional space: A guide for environmental chemists on the identification of persistent and bioaccumulative organics using mass spectrometry

Since 2001, twenty-eight halogenated groups of persistent organic pollutants (POPs) have been banned or restricted by the Stockholm Convention. Identifying new POPs among the hundreds of thousands of anthropogenic chemicals is a major challenge that is increasingly being met by state-of-the-art mass spectrometry (MS). The first step to identification of a contaminant molecule (M) is the determination of the type and number of its constituent elements, viz. its elemental composition, from mass-to-charge (m/z) measurements and ratios of isotopic peaks (M + 1, M + 2 etc.). Not every combination of elements is possible. Boundaries exist in compositional space that divides feasible and improbable compositions as well as different chemical classes. This study explores the compositional space boundaries of persistent and bioaccumulative organics. A set of ~305,134 compounds (PubChem) was used to visualize the compositional space occupied by F, Cl, and Br compounds, as defined by m/z and isotope ratios. Persistent bioaccumulative organics, identified by in silico screening of 22,049 commercial chemicals, reside in more constrained regions characterized by a higher degree of halogenation. In contrast, boundaries surrounding non-halogenated chemicals could not be defined. Finally, a script tool (R code) was developed to select potential POPs from high resolution MS data. When applied to household dust (SRM 2585), this approach resulted in the discovery of previously unknown chlorofluoro flame retardants.

Expanding phytoremediation to the realms of known and unknown organic chemicals of concern

Recent advancements in analytical chemistry and data analyses via high-resolution mass spectrometry (HRMS) are evolving scientific understanding of the potential totality of organic chemical exposure and pollutant risk. This review addresses the importance of HRMS approaches, namely suspect screening and nontarget chemical analyses, to the realm of phytoremediation. These analytical approaches are not without caveats and constraints, but they provide an opportunity to understand in greater totality how plant-based technologies contribute, mitigate, and reduce organic chemical exposure across scales of experimental and system-level studies. These analytical tools can enlighten the complexity and efficacy of plant-contaminant system design and expand our understanding of biogenic and anthropogenic chemicals at work in phytoremediation systems. Advances in data analytics from biological sciences, such as metabolomics, are crucial to HRMS analysis. This review provides an overview of targeted, suspect screening, and nontarget HRMS approaches, summarizes the expanding knowledge of regulated and unregulated organic chemicals in the environment, addresses requisite HRMS instrumentation, analysis cost, uncertainty, and data processing techniques, and offers potential bridges of HRMS analyses to phytoremediation research and application.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

Non-targeted GC/MS analysis of exhaled breath samples: Exploring human biomarkers of exogenous exposure and endogenous response from professional firefighting activity

A non-targeted analysis workflow was applied to analyze exhaled breath samples collected from firefighters pre- and post-structural fire suppression. Breath samples from firefighters functioning in attack and search positions were examined for target and non-target compounds in automated thermal desorption-GC/MS (ATD-GC/MS) selected ion monitoring (SIM)/scan mode and reviewed for prominent chemicals. Targeted chemicals included products of combustion such as benzene, toluene, xylenes, and polycyclic aromatic hydrocarbons (PAH) that serve as a standard assessment of exposure. Sixty unique chemical features representative of exogenous chemicals and endogenous compounds, including single-ring aromatics, polynuclear aromatic hydrocarbons, volatile sulfur-containing compounds, aldehydes, alkanes, and alkenes were identified using the non-targeted analysis workflow. Fifty-seven out of 60 non-targeted features changed by at least 50% from pre- to post-fire suppression activity in at least one subject, and 7 non-targeted features were found to exhibit significantly increased or decreased concentrations for all subjects as a group. This study is important for (1) alerting the firefighter community to potential new exposures, (2) expanding the current targeted list of toxicants, and (3) finding biomarkers of response to firefighting activity as reflected by changes in endogenous compounds. Data demonstrate that there are non-targeted compounds in firefighters' breath that are indicative of environmental exposure despite the use of protective gear, and this information may be further utilized to improve the effectiveness of personal protective equipment.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.