Comparison of Software Tools for Liquid Chromatography–High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples

Prioritization of Unknown LC-HRMS Features Based on Predicted Toxicity Categories

Complex environmental samples contain a diverse array of known and unknown constituents. While liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) nontargeted analysis (NTA) has emerged as an essential tool for the comprehensive study of such samples, the identification of individual constituents remains a significant challenge, primarily due to the vast number of detected features in each sample. To address this, prioritization strategies are frequently employed to narrow the focus to the most relevant features for further analysis. In this study, we developed a novel prioritization strategy that directly links fragmentation and chromatographic data to aquatic toxicity categories, bypassing the need for identification of individual compounds. Given that features are not always well-characterized through fragmentation, we created two models: (1) a Random Forest Classification (RFC) model, which classifies fish toxicity categories based on MS1, retention, and fragmentation data─expressed as cumulative neutral losses (CNLs)─when fragmentation information is available, and (2) a Kernel Density Estimation (KDE) model that relies solely on retention time and MS1 data when fragmentation is absent. Both models demonstrated accuracy comparable to that of structure-based prediction methods. We further tested the models on a pesticide mixture in a tea extract measured by LC-HRMS, where the CNL-based RFC model achieved 0.76 accuracy and the KDE model reached 0.61, showcasing their robust performance in real-world applications.

Automated Integration and Quality Assessment of Chromatographic Peaks in LC-MS-Based Metabolomics and Lipidomics Using TARDIS

In recent years, liquid chromatography coupled to mass spectrometry (LC-MS) has emerged as the main technology to measure the whole of small molecules (the metabolome) in a diversity of matrices. Within the field of computational metabolomics, significant efforts have been made in the development of tools to (pre)process untargeted LC-MS data. However, tools that circumvent the time-consuming, manual preprocessing of targeted LC-MS data with vendor-specific software remain sparse. We therefore present TARDIS, an open-source R package for the analysis of targeted LC-MS metabolomics and lipidomics data. Both established (area under the curve, maximum intensity and points over the peak) and recently developed (custom signal-to-noise ratio and bell-curve similarity) quality metrics were included to offer increased efficiency of peak quality evaluation. The robustness of TARDIS' peak integration was demonstrated through a quantitative comparison to state-of-the-art vendor software. To this end, applicability at a large scale (n = 1786) was validated across three distinct biofluids (stool, saliva and urine) and two LC-MS instruments, using data from the FAME, ENVIRONAGE, and FGFP cohort studies. In conclusion, TARDIS offers a robust and scalable open-source solution for the targeted analysis of LC-MS metabolomics and lipidomics data. TARDIS and its source code are freely available at https://github.com/UGent-LIMET/TARDIS.

Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications

Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targeted screening methods often fail to detect these compounds, making non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) essential for identifying unknown or suspected contaminants. However, interpreting the vast datasets generated by HRMS is complex and requires advanced data processing techniques. Recent advancements in machine learning (ML) models offer great potential for enhancing NTA applications. As such, we reviewed key developments, including optimizing workflows using computational tools, improved chemical structure identification, advanced quantification methods, and enhanced toxicity prediction capabilities. It also discusses challenges and future perspectives in the field, such as refining ML tools for complex mixtures, improving inter-laboratory validation, and further integrating computational models into environmental risk assessment frameworks. By addressing these challenges, ML-assisted NTA can significantly enhance the detection, quantification, and evaluation of EECs, ultimately contributing to more effective environmental monitoring and public health protection.

Community Needs and Proposed Solutions for a Broadly Applicable Standard/QC Mixture for High-Resolution Mass Spectrometry-Based Non-Targeted Analysis

Non-targeted analysis (NTA) using high-resolution mass spectrometry (HRMS) is a global chemical screening approach that generates information-rich data which can be used to detect and identify unknown chemicals. NTA is a powerful approach which is increasingly being used for a variety of sample types, research fields, and goals. However, there are challenges associated with accurate assessments of data quality and method performance, comparability across laboratories/instruments/methods, and communication of results/confidence. A standard mixture containing a sufficient number and diversity of chemicals would help address these needs, but is not yet commercially available. Thus, we conducted a survey of 146 NTA researchers to examine desired requirements for the broad fields, studies, and goals where NTA can be applied. We also compare this feedback to previously published in-house standard mixtures, which, we argue, are models for a standard that can be adjusted to fit the NTA community's needs and possibly commercialized. Reversed-phase liquid chromatography HRMS is one of the most common methods used for NTA; therefore, this survey is focused on characteristics necessary for these types of methods. We intend this information to communicate the need for an interdisciplinary NTA standard mixture, the importance of implementing standards, and to lower the barriers for chemical vendor standard mixture development and distribution.

Communicating with Stakeholders to Identify High-Impact Research Directions for Non-Targeted Analysis

Non-targeted analysis (NTA) using high-resolution mass spectrometry without defined chemical targets has the potential to expand and improve chemical monitoring in many fields. Despite rapid advancements within the research community, NTA methods and data remain underutilized by many potential beneficiaries. To better understand barriers toward widespread adoption, the Best Practices for Non-Targeted Analysis (BP4NTA) working group conducted focus group meetings and follow-up surveys with scientists (n = 61) from various sectors (e.g., drinking water utilities, epidemiologists, n = 9) where NTA is expected to provide future value. Meeting participants included producers and end-users of NTA data with a wide range of familiarity with NTA methods and outputs. Discussions focused on identifying specific barriers that limit adoption and on setting NTA product development priorities. Stated priorities fell into four major categories: 1) education and training materials; 2) QA/QC frameworks and study design guidance; 3) accessible compound databases and libraries; and 4) NTA data linkages with chemical fate and toxicity information. Based on participant feedback, this manuscript proposes research directions, such as standardization of training materials, that BP4NTA and other institutions can pursue to expand NTA use in various application scenarios and decision contexts.

Signal Processing Workflow for Suspect Screening in LC × LC-HRMS: Efficient Extraction of Pure Mass Spectra for Identification of Suspects in Complex Samples Using a Mass Filtering Algorithm

The data processing workflows for comprehensive two-dimensional liquid chromatography (LC × LC) hyphenated to high-resolution mass spectrometry (HRMS) operated in data-independent acquisition (DIA) are limited compared to their one-dimensional counterparts. A two-step workflow is proposed to extract pure mass spectra from LC × LC-HRMS. First, a mass filtering (MF) algorithm groups ions belonging to the same compound based on their elution profile similarity in the first (1D) and second dimension (2D). Second, the filtered data are deconvoluted using multivariate curve resolution (MCR) to address potential coelution. The presented workflow is termed MF + MCR and was tested on pulsed elution-LC × LC-HRMS data from a wastewater effluent extract. The proposed workflow was benchmarked to the following three data processing strategies for mass spectra extraction: peak apex (PAM), using the MF approach alone, or using MCR without prior MF. The MF + MCR workflow identified 25 suspect compounds, compared to 23, 16, and 10 identified by MF, MCR, and PAM, respectively. The nine suspects that could not be identified using MCR compared to the MF + MCR all had low total signal contributions, i.e., low intensities compared to the TIC. This showed that adequate preprocessing prior to MCR is essential for trace level analysis. Additionally, it was shown that the MF + MCR workflow extracted statistically significantly purer mass spectra compared to PAM (p-value: 0.003) and MCR (p-value: 0.04) from a spiked blank sample. The results highlight that by utilizing the elution profiles in both chromatographic dimensions, clean mass spectra of analytes at trace levels measured in DIA can be extracted, allowing for more reliable compound identification compared to the workflows that were used for benchmarking.

Comprehensive screening and analysis of pharmaceuticals and pharmaceutically active chemicals in wastewater: health and environmental hazards and removal efficiency of wastewater treatment plant in Malaysia

Wastewater treatment plant (WWTP) is a sustainable technique for making wastewater reusable for non-potable purposes. However, in developing countries, most conventional WWTPs are not equipped to trap all pharmaceutical residues (PRs) and pharmaceutically active chemicals (PhACs). This study aims to perform non-target screening of these contaminants in wastewater and explore health and environmental hazards and the removal efficiency of a WWTP in Malaysia. At Indah Water WWTP, a total of 28 wastewater samples (i.e., 2 L each of 14 influent and 14 effluent) were collected every day for a week from February to April 2023. The supernatant of the centrifuged sample was analyzed with the LCMS-QTOF system. Chromatographic profiles were analyzed, and the compounds were annotated using the METLIN database. Categorical data were statistically analyzed with SPSS 29.0 using a chi-square test and continuous variables using paired t-test and multiple regression. PRs like micronutrient (9, 2.3%) and PhACs like lipid (83, 20.8%) were more frequent. Detection frequencies of PRs and PhACs were 72 (18%) and 328 (82%), respectively. Efficiency of WWTP was 36.4 to 100% for PRs removal (mean ± SD: 65.85 ± 56.43%) and 20 to 100% for PhACs removal (mean ± SD: 49.30 ± 55.94%). A total of 943 (mean ± SD: 67.36 ± 43.28) and 400 (mean ± SD: 28.57 ± 32.44) unique PRs and PhACs were recorded. A total of 40 (10%) PRs and PhACs had the potential to irritate eyes, skin, and respiratory tract, and 46 (11.5%) chemicals needed to be avoided from being discharged into the environment. Though WWTP was 98.0% compliant with environmental standards, its efficiency should still be increased to remove the full range of PRs and PhACs. The research has implications for SDGs 6 and 14.

An integrated approach to evaluating water contaminants and evaporation in agricultural water distribution systems

This study presents an innovative approach for assessing water quality in agricultural irrigation networks, integrating stable isotope analysis, in vivo zebrafish screening, and comprehensive chemical profiling to investigate the occurrence, transformation, and potential toxicity of organic contaminants. Stable isotope analysis was used to measure evaporation as a proxy for water residence time in the canal, while liquid chromatography-high resolution mass spectrometry (LC-HRMS) identified a range of organic compounds in water samples collected from both the irrigation canal and its source river. Results indicated a reduction in contaminant levels in the canal compared to the river, with the most significant evaporation and concentration changes occurring at a holding reservoir, suggesting that managing residence time could help reduce water loss in arid irrigation networks. The data also highlighted how evaporation, particularly during the dry, hot season, influences contaminant dynamics. Hierarchical clustering of LC-HRMS results showed notable differences between the chemical profiles of canal and river samples, indicating that irrigation systems may contribute to the degradation or removal of certain compounds. Over 60 % of detected compounds were naturally derived, with anthropogenic contaminants like pesticides and personal care products further highlighting human impacts. Priority contaminants, including DEET and 2-naphthalene sulfonic acid, likely originated from urban activities upstream. Initial screening using zebrafish embryos showed bioactivity across sites, confirming the presence of contaminants needing further examination. Correlation analysis linked natural compounds to evaporation rates, suggesting that flora and fauna play significant roles in the chemical makeup of canal water. Overall, this approach provides a comprehensive framework for monitoring irrigation water, offering insights into contaminant behavior and supporting the development of standardized methods for assessing chemical fate and ecological risks in agricultural irrigation systems.

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.

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.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Effect of calcium and phosphorus on ammonium and nitrate nitrogen adsorption onto iron (hydr)oxides surfaces: CD-MUSIC model and DFT computation

Calcium (Ca2+) and phosphorous (PO43-) significantly influence the form and effectiveness of nitrogen (N), however, the precise mechanisms governing the adsorption of ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) are still lacking. This study employed batch adsorption experiments, charge distribution and multi-site complexation (CD-MUSIC) models and density functional theory (DFT) calculations to elucidate the mechanism by which Ca2+ and PO43- affect the adsorption of NH4+-N and NO3--N on the goethite (GT) surface. The results showed that the adsorption of NH4+-N on the GT exhibited an initial increase followed by a decrease as pH increased, peaking at a pH of 8.5. Conversely, the adsorption of NO3--N decreased with rising pH. According to the CD-MUSIC model, Ca2+ minimally affected the NH4+-N adsorption on the GT but enhanced NO3--N adsorption via electrostatic interaction, promoting the adsorption of ≡FeOH-NO3- and ≡Fe3O-NO3- species. Similarly, PO43- inhibited the adsorption of ≡FeOH-NO3- and ≡Fe3O-NO3- species. However, PO43- boosted NH4+-N adsorption by facilitating the formation of ≡Fe3O-NH4+ via electrostatic interaction and site competition. DFT calculations indicates that although bidentate phosphate (BP) was beneficial to stabilize NH4+-N than monodentate phosphate (SP), SP-NH4+ was the main adsorption configuration at pH 5.5-9.5 owing the prevalence of SP on the GT surface under site competition of NH4+-N. The results of CD-MUSIC model and DFT calculation were verified mutually, and provide novel insights into the mechanisms underlying N fixation and migration in soil.

qPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data

We present qPeaks (quality peaks), a novel, user-parameter-free algorithm for peak detection and peak characterization applicable to chromatographic data. The algorithm is based on a linearizable regression model that analyzes asymmetric peaks and estimates the specific uncertainties associated with the peak regression parameters. The uncertainties of the parameters are used to derive a data quality score DQSpeak, rendering low reliability results more transparent during processing and allowing for the prioritization of generated features. High DQSpeak chromatographic peaks have a lower chance of being classified as false-positive and show higher repeatability over multiple measurements. The high efficiency of the algorithm makes it particularly useful for application within processing routines of nontarget screening through chromatography coupled with high-resolution mass spectrometry. qPeaks is integrated into the qAlgorithms nontarget screening processing toolbox and appends a parameter-free chromatographic peak detection and characterization step to it. With qAlgorithms, now high-resolution mass spectra are centroided using the qCentroids algorithms, centroids are clustered to form extracted ion chromatograms (EICs) with the qBinning algorithm, and chromatographic peaks are found on the generated EICs with qPeaks. However, all tools from qAlgorithms can also be used independently.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

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.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

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.

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

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

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.

OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria

We introduce OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbitrap instruments, as well as the concepts of chemical consistency and fragments networks. OrbiFragsNets takes advantage of the specific confidence interval for each peak in every MS2 spectrum, which is an unclear idea across the high-resolution mass spectrometry literature. The spectrum annotations are expressed as fragments networks, a set of networks with the possible combinations of annotations for the fragments. The model behind OrbiFragsNets is briefly described here and explained in detail in the constantly updated manual available in the GitHub repository. This new approach in MS2 spectrum de novo automatic annotation proved to perform as good as well established tools such as RMassBank and SIRIUS.•A new approach on automatic annotation of Orbitrap MS2 spectra is introduced.•Possible spectrum annotation are described as independent consistent networks, with annotations for each fragment as nodes, and annotations for the mass difference between fragments as edges.•Annotation process is described as the selection of the most connected fragments network.

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

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.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

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.

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.

Antioxidant and Wound Healing Bioactive Potential of Extracts Obtained from Bark and Needles of Softwood Species

Interest in the extraction of phytochemical bioactive compounds, especially polyphenols from biomass, has recently increased due to their valuable biological potential as natural sources of antioxidants, which could be used in a wide range of applications, from foods and pharmaceuticals to green polymers and bio-based materials. The present research study aimed to provide a comprehensive chemical characterization of the phytochemical composition of forest biomass (bark and needles) of softwood species (Picea abies L., H. Karst., and Abies alba Mill.) and to investigate their in vitro antioxidant and antimicrobial activities to assess their potential in treating and healing infected chronic wounds. The DPPH radical-scavenging method and P-LD were used for a mechanistic explanation of the biomolecular effects of the investigated bioactive compounds. (+)-Catechin, epicatechin, rutin, myricetin, 4 hydroxybenzoic and p-cumaric acids, kaempherol, and apigenin were the main quantified polyphenols in coniferous biomass (in quantities around 100 µg/g). Also, numerous phenolic acids, flavonoids, stilbenes, terpenes, lignans, secoiridoids, and indanes with antioxidant, antimicrobial, anti-inflammatory, antihemolytic, and anti-carcinogenic potential were identified. The Abies alba needle extract was more toxic to microbial strains than the eukaryotic cells that provide its active wound healing principles. In this context, developing industrial upscaling strategies is imperative for the long-term success of biorefineries and incorporating them as part of a circular bio-economy.

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

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

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

Enhanced selectivity for acidic contaminants in drinking water: From suspect screening to toxicity prediction

A novel analytical workflow for suspect screening of organic acidic contaminants in drinking water is presented, featuring selective extraction by silica-based strong anion-exchange solid-phase extraction, mixed-mode liquid chromatography-high resolution accurate mass spectrometry (LC-HRMS), peak detection, feature reduction and compound identification. The novel use of an ammonium bicarbonate-based elution solvent extended strong anion-exchange solid-phase extraction applicability to LC-HRMS of strong acids. This approach performed with consistently higher recovery and repeatability (88 ± 7 % at 500 ng L^-1), improved selectivity and lower matrix interference (mean = 12 %) over a generic mixed-mode weak anion exchange SPE method. In addition, a novel filter for reducing full-scan features from fulvic and humic acids was successfully introduced, reducing workload and potential for false positives. The workflow was then applied to 10 London municipal drinking water samples, revealing the presence of 22 confirmed and 37 tentatively identified substances. Several poorly investigated and potentially harmful compounds were found which included halogenated hydroxy-cyclopentene-diones and dibromomethanesulfonic acid. Some of these compounds have been reported as mutagenic in test systems and thus their presence here requires further investigation. Overall, this approach demonstrated that employing selective extraction improved detection and helped shortlist suspects and potentially toxic chemical contaminants with higher confidence.

Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky-Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC-MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.

Discrimination of Olive Oil and Extra-Virgin Olive Oil from Other Vegetable Oils by Targeted and Untargeted HRMS Profiling of Phenolic and Triterpenic Compounds Combined with Chemometrics

Extra-virgin olive oil (EVOO) and virgin olive oil (VOO) are valuable natural products of great economic interest for their producing countries, and therefore, it is necessary to establish methods capable of proving the authenticity of these oils on the market. This work presents a methodology for the discrimination of olive oil and extra-virgin olive oil from other vegetable oils based on targeted and untargeted high-resolution mass spectrometry (HRMS) profiling of phenolic and triterpenic compounds coupled with multivariate statistical analysis of the data. Some phenolic compounds (cinnamic acid, coumaric acids, apigenin, pinocembrin, hydroxytyrosol and maslinic acid), secoiridoids (elenolic acid, ligstroside and oleocanthal) and lignans (pinoresinol and hydroxy and acetoxy derivatives) could be olive oil biomarkers, whereby these compounds are quantified in higher amounts in EVOO compared to other vegetable oils. The principal component analysis (PCA) performed based on the targeted compounds from the oil samples confirmed that cinnamic acid, coumaric acids, apigenin, pinocembrin, hydroxytyrosol and maslinic acid could be considered as tracers for olive oils authentication. The heat map profiles based on the untargeted HRMS data indicate a clear discrimination of the olive oils from the other vegetable oils. The proposed methodology could be extended to the authentication and classification of EVOOs depending on the variety, geographical origin, or adulteration practices.

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.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS¹ and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS¹ spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS¹ and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

How Low Can You Go? Selecting Intensity Thresholds for Untargeted Metabolomics Data Preprocessing

Untargeted mass spectrometry (MS) metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data preprocessing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features, but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresholds (1) improved the limit of detection, (2) increased the number of features detected with an associated isotope pattern and/or an MS-MS fragmentation spectrum, and (3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low- and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in the cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.

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.

Bioactive Phytochemical Composition of Grape Pomace Resulted from Different White and Red Grape Cultivars

Grapes are rich in phenolic compounds, being important for human health with anti-inflammatory, antiatherosclerotic, antimutagenic, anticarcinogenic, antibacterial, antiviral, and antimicrobial activity. The winemaking of the grapes generates significant amounts of waste. These wastes contain bioactive compounds in their biomass that can be used as a source of food improvement or as a source of nutrition supplementation. This study looks at the content of bioactive compounds, the polyphenolic profile, and the antioxidant activity in different white and red grape pomaces. The investigation of bioactive characteristics (total polyphenols, total flavonoids, catechins, tannins, and antioxidant activity) was carried out by UV-Vis spectrophotometric methods, while the individual polyphenolic composition was investigated by target and screening UHPLC-HRMS/MS analysis. Principal components (PCA) and the heat maps analysis allows the discrimination between the grape pomace resulted from white grape cultivars (Muscat Ottonel and Tamaioasa Romaneasca) and red grape pomaces (Cabernet Sauvignon, Merlot, Feteasca Neagra, Burgund Mare, Pinot Nore), with the identification of the specific phenolic compounds for each grape pomace type.

Eco-Friendly Solution Based on Rosmarinus officinalis Hydro-Alcoholic Extract to Prevent Biodeterioration of Cultural Heritage Objects and Buildings

Biodeterioration of cultural heritage is caused by different organisms capable of inducing complex alteration processes. The present study aimed to evaluate the efficiency of Rosmarinus officinalis hydro-alcoholic extract to inhibit the growth of deteriogenic microbial strains. For this, the physico-chemical characterization of the vegetal extract by UHPLC–MS/MS, its antimicrobial and antibiofilm activity on a representative number of biodeteriogenic microbial strains, as well as the antioxidant activity determined by DPPH, CUPRAC, FRAP, TEAC methods, were performed. The extract had a total phenol content of 15.62 ± 0.97 mg GAE/mL of which approximately 8.53% were flavonoids. The polyphenolic profile included carnosic acid, carnosol, rosmarinic acid and hesperidin as major components. The extract exhibited good and wide spectrum antimicrobial activity, with low MIC (minimal inhibitory concentration) values against fungal strains such as Aspergillus clavatus (MIC = 1.2 mg/mL) and bacterial strains such as Arthrobacter globiformis (MIC = 0.78 mg/mL) or Bacillus cereus (MIC = 1.56 mg/mL). The rosemary extract inhibited the adherence capacity to the inert substrate of Penicillium chrysogenum strains isolated from wooden objects or textiles and B. thuringiensis strains. A potential mechanism of R. officinalis antimicrobial activity could be represented by the release of nitric oxide (NO), a universal signalling molecule for stress management. Moreover, the treatment of microbial cultures with subinhibitory concentrations has modulated the production of microbial enzymes and organic acids involved in biodeterioration, with the effect depending on the studied microbial strain, isolation source and the tested soluble factor. This paper reports for the first time the potential of R. officinalis hydro-alcoholic extract for the development of eco-friendly solutions dedicated to the conservation/safeguarding of tangible cultural heritage.

Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra

High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard’s msConvert. Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.

Power of mzRAPP-Based Performance Assessments in MS1-Based Nontargeted Feature Detection

When performing chromatography-mass spectrometry-based nontargeted metabolomics, or exposomics, one of the key steps in the analysis is to obtain MS1-based feature tables. Inapt parameter settings in feature detection will result in missing or wrong quantitative values and might ultimately lead to downstream incorrect biological interpretations. However, until recently, no strategies to assess the completeness and abundance accuracy of feature tables were available. Here, we show that mzRAPP enables the generation of benchmark peak lists by using an internal set of known molecules in the analyzed data set. Using the benchmark, the completeness and abundance accuracy of feature tables can be assessed in an automated pipeline. We demonstrate that our approach adds to other commonly applied quality assurance methods such as manual or automatized parameter optimization techniques or removal of false-positive signals. Moreover, we show that as few as 10 benchmark molecules can already allow for representative performance metrics to further improve quantitative biological understanding.

Nontarget Screening Exhibits a Seasonal Cycle of PM2.5 Organic Aerosol Composition in Beijing

The molecular composition of atmospheric particulate matter (PM) in the urban environment is complex, and it remains a challenge to identify its sources and formation pathways. Here, we report the seasonal variation of the molecular composition of organic aerosols (OA), based on 172 PM_2.5 filter samples collected in Beijing, China, from February 2018 to March 2019. We applied a hierarchical cluster analysis (HCA) on a large nontarget-screening data set and found a strong seasonal difference in the OA chemical composition. Molecular fingerprints of the major compound clusters exhibit a unique molecular pattern in the Van Krevelen-space. We found that summer OA in Beijing features a higher degree of oxidation and a higher proportion of organosulfates (OSs) in comparison to OA during wintertime, which exhibits a high contribution from (nitro-)aromatic compounds. OSs appeared with a high intensity in summer-haze conditions, indicating the importance of anthropogenic enhancement of secondary OA in summer Beijing. Furthermore, we quantified the contribution of the four main compound clusters to total OA using surrogate standards. With this approach, we are able to explain a small fraction of the OA (∼11-14%) monitored by the Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM). However, we observe a strong correlation between the sum of the quantified clusters and OA measured by the ToF-ACSM, indicating that the identified clusters represent the major variability of OA seasonal cycles. This study highlights the potential of using nontarget screening in combination with HCA for gaining a better understanding of the molecular composition and the origin of OA in the urban environment.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential ¹²C and ¹³C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.

Bromide strongly influences the formation of reaction products during the ozonation of diclofenac, metoprolol and isoproturon

Bromide as an omnipresent matrix component in wastewater can react with ozone to form hypobromous acid (HOBr). This secondary oxidant can subsequently react with micropollutants but also with formed intermediates. Therefore, bromide and especially HOBr can highly influence the formation of transformation products (TPs). This has already been reported for the ozonation of N,N-dimethylsulfamide leading to the formation of the cancerogenic N-nitrosodimethylamine only in bromide containing waters. In this study, the influence of different bromide and ozone concentrations on the formation of TPs during the ozonation of isoproturon (ISO), metoprolol (METO) and diclofenac (DCF) were investigated. Additionally, TPs were identified, which are formed in the direct reaction of the micropollutants with HOBr with and without subsequent ozonation. The results showed that even if the reactions of ozone with the substances should be favored bromide can highly influence the formation of TPs already at low concentrations. In summary, new TPs after the reaction with HOBr (and subsequent ozonation) could be postulated for ISO, METO and DCF. This underlines that the present water matrix can have a high influence on the formation of TPs and that these mechanisms need to be investigated further.

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

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

Phytochemical and Nutritional Profile Composition in Fruits of Different Sweet Chestnut (Castanea sativa Mill.) Cultivars Grown in Romania

Chestnut (Castanea sativa Mill.), a valuable fruit crop, is cultivated in small areas in Romania, mostly in the west, where the moderate continental climate has a slight Mediterranean influence. This work aims to investigate the bioactive characteristics (total polyphenols, total flavonoids and antioxidant activity), individual polyphenolic composition, phytochemical and nutritional HRMS screening profiles, sugar and mineral composition of six sweet chestnut cultivars, namely ‘Marsol’, ‘Maraval’, ‘Bournette’, ‘Précoce Migoule’ and ‘Marissard’ grown at Fruit Growing Research—Extension Station (SCDP) Vâlcea, in Northern Oltenia, Romania. Fruit samples were collected in two consecutive years, in order to study the impact of genetic variability between cultivars and the influence of the different climatic conditions corresponding to different cultivation years. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) allow the discrimination between the sweet chestnut fruits harvested in different years and different sweet chestnut cultivars. Analytical investigations revealed that sweet chestnut cultivars grown in Romania show similar bioactive, phytochemical and nutritional composition to cultivars grown in the large European chestnut-producing countries, indicating the high adaptation potential of the chestnut in the temperate continental zone with small Mediterranean influences characteristic of the southwestern area of Romania.

NP Analyst: An Open Online Platform for Compound Activity Mapping

Few tools exist in natural products discovery to integrate biological screening and untargeted mass spectrometry data at the library scale. Previously, we reported Compound Activity Mapping as a strategy for predicting compound bioactivity profiles directly from primary screening results on extract libraries. We now present NP Analyst, an open online platform for Compound Activity Mapping that accepts bioassay data of almost any type, and is compatible with mass spectrometry data from major instrument manufacturers via the mzML format. In addition, NP Analyst will accept processed mass spectrometry data from the MZmine 2 and GNPS open-source platforms, making it a versatile tool for integration with existing discovery workflows. We demonstrate the utility of this new tool for both the dereplication of known compounds and the discovery of novel bioactive natural products using a challenging low-resolution antimicrobial bioassay data set. This new platform is available at www.npanalyst.org.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.