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

Purschke, Kirsten; Zoell, Christian; Leonhardt, Juri; Weber, Markus; Schmidt, Torsten

doi:10.1016/j.scitotenv.2019.135835

Cited by 19 publications

(15 citation statements)

References 21 publications

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“…Details of the laboratory and data analysis practices are presented in the Supporting Information (SI 2). The LC–HRMS analysis method employed was based on Purschke et al (2020) . The detailed description of the LC–HRMS acquisition method can be found in the Supporting Information (see SI 2.3, Table S4).…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of Nontarget Long-Term LC–HRMS Time Series Data Using Multivariate Statistical Approaches

et al. 2020

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The use of liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS) has steadily increased in many application fields ranging from metabolomics to environmental science. HRMS data are frequently used for nontarget screening (NTS), i.e., the search for compounds that are not previously known and where no reference substances are available. However, the large quantity of data produced by NTS analytical workflows makes data interpretation and time-dependent monitoring of samples very sophisticated and necessitates exploiting chemometric data processing techniques. Consequently, in this study, a prioritization method to handle time series in nontarget data was established. As proof of concept, industrial wastewater was investigated. As routine industrial wastewater analyses monitor the occurrence of a limited number of targeted water contaminants, NTS provides the opportunity to detect also unknown trace organic compounds (TrOCs) that are not in the focus of routine target analysis. The developed prioritization method enables reducing raw data and including identification of prioritized unknown contaminants. To that end, a five-month time series for industrial wastewaters was utilized, analyzed by liquid chromatography–time-of-flight mass spectrometry (LC–qTOF-MS), and evaluated by NTS. Following peak detection, alignment, grouping, and blank subtraction, 3303 features were obtained of wastewater treatment plant (WWTP) influent samples. Subsequently, two complementary ways for exploratory time trend detection and feature prioritization are proposed. Therefore, following a prefiltering step, featurewise principal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wise) were used to annotate trends of relevant wastewater contaminants. With sparse factorization of data matrices using GPCA, groups of correlated features/mass fragments or adducts were detected, recovered, and prioritized. Similarities and differences in the chemical composition of wastewater samples were observed over time to reveal hidden factors accounting for the structure of the data. The detected features were reduced to 130 relevant time trends related to TrOCs for identification. Exemplarily, as proof of concept, one nontarget pollutant was identified as N-methylpyrrolidone. The developed chemometric strategies of this study are not only suitable for industrial wastewater but also could be efficiently employed for time trend exploration in other scientific fields.

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Section: Methodsmentioning

confidence: 99%

“…The LC−HRMS analysis method employed was based on Purschke et al (2020). 28 The detailed description of the LC−HRMS acquisition method can be found in the Supporting Information (see SI 2.3, Table S4). In brief, samples were diluted by a factor of 10 with Milli-Q and 0.1% formic acid and were spiked with 10 μL of ISTD (cf.…”

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Evaluation of Nontarget Long-Term LC–HRMS Time Series Data Using Multivariate Statistical Approaches

et al. 2020

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“…Purschke et al. developed a proof of concept method using two-dimensional LC with ultraviolet detection and HRMS to enhance untargeted analysis for tentative identification of unknown industrial wastewater contaminants . Blum et al conducted two-dimensional gas chromatography–mass spectrometry (GCxGC-MS) analysis of on-site sewage treatment facilities .…”

Section: Pharmaceuticals and Personal Care Productsmentioning

confidence: 99%

“…Purschke et al developed a proof of concept method using two-dimensional LC with ultraviolet detection and HRMS to enhance untargeted analysis for tentative identification of unknown industrial wastewater contaminants. 147 Blum et al conducted two-dimensional gas chromatography−mass spectrometry (GCxGC-MS) analysis of on-site sewage treatment facilities. 148 By using GCxGC, Blum et al was able to improve compound separation from matrix interference without having to conduct expensive and timeconsuming sample preparation.…”

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confidence: 99%

From Pesticides to Per- and Polyfluoroalkyl Substances: An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics

et al. 2020

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Environmental analysis of xenobiotics is a challenging yet necessary undertaking to characterize pollution levels, assess the effectiveness of remediation interventions, and prevent adverse environmental and health outcomes. Xenobiotics are concerning from an environmental perspective due to their chemical persistence, toxicity to humans and wildlife, and prolific use in agricultural and industrial applications. 1 Many xenobiotics are persistent organic pollutants (POPs), and the number of POPs listed in the Stockholm Convention is

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“…Some research simply uses MetFrag purely for its in silico fragmentation capabilities, i.e. not paired with any compound database [9,40,49]. Many examples use only the Frag-menterScore to rank candidates retrieved from Chem-Spider alone [3,31,35], PubChem alone [29,61,64], or a combination of either or both with other databases [8,25,45,47] like KEGG [22], FOR-IDENT [30] and MassBank [36].…”

Section: Introductionmentioning

confidence: 99%

Retrospective non-target analysis to support regulatory water monitoring: from masses of interest to recommendations via in silico workflows

Lai

Singh

Kovalova³

et al. 2021

Environ Sci Eur

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Background Applying non-target analysis (NTA) in regulatory environmental monitoring remains challenging—instead of having exploratory questions, regulators usually already have specific questions related to environmental protection aims. Additionally, data analysis can seem overwhelming because of the large data volumes and many steps required. This work aimed to establish an open in silico workflow to identify environmental chemical unknowns via retrospective NTA within the scope of a pre-existing Swiss environmental monitoring campaign focusing on industrial chemicals. The research question addressed immediate regulatory priorities: identify pollutants with industrial point sources occurring at the highest intensities over two time points. Samples from 22 wastewater treatment plants obtained in 2018 and measured using liquid chromatography–high resolution mass spectrometry were retrospectively analysed by (i) performing peak-picking to identify masses of interest; (ii) prescreening and quality-controlling spectra, and (iii) tentatively identifying priority “known unknown” pollutants by leveraging environmentally relevant chemical information provided by Swiss, Swedish, EU-wide, and American regulators. This regulator-supplied information was incorporated into MetFrag, an in silico identification tool replete with “post-relaunch” features used here. This study’s unique regulatory context posed challenges in data quality and volume that were directly addressed with the prescreening, quality control, and identification workflow developed. Results One confirmed and 21 tentative identifications were achieved, suggesting the presence of compounds as diverse as manufacturing reagents, adhesives, pesticides, and pharmaceuticals in the samples. More importantly, an in-depth interpretation of the results in the context of environmental regulation and actionable next steps are discussed. The prescreening and quality control workflow is openly accessible within the R package Shinyscreen, and adaptable to any (retrospective) analysis requiring automated quality control of mass spectra and non-target identification, with potential applications in environmental and metabolomics analyses. Conclusions NTA in regulatory monitoring is critical for environmental protection, but bottlenecks in data analysis and results interpretation remain. The prescreening and quality control workflow, and interpretation work performed here are crucial steps towards scaling up NTA for environmental monitoring.

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

Cited by 19 publications

References 21 publications

Evaluation of Nontarget Long-Term LC–HRMS Time Series Data Using Multivariate Statistical Approaches

Evaluation of Nontarget Long-Term LC–HRMS Time Series Data Using Multivariate Statistical Approaches

From Pesticides to Per- and Polyfluoroalkyl Substances: An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics

Retrospective non-target analysis to support regulatory water monitoring: from masses of interest to recommendations via in silico workflows

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