General strategies to increase the repeatability in non-target screening by liquid chromatography-high resolution mass spectrometry

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.

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.

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

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

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.

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

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

From data to reliable conclusions: Identification and comparison of persistent micropollutants and transformation products in 37 wastewater samples by non-target screening prioritization

In this study, micropollutants in wastewater effluents were prioritized by monitoring the composition of influent and effluent wastewater by liquid chromatography - high-resolution mass spectrometry (LCHRMS) non-target screening (NTS) analysis. The study shows how important data pre-processing and filtering of raw data is to produce reliable NTS data for comparison of compounds between many samples (37 wastewater samples) analyzed at different times. Triplicate injections were critical for reducing the number of false-positive detections. Intensity drift corrections within and between batches analyzed months apart made peak intensities comparable across samples. Adjustment of the feature detection threshold was shown to be important, due to large intensity variations for low abundance compounds across batches. When the threshold correction cut-offs, or the filtering of relevant compounds by the occurrence frequency, were too stringent, a high number of false positive transformation products (TPs) were reported. We also showed that matrix effect correction by internal standards can over- or under-correct the intensity for unknown compounds, thus the TIC matrix effect correction was shown as an additional tool for a retention time dependent matrix effect correction. After these preprocessing and filtering steps, we identified 78 prioritized compounds, of which 36 were persistent compounds, defined as compounds with a reduction in peak intensity between influent and effluent wastewater <50%, and 13 compounds were defined as TPs because they occurred solely in the effluent samples. Some examples of persistent compounds are 1,3-diphenylguanidine, amisulpride and the human metabolites from losartan, verapamil and methadone. To our knowledge, nine of the identified TPs have not been previously described in effluent wastewater. The TPs were derived from metoprolol, fexofenadine, DEET and losartan. The screening of all identified compounds in effluent samples from eight wastewater treatment plants (WWTPs) showed that potential drugs of abuse, anti-psychotic and anti-depressant drugs were predominant in the capital city region, whereas the anti-epileptic agents and agricultural pesticides were dominant in more rural areas.

Suspected-screening assessment of the occurrence of organic compounds in sewage sludge

The profiling of emerging organic pollutants present in sludge and generated during wastewater treatment is much more limited than in water. This is mainly due to the difficulty of sludge analysis because of its high content of organic matter and interfering compounds. In this study, a generic extraction method using a mixture of buffered water (pH 4.1) and solid phase extraction (SPE) clean-up was applied to samples of sludge obtained in different treatment plants. This extraction was followed by determination of the contaminants by ultra-high performance liquid chromatography coupled to high resolution mass spectrometry (UHPLC-HRMS), using suspected screening to detect the most relevant organic compounds that access the environment through sludge application. This screening (including >3000 substances, such as, pharmaceuticals, pesticides, metabolites and industrial chemicals) tentatively identified 122 compound and assigned most probable structure to 39. The set of compounds assigned to a probable structure was increased in 14 compounds by searching in a free database of metabolites. Fifteen compounds were unequivocally confirmed against the analytical standard. Pharmaceuticals and personal care products (PPCPs), with 31 substances identified and 8 confirmed were the main group of compounds. Compounds frequently detected in all sludge samples include nucleotides such as adenosine triphosphate, amino acids such as phenylalanine, or peptides such as leu-phe. Altogether, the results of this work highlight the interest of HRMS to draw the profile of organic compounds in complex matrices.

Tracking pollutants in a municipal sewage network impairing the operation of a wastewater treatment plant

This work provides a screening of organic contaminants and characterization of the dissolved organic matter in the sewer network until the municipal wastewater treatment plant (WWTP), identifying the network areas with a higher degree of contamination and their impact on the WWTP performance, particularly in the activated sludge reactor. Three monitoring campaigns were carried out at six selected locations of the sewage system (PVZ-1, PVZ-2, PS-F, PS-VC, CP-VC, and PS-T), influent (WWTP_INF) and effluent (WWTP_EFF) of the WWTP. Advanced analytical techniques were employed, namely excitation/emission matrix fluorescence-parallel factor analysis (EEM-PARAFAC), size exclusion chromatography with organic carbon detector (SEC-OCD), and liquid chromatography with high-resolution-mass spectrometric detection (LC-HRMS). EEM-PARAFAC showed higher fluorescence intensity for the protein-like component (C2), particularly at CP-VC (near seafood industries) associated with the presence of surfactants (~50 mg/L). SEC-OCD highlighted the WWTP efficiency in removing low molecular weight acids and neutrals. LC-HRMS tentatively identified 108 compounds of emerging concern (CEC) and similar detection patterns were obtained for all wastewater samples, except for PVZ-2 (lower detection), many of which occurred in the effluent. Eight CECs included on relevant Watch-Lists were detected in all WWTP_EFF samples. Furthermore, 111 surfactants were detected, the classes more frequently found being alcohol ethoxylates (AEOs), nonylphenol polyethoxylates (NPEOs) and linear alkylbenzene sulphonates (LAS). The continuous presence of LAS and NPEOs allied to surfactants concentrations in the WWTP_INF of 15-20 mg/L, with CP-VC location (linked with food industries) as an important contributor, explain the morphological changes in the activated sludge and high LAS content in the dewatered sludge, which may have impacted WWTP performance.

Open-source feature detection for non-target LC-MS analytics

RATIONALE

Non-target screening techniques using high-resolution mass spectrometers become more and more important for environmental sciences. Highly reliable and sophisticated software solutions are required to deal with the large amount of data obtained from such analyses.

METHODS

Processing of high-resolution LC-HRMS data was performed upon conversion into an open, XML-based data format followed by an automated assignment of chromatographic peaks using the open-source programming language R. Raw data from three different LC-HRMS systems were processed as a proof of principle.

RESULTS

We present a simple and straightforward algorithm to extract chromatographic peaks from previously m/z-centroided data based on the open-source programming language R and C++. The working principle and processing parameters are explained in detail. A ready-to-use script is provided in the supporting information.

CONCLUSIONS

The developed algorithm enables a comprehensible automated peak picking of non-target LC-MS data. Application to three completely different HRMS raw data files showed reasonable False Positives and False Negatives detection and moderate calculation times.

Characterization of advanced wastewater treatment with ozone and activated carbon using LC-HRMS based non-target screening with automated trend assignment

Advanced treatment is increasingly being applied to improve abatement of micropollutants in wastewater effluent and reduce their load to surface waters. In this study, non-target screening of high-resolution mass spectrometry (HRMS) data, collected at three Swiss wastewater treatment plants (WWTPs), was used to evaluate different advanced wastewater treatment setups, including (1) granular activated carbon (GAC) filtration alone, (2) pre-ozonation followed by GAC filtration, and (3) pre-ozonation followed by powdered activated carbon (PAC) dosed onto a sand filter. Samples were collected at each treatment step of the WWTP and analyzed with reverse-phase liquid chromatography coupled to HRMS. Each WWTP received a portion of industrial wastewater and a prioritization method was applied to select non-target features potentially resulting from industrial activities. Approximately 37,000 non-target features were found in the influents of the WWTPs. A number of non-target features (1207) were prioritized as likely of industrial origin and 54 were identified through database spectral matching. The fates of all detected non-target features were assessed through a novel automated trend assignment method. A trend was assigned to each non-target feature based on the normalized intensity profile for each sampling date. Results showed that 73±4% of influent non-target features and the majority of industrial features (89%) were well-removed (i.e., >80% intensity reduction) during biological treatment in all three WWTPs. Advanced treatment removed, on average, an additional 11% of influent non-target features, with no significant differences observed among the different advanced treatment settings. In contrast, when considering a subset of 66 known micropollutants, advanced treatment was necessary to adequately abate these compounds and higher abatement was observed in fresh GAC (7,000-8,000 bed volumes (BVs)) compared to older GAC (18,000-48,000 BVs) (80% vs 56% of micropollutants were well-removed, respectively). Approximately half of the features detected in the WWTP effluents were features newly formed during the various treatment steps. In ozonation, between 1108-3579 features were classified as potential non-target ozonation transformation products (OTPs). No difference could be observed for their removal in GAC filters at the BVs investigated (70% of OTPs were well-removed on average). Similar amounts (67%) was observed with PAC (7.7-13.6 mg/L) dosed onto a sand filter, demonstrating that a post-treatment with activated carbon is efficient for the removal of OTPs.

Suspect screening workflow comparison for the analysis of organic xenobiotics in environmental water samples

Suspect screening techniques are able to determine a broader range of compounds than traditional target analysis. However, the performance of the suspect techniques relies on the procedures implemented for peak annotation and for this, the list of potential candidates is clearly a limiting factor. In order to study this effect on the number of compounds annotated in environmental water samples, a method was validated in terms of absolute recoveries, limits of quantification and identification, as well as the peak picking capability of the software (Compound Discoverer 2.1) using a target list of 178 xenobiotics. Four suspect screening workflows using different suspect lists were compared: (i) the Stoffident list, (ii) all the NORMAN lists, (iii) suspects containing C, H, O, N, S, P, F or Cl in their molecular formula with more than 10 references in Chemspider and (iv) the mzCloud library. The results were compared in terms of the number of annotated compounds at each confidence level. The same 8 compounds (atenolol, caffeine, caprolactam, carbendazim, cotinine, diclofenac, propyphenazone and trimetoprim) were annotated at the highest confidence level using the four workflows. Remarkable differences were observed for lower confidence levels but only 4 features were annotated at different levels by the four workflows. While the third approach provided the highest number of annotated features, the workflow based on the mzCloud library rendered satisfactory results with a simpler approach. Finally, this latter approach was extended to the analysis of organic xenobiotics in different environmental water samples.

High-Coverage Screening of Sulfonamide Metabolites in Goat Milk by Magnetic Doped S Graphene Combined with Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry

Currently, there are more than 1000 varieties of synthetic sulfonamides universally used as antibiotics causing severe results of potential carcinogenicity and drug resistance for human health due to excessive residue of animal-derived food. A facile and novel approach for untargeted screening of sulfonamides (SAs) and metabolites was proposed based on magnetic solid-phase extraction-ultrahigh-performance liquid chromatography-tandem high-resolution mass spectrometry (MSPE-UHPLC-HRMS). Compared with QuEChERS without the clean-up procedure and SPE in terms of matrix effect and absolute recovery, magnetic doped S graphene (S-doping level: 2.82%) synthesized via a solid-state microwave approach and the aggregation wrap mechanism was used as a novel adsorbent for nonspecific extraction of desired analytes by the noncovalent interaction between electron-deficient thiophene sulfur and electron donors such as amino or amide as well as π-π stacking interactions. Combined with variable data-independent acquisition, characteristic fragment-ion filtering (m/z 156.01138 or m/z 108.04439) and assignment of extracted-ion chromatograms of marked fragment ions were successfully utilized to screen the desired analytes and subsequently confirmed with the availability of reference standards. The optimized and validated approach for spiked 26 SAs and 9 metabolites in control goat milk demonstrated satisfactory accuracy (80.1-112.6%) and precision (RSDs < 6.4%) for matrix-matched standard addition. After applying suspect goat milk samples, untargeted SA analytes including sulfanilamide or an N⁴-acetylsulfamethazine metabolite with concentration ranging from 66.3 to 398.5 ng L^-1 were determined in 5 of 45 goat milk samples.

Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion

The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening analysis methods are needed to monitor the raw and drinking water quality continuously. Non-target screening analysis has been shown to allow for a more comprehensive investigation of drinking water processes compared to target analysis alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatography coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening analysis to identify known and unknown substances. Additionally, the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Positive findings for a total of 60 substances in at least one sample were obtained through quantitative screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards. In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two β-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards. Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS analysis.

Combination of different chromatographic and sampling modes for high-resolution mass spectrometric screening of organic microcontaminants in water

This study explores the combination of two sampling strategies (polar organic compounds integrative sampler (POCIS) vs. spot sampling) and four chromatographic retention modes (reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), mixed-mode liquid chromatography (MMLC) and supercritical fluid chromatography (SFC)) for high-resolution mass spectrometry (HRMS) screening of organic pollutants in water samples. To this end, a suspect screening approach, using iterative data-dependent tandem mass spectrometry (MS/MS) driven by a library of 3227 chemicals (including pharmaceuticals, pesticides, drugs of abuse, human metabolites, industrial chemicals and other pollutants), was employed. Results show that POCIS can afford a larger number of positive identifications as compared to spot sampling. On the other hand, the best suited retention mechanisms, in terms of identified analytes, are SFC, and followed by RPLC, MMLC and HILIC. However, the best combination (POCIS + SFC) would only allow the identification of 67% of the detected analytes. Thus, the combination of the two sampling strategies, spot and passive sampling, with two orthogonal retention mechanisms, RPLC and SFC, is proposed in order to maximize the number of analytes detected (89%). This strategy was applied to different surface water (river and estuary) samples from Galicia (NW Spain). A total of 155 compounds were detected at a confidence level 2a, from which the major class was pharmaceuticals (61%).

Data-Independent Identification of Suspected Organic Pollutants Using Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry

The identity of an unknown environmental pollutant is reflected by the mass and dissociation chemistry of its (quasi)molecular ion. Gas chromatography-atmospheric pressure chemical ionization-mass spectrometry (GC-APCI-MS) increases the yield of molecular ions (compared to conventional electron ionization) by collisional cooling. Scanning quadrupole data-independent acquisition (SQDIA) permits unbiased, unattended selection of (quasi)molecular ions and acquisition of structure-diagnostic collision-induced dissociation mass spectra, while minimizing interferences, by sequentially cycling a quadrupole isolation window through the m/z range. This study reports on the development of a suspect screening method based on industrial compounds with bioaccumulation potential. A comparison of false and correct identifications in a mixed standard containing 30 analytes suggests that SQDIA results in a markedly lower false-positive rate than standard DIA: 5 for SQDIA and 82 for DIA. Electronic waste dust was analyzed using GC and quadrupole time-of-flight MS with APCI and SQDIA acquisition. A total of 52 brominated, chlorinated, and organophosphorus compounds were identified by suspect screening; 15 unique elemental compositions were identified using nontargeted screening; 17 compounds were confirmed using standards and others identified to confidence levels 2, 3, or 4. SQDIA reduced false-positive identifications, compared to experiments without quadrupole isolation. False positives also varied by class: 20% for Br, 37% for Cl, 75% for P, and >99% for all other classes. The structure proposal of a previously reported halogenated compound was revisited. The results underline the utility of GC-SQDIA experiments that provide information on both the (quasi)molecular ions and its dissociation products for a more confident structural assignment.

Comparison of preconcentration methods for nontargeted analysis of natural waters using HPLC-HRMS: Large volume injection versus solid-phase extraction

Nontargeted analysis of water samples using liquid chromatography combined with high‐resolution mass spectrometers is an emerging approach for surface water monitoring and evaluation of water treatment processes. In this study, sample preconcentration via direct, large volume injection with 500 μL and 1000 μL injection volumes was compared to SPE regarding analytical performance parameters in targeted and nontargeted workflows. In targeted analysis, the methods were evaluated in terms of LOD and intrabatch precision of the selected compounds, whereas in nontargeted analysis, the number of detected unknown compounds, the method's intra‐batch precision, and the retention time versus molecular mass pattern of the detected unknowns were evaluated. In addition, a novel intensity drift correction method was developed that is not based on quality control samples and makes use of the signals obtained for continuously infused reference compounds, which are conventionally utilized for online mass drift correction. It could be demonstrated that the new correction method significantly reduced the bias introduced by instrumental drift and is important for the reliable intercomparison of different nontargeted methods. Intercomparison of results showed that the 1000 μL large volume injection method revealed the best performance in terms of precision under repeatability conditions of measurement as well as lower LODs for targeted compound analysis. In nontargeted analysis, the SPE method detected a higher number of unknown compounds but exhibited also a higher uncertainty of measurement caused by matrix effects.

Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation

Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.

Targeted and non-targeted unexpected food contaminants analysis by LC/HRMS: Feasibility study on rice

A widely applicable analytical LC/HRMS method based on ion source optimization, data treatment optimization on rice matrix was developed. The effects of key parameters of ion source, and their interactions on ESI response were studied on HPLC-QTOF. Compared with center points, 40% and 20% increase of response factors in the positive and negative mode can be achieved by ion source optimization, respectively. Data processing strategies inspired from metabolomics and multi-targeted analysis were compared and developed using case and control rice samples. Highly automated workflow using XCMS achieved highest mass accuracy, highest detection rate of 96% for 5 μg/kg in a non-targeted way. A clear distinction between the control and contaminated samples by PCA and PLS-DA was also achieved by this workflow using XCMS, even for the concentration of 5 μg/kg.

Identification of acetylcholinesterase inhibitors in water by combining two-dimensional thin-layer chromatography and high-resolution mass spectrometry

Effect-directed analysis (EDA) is increasingly used in environmental monitoring to detect and identify key toxicants. High-performance thin-layer chromatography (HPTLC) has proven to be a very suitable fractionation technique for this purpose. However, HPTLC is limited in its separation efficiency. Thus, separated fractions could still contain many different components and identification of the effective substances remains difficult. Therefore, in this study a workflow for selective EDA with two-dimensional HPTLC in combination with high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) was developed. The aim of the workflow was the stepwise reduction of the sample complexity in order to reduce the number of signals that could be responsible for the measured effects. As a consequence, the identification of effective substances should be facilitated. The acetylcholinesterase inhibition assay (AChE assay) for the detection of potential neurotoxic compounds was applied for biotesting. The transfer of effective zones from the first to the second dimension and also to the mass spectrometric measurement was enabled by extraction. A proof of concept was performed by spiking six acetylcholinesterase inhibiting substances into three different water matrices that were investigated with the developed workflow. The successful prioritization of all spiked compounds confirmed the efficiency of the workflow, regardless of the sample matrix. Biotesting of different water samples resulted in numerous potentially neurotoxic effects, which overlapped strongly in the first separation dimension. The higher peak capacity reached by two-dimensional HPTLC, on the other hand, resulted in discrete effective zones and enabled the identification of several compounds. For the substances lumichrome, a derivate of riboflavin and paraxanthine as well as for linear alkylbenzene sulfonates that were applied as anionic surfactants in detergents, the inhibiting effect to the AChE could be confirmed.

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.

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.

Nontarget analysis: A new tool for the evaluation of wastewater processes

Strategies to determine the removal efficiency of micropollutants in wastewater treatment plants (WWTPs) are widely discussed. Especially the evaluation of the potential benefit of further advanced treatment steps such as an additional tertiary treatment based on ozonation or activated carbon have come into focus. Such evaluation strategies are often based on the removal behavior of known micropollutants via target or suspected analysis. The utilization of nontarget analysis is considered to lead to a more comprehensive picture as also unknown or not expected micropollutants are analyzed. Here, the results of an evaluation via target and nontarget analysis were compared for biological treatment (BT) processes of eleven full-scale WWTPs and three different post-treatments (PTs): one sand filter (SF) and two granular activated carbon (GAC) filters. The similarity of the determined removals from target and nontarget analysis of the BTs increased significantly by excluding easily degradable "features" from the nontarget evaluation. A similar ranking of the removal trends for the BTs could also be achieved by comparing this new subset of nontarget features with a set of nine readily to moderately biodegradable micropollutants. This observation suggests that a performance ranking of BTs based either on target or nontarget analysis is plausible. In contrast to the BTs, the evaluation of the three PTs revealed that the difference of feature removal between SF and the two GACs was small, but large for the target analytes with substantially higher removal effciencies for the GACs compared to the SF. In addition to the removal behavior, the nontarget analysis provided further information about the number and quantity of transformation products (TPs) in the effluent from the BTs. For all BTs more than half (55-67%) of the features detected in the effluent were not found in the influent. A comparable proportion of TPs was also detected after GAC and sand filtration due to their microbial activities.

"-Omics" workflow for paleolimnological and geological archives: A review

"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.

Evaluating emerging organic contaminant removal in an engineered hyporheic zone using high resolution mass spectrometry

The hyporheic zone (HZ), located at the interface of surface and groundwater, is a natural bioreactor for attenuation of chemical contaminants. Engineered HZs can be incorporated into stream restoration projects to enhance hyporheic exchange, with flowpaths optimized to promote biological habitat, water quantity, and water quality improvements. Designing HZs for in-stream treatment of stormwater, a significant source of flow and contaminant loads to urban creeks, requires assessment of both the hydrology and biogeochemical capacity for water quality improvement. Here, we applied tracer tests and high resolution mass spectrometry (HRMS) to characterize an engineered hyporheic zone unit process, called a hyporheic design element (HDE), in the Thornton Creek Watershed in Seattle, WA. Dye, NaCl, and bromide were used to hydrologically link downwelling and upwelling zones and estimate the hydraulic retention time (HRT) of hyporheic flowpaths. We then compared water quality improvements across hydrologically-linked surface and hyporheic flowpaths (3-5 m length; ∼30 min to >3 h) during baseflow and stormflow conditions. We evaluated fate outcomes for 83 identified contaminants during stormflow, including those correlated with an urban runoff mortality syndrome in coho salmon. Non-target HRMS analysis was used to assess holistic water quality improvements and evaluate attenuation mechanisms. The data indicated substantial water quality improvement in hyporheic flowpaths relative to surface flow and improved contaminant removal with longer hyporheic HRT (for ∼1900 non-target compounds detected during stormflow, <17% were attenuated >50% via surface flow vs. 59% and 78% via short and long hyporheic residence times, respectively), and strong contributions of hydrophobic sorption towards observed contaminant attenuation.

Untargeted time-pattern analysis of LC-HRMS data to detect spills and compounds with high fluctuation in influent wastewater

Peak prioritization plays a key role in non-target analysis of complex samples in order to focus the elucidation efforts on potentially relevant substances. The present work shows the development of a computational workflow capable of detecting compounds that exhibit large variation in intensity over time. The developed approach is based on three open-source R packages (xcms, CAMERA and TIMECOURSE) and includes the use of the statistical test Multivariate Empirical Bayes Approach to rank the compounds based on the Hotelling T2 coefficient, which is an indicator of large concentration variations of unknown components. The approach was applied to replicate series of 24 h composite flow-proportional influent wastewater samples collected during 8 consecutive days. 60 events involving unknown substances with high fluctuation over time were successfully prioritized. 14 of those compounds were tentatively identified using HRMS/MS libraries, chemical databases, in-silico fragmentation tools, and retention time prediction models. Four compounds were confirmed with standards from which two never reported before in wastewater.

Non-targeted analysis of unexpected food contaminants using LC-HRMS

A non-target analysis method for unexpected contaminants in food is described. Many current methods referred to as “non-target” are capable of detecting hundreds or even thousands of contaminants. However, they will typically still miss all other possible contaminants. Instead, a metabolomics approach might be used to obtain “true non-target” analysis. In the present work, such a method was optimized for improved detection capability at low concentrations. The method was evaluated using 19 chemically diverse model compounds spiked into milk samples to mimic unknown contamination. Other milk samples were used as reference samples. All samples were analyzed with UHPLC-TOF-MS (ultra-high-performance liquid chromatography time-of-flight mass spectrometry), using reversed-phase chromatography and electrospray ionization in positive mode. Data evaluation was performed by the software TracMass 2. No target lists of specific compounds were used to search for the contaminants. Instead, the software was used to sort out all features only occurring in the spiked sample data, i.e., the workflow resembled a metabolomics approach. Procedures for chemical identification of peaks were outside the scope of the study. Method, study design, and settings in the software were optimized to minimize manual evaluation and faulty or irrelevant hits and to maximize hit rate of the spiked compounds. A practical detection limit was established at 25 μg/kg. At this concentration, most compounds (17 out of 19) were detected as intact precursor ions, as fragments or as adducts. Only 2 irrelevant hits, probably natural compounds, were obtained. Limitations and possible practical use of the approach are discussed.

LC-HRMS Data Processing Strategy for Reliable Sample Comparison Exemplified by the Assessment of Water Treatment Processes

The behavior of micropollutants in water treatment is an important aspect in terms of water quality. Nontarget screening by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) offers the opportunity to comprehensively assess water treatment processes by comparing the signal heights of all detectable compounds before and after treatment. Without preselection of known target compounds, all accessible information is used to describe changes across processes and thus serves as a measure for the treatment efficiency. In this study, we introduce a novel LC-HRMS data processing strategy for the reliable classification of signals based on the observed fold changes. An approach for filtering detected features was developed and, after parameter adjustment, validated for its recall and precision. As proof of concept, the fate of 411 target compounds in a 0.1 μg/L standard mix was tracked throughout the data processing stages, where 406 targets were successfully recognized and retained during filtering. Potential pitfalls in signal classification were addressed. We found the recursive peak integration to be a key point for the reliable classification of signal changes across a process. For evaluating the repeatability, a combinatorial approach was conducted to verify the consistency of the final outcome using technical replicates of influent and effluent samples taken from an ozonation process during drinking water treatment. The results showed sufficient repeatability and thus emphasized the applicability of nontarget screening for the assessment of water treatment processes. The developed data processing strategies may be transferred to other research fields where sample comparisons are conducted.

Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go?

The vast, diverse universe of organic pollutants is a formidable challenge for environmental sciences, engineering, and regulation. Nontarget screening (NTS) based on high resolution mass spectrometry (HRMS) has enormous potential to help characterize this universe, but is it ready to go for real world applications? In this Feature article we argue that development of mass spectrometers with increasingly high resolution and novel couplings to both liquid and gas chromatography, combined with the integration of high performance computing, have significantly widened our analytical window and have enabled increasingly sophisticated data processing strategies, indicating a bright future for NTS. NTS has great potential for treatment assessment and pollutant prioritization within regulatory applications, as highlighted here by the case of real-time pollutant monitoring on the River Rhine. We discuss challenges for the future, including the transition from research toward solution-centered and robust, harmonized applications.

Selective two-dimensional effect-directed analysis with thin-layer chromatography

There are thousands of organic trace substances in the environment that are not fully characterized, and evaluation of their relevance to the ecosystem is difficult. Effect-directed analysis (EDA) is a suitable tool to assess the effects of a substance via in-vitro bioassays, which can provide information about the relevance of the substance. High-performance thin-layer chromatography (HPTLC) has been shown to be a good method for fractionation. Environmental samples, however, often have high complexity, which is why the peak capacity of HPTLC is not sufficient. Therefore, this study focused on the development of selective two-dimensional (2D) HPTLC-EDA to increase the peak capacity and facilitate the identification of effective compounds. Thus, only effective zones were selected in the first dimension in terms of heart-cutting and were transferred to the second dimension through elution head-based extraction. Three 2D approaches were developed and validated. The best results in terms of peak capacity and orthogonality were achieved when the retardation factors of the first dimension were used to adjust the mobile phase (MP) for the second dimension. Applying the acetylcholinesterase (AChE) inhibition assay as an example EDA, analysis of spiked surface water by 2D HPTLC-EDA allowed zones with neurotoxic effects to responsible substances to be assigned. The 2D separation reduced the complexity of effective zones and thus facilitated the subsequent identification of effective compounds. Knowledge about a substancés effects enabled assessment of its relevance to the environment.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.