The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment

Approaches for GC-HRMS Screening of Organic Microcontaminants: GC-APCI-IMS-QTOF versus GC-EI-QOrbitrap

This study explores the capabilities of GC-APCI-IMS-QTOF MS and GC-EI-QOrbitrap MS in screening applications and different strategies for wide-scope screening of organic microcontaminants using target suspect and nontarget approaches. On one side, GC-APCI-IMS-QTOF MS excels at preserving molecular information and adds ion mobility separation, facilitating screening through the list of componentized features containing accurate mass, retention time, CCS, and fragmentation data. On the other side, the extensive and robust fragmentation of GC-EI-QOrbitrap MS allows the application of different strategies for target and nontarget approaches using the NIST library spectra. Our findings revealed that GC-EI-QOrbitrap MS is more sensitive in target approaches. Automated workflows for suspect screening in GC-APCI-IMS-QTOF MS minimize false annotations but face challenges with false negatives due to in-source fragmentation and limitations when using in silico fragmentation tools. Conversely, a nontarget approach in GC-EI-QOrbitrap MS can reliably identify unknowns but results in more false annotations in complex matrices. This work highlights the strengths and limitations of each system and guides for their optimal application for wide-scope screening in environmental and food safety applications.

Characterizing environmental contamination by plant protection products along the land-to-sea continuum:a focus on France and French overseas territories

Environmental compartments are contaminated by a broad spectrum of plant protection products (PPPs) that are currently widely used in agriculture or, for some of them, whose use was banned many years ago. The aim of this study is to draw up an overview of the levels of contamination of soils, continental aquatic environments, seawaters and atmosphere by organic PPPs in France and the French overseas territories, based on data from the scientific publications and the grey literature. It is difficult to establish an exhaustive picture of the overall contamination of the environment because the various compartments monitored, the monitoring frequencies, the duration of the studies and the lists of substances are not the same. Of the 33 PPPs most often recorded at high concentration levels in at least one compartment, 5 are insecticides, 9 are fungicides, 15 are herbicides and 4 are transformation products. The PPP contamination of the environment shows generally a seasonal variation according to crop cycles. On a pluriannual scale, the contamination trends are linked to the level of use driven by the pest pressure, and especially to the ban of PPP. Overall, the quality of the data acquired has been improved thanks to new, more integrative sampling strategies and broad-spectrum analysis methods that make it possible to incorporate the search for emerging contaminants such as PPP transformation products. Taking into account additional information (such as the quantities applied, agricultural practices, meteorological conditions, the properties of PPPs and environmental conditions) combined with modelling tools will make it possible to better assess and understand the fate and transport of PPPs in the environment, inter-compartment transfers and to identify their potential impacts. Simultaneous monitoring of all environmental compartments as well as biota in selected and limited relevant areas would also help in this assessment.

Phytochemical fingerprinting of phytotoxins as a cutting-edge approach for unveiling nature's secrets in forensic science

The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier, providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinting of phytotoxins as a cutting-edge approach. This study explores the dynamic intersection of phytochemistry and forensic science, highlighting how the unique phytochemical profiles of toxic plants and their secondary metabolites, serve as distinctive markers for forensic investigations. By utilizing advanced techniques such as Ultra-High-Performance Liquid Chromatography (UHPLC) and High-Resolution Mass Spectrometry (HRMS), the detection and quantification of plant-derived are made more accurate in forensic contexts. Real-world case studies are presented to demonstrate the critical role of plant toxins in forensic outcomes and legal proceedings. The challenges, potential, and future prospects of integrating phytochemical fingerprinting of plant toxins into forensic science were discussed. This review aims to illuminate phytochemical fingerprinting of plant toxins as a promising tool to enhance the precision and depth of forensic analyses, offering new insights into the complex stories embedded in plant toxins.

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Target, suspect, and non-target screening of per- and poly-fluoroalkyl substances in wastewater treatment plant effluents in South Korea using ion mobility spectrometry-mass spectrometry

This study used target, suspect, and non-target screening, to assess the presence of per- and polyfluoroalkyl substances (PFASs) in domestic (municipal) and industrial wastewater treatment plants (WWTPs) in South Korea. Target analysis quantified 20 PFASs in the WWTP effluents. Total concentration of PFASs ranged from 69.1 to 79.6 ng/L and the concentrations of perfluorobutanoic acid (PFBA) (mean: 15.6 ng/L, median: 14.9 ng/L) and perfluorooctanoic acid (PFOA) (mean: 14.7 ng/L, median: 12.7 ng/L) were higher than those of other PFASs. Compared to 2010, there was an overall increase in perfluoroalkyl carboxylic acids (PFCAs), particularly perfluoroheptanoic acid, (PFHpA), which showed a nearly 10-fold increase, with current concentrations reaching 9.5 ng/L. Suspect and non-target screening with ion mobility spectrometry (IMS)-mass spectrometry was used to identify additional PFASs based on their exact mass, collision cross-section (CCS), and tandem mass spectrometry fragmentation patterns. Twenty compounds were identified as PFAS compounds through suspect screening at a confidence level (CL) of 3 or higher, with five compounds identified at CL 2. Additionally, fragment-based, suspect and non-target screening identified emerging PFASs, including FBSA, a n:2 fluorotelomer-based non-polymer, and bistriflimide, all with CL 2. Semi-quantification of identified PFASs revealed that the concentrations of PFASs identified by suspect and non-target screening were higher than those of the target PFASs, especially in industrial wastewater effluents.

Trapped and drift-tube ion-mobility spectrometry for the analysis of environmental contaminants: Comparability of collision cross-section values and resolving power

RATIONALE

Ion-mobility (IM)-derived collision cross-section (CCS) values can serve as a valuable additional identification parameter within suspect and non-target screening studies of environmental contaminants. However, these applications require to assess the reproducibility of CCS calculations between different IM set-ups. Especially for the comparison of trapped and drift-tube IM (TIMS/DTIM) derived CCS values, data for environmental applications is lacking.

METHODS

The presented study assessed the bias of TIMS derived CCSN2 (TIMSCCSN2) values of 48 environmental contaminants from three classes in comparison to a previously established DTIM database. Based on two sets of isomeric bisphenols, the resolving power of both systems was compared, addressing the instrumental settings which influence the resolution of TIMS measurements.

RESULTS

For 91% of the datapoints, bias between TIMSCCSN2 and DTCCSN2 values (latter set as reference) were < 2%, indicating a good inter-platform reproducibility. TIMS resolving power was dependent on the selected mobility window and ramping times whereby a resolution of up to 116 was achieved. Similar resolving power was observed for multiplexed DTIMS data if a high-resolution post-processing step was implemented.

CONCLUSIONS

These results provide valuable insights in CCSN2 reproducibility facilitating database transfer in future TIMS based studies. Knowledge on the influence of acquisition settings on robustness of TIMSCCSN2 calculations and resolving power can ease method development supporting efficient development and reliable identifications of emerging environmental contaminants.

In Vitro Metabolism of Quaternary Ammonium Compounds and Confirmation in Human Urine by Liquid Chromatography Ion-Mobility High-Resolution Mass Spectrometry

Quaternary ammonium compounds (QACs) are high-production chemicals used as cleaning and disinfecting agents. Due to their ubiquitous presence in the environment and several toxic effects described, human exposure to these chemicals gained increasing attention in recent years. However, very limited data on the biotransformation of QACs is available, hampering exposure assessment. In this study, three QACs (dimethyl dodecyl ammonium, C10-DDAC; benzyldimethyl dodecylammonium, C12-BAC; cetyltrimethylammonium, C16-ATMAC) commonly detected in indoor microenvironments were incubated with human liver microsomes and cytosol (HLM/HLC) simulating Phase I and II metabolism. Thirty-one Phase I metabolites were annotated originating from 19 biotransformation reactions. Four metabolites of C10-DDAC were described for the first time. A detailed assessment of experimental fragmentation spectra allowed to characterize potential oxidation sites. For each annotated metabolite, drift-tube ion-mobility derived collision cross section (DTCCSN2) values were reported, serving as an additional identification parameter and allowing the characterization of changes in DTCCSN2 values following metabolism. Lastly, eight metabolites, including four metabolites of both C12-BAC and C10-DDAC, were confirmed in human urine samples showing high oxidation states through introduction of up to four oxygen atoms. This is the first report of higher oxidized C10-DDAC metabolites in human urine facilitating future biomonitoring studies on QACs.

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling

The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Sliding Windows in Ion Mobility (SWIM): A New Approach to Increase the Resolving Power in Trapped Ion Mobility-Mass Spectrometry Hyphenated with Chromatography

Over the past decade, the separation efficiency achieved by linear IMS instruments has increased substantially, with state-of-the-art IM technologies, such as the trapped ion mobility (TIMS), the cyclic traveling wave ion mobility (cTWIMS), and the structure for lossless ion manipulation (SLIM) platforms commonly demonstrating resolving powers in excess of 200. However, for complex sample analysis that require front end separation, the achievement of such high resolving power in TIMS is significantly hampered, since the ion mobility range must be broad enough to analyze all the classes of compounds of interest, whereas the IM analysis time must be short enough to cope with the time scale of the preseparation technique employed. In this paper, we introduce the concept of sliding windows in ion mobility (SWIM) for chromatography hyphenated TIMS applications that bypasses the need to use a wide and fixed IM range by using instead narrow and mobile ion mobility windows that adapt to the analytes' ion mobility during chromatographic separation. GC-TIMS-MS analysis of a mixture of 174 standards from several halogenated persistent organic pollutant (POP) classes, including chlorinated and brominated dioxins, biphenyls, and PBDEs, demonstrated that the average IM resolving power could be increased up to 40% when the SWIM mode was used, thereby greatly increasing the method selectivity for the analysis of complex samples.

Analytical approaches for screening of per- and poly fluoroalkyl substances in food items: A review of recent advances and improvements

Per and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals characterized by their ubiquitous nature in all environmental compartments which makes them of increasing concern due to their persistence, bioaccumulation, and toxicity (PBT). Several instrumental methodologies and separation techniques have been identified in the literature for the detection and quantification of PFAS in environmental samples. In this review, we have identified and compared common separation techniques adopted for the extraction of PFAS in food items, and analytical methodologies for identification and quantification of PFAS in food items of plant and animal origin, highlighting recent advances in tandem techniques for the high selectivity and separation of PFAS related compounds as well as knowledge gaps and research needs on current analytical methodologies.

Identification and characterization of quaternary ammonium compounds in Flemish indoor dust by ion-mobility high-resolution mass spectrometry

Quaternary ammonium compounds (QACs) are a class of surfactants commonly used in disinfecting and cleaning products. Their use has substantially increased during the COVID-19 pandemic leading to increasing human exposure. QACs have been associated with hypersensitivity reactions and an increased risk of asthma. This study introduces the first identification, characterization and semi-quantification of QACs in European indoor dust using ion-mobility high-resolution mass spectrometry (IM-HRMS), including the acquisition of collision cross section values (^DTCCS_N2) for targeted and suspect QACs. A total of 46 indoor dust samples collected in Belgium were analyzed using target and suspect screening. Targeted QACs (n = 21) were detected with detection frequencies ranging between 4.2 and 100 %, while 15 QACs showed detection frequencies > 90 %. Semi-quantified concentrations of individual QACs showed a maximum of 32.23 µg/g with a median ∑QAC concentration of 13.05 µg/g and allowed the calculation of Estimated Daily Intakes for adults and toddlers. Most abundant QACs matched the patterns reported in indoor dust collected in the United States. Suspect screening allowed the identification of 17 additional QACs. A dialkyl dimethyl ammonium compound with mixed chain lengths (C16:C18) was characterized as a major QAC homologue with a maximum semi-quantified concentration of 24.90 µg/g. The high detection frequencies and structural variabilities observed call for more European studies on potential human exposure to these compounds. For all targeted QACs, drift tube IM-HRMS derived collision cross section values (^DTCCS_N2) are reported. Reference ^DTCCS_N2 values allowed the characterization of CCS-m/z trendlines for each of the targeted QAC classes. Experimental CCS-m/z ratios of suspect QACs were compared with the CCS-m/z trendlines. The alignment between the two datasets served as an additional confirmation of the assigned suspect QACs. The use of the 4bit multiplexing acquisition mode with consecutive high-resolution demultiplexing confirmed the presence of isomers for two of the suspect QACs.

Effects of structural characteristics of (un)conjugated steroid metabolites in their collision cross section value

In this work, the collision cross section (CCS) value of 103 steroids (including unconjugated metabolites and phase II metabolites conjugated with sulfate and glucuronide groups) was determined by liquid chromatography coupled to traveling wave ion mobility spectrometry (LC-TWIMS). A time of flight (QTOF) mass analyzer was used to perform the analytes determination at high-resolution mass spectrometry. An electrospray ionization source (ESI) was used to generate [M+H]⁺, [M + NH₄]⁺ and/or [M - H]^- ions. High reproducibility was observed for the CCS determination in both urine and standard solutions, obtaining RSD lower than 0.3% and 0.5% in all cases respectively. CCS determination in matrix was in accordance with the CCS measured in standards solution showing deviations below 2%. In general, CCS values were directly correlated with the ion mass and allowed differentiating between glucuronides, sulfates and free steroids although differences among steroids of the same group were less significant. However, more specific information was obtained for phase II metabolites observing differences in the CCS value of isomeric pairs concerning the conjugation position or the α/β configuration, which could be useful in the structural elucidation of new steroid metabolites in the anti-doping field. Finally, the potential of IMS reducing interferences from the sample matrix was also tested for the analysis of a glucuronide metabolite of bolasterone (5β-androstan-7α,17α-dimethyl-3α,17β-diol-3-glucuronide) in urine samples.

Ultra-high performance liquid chromatography coupled to ion mobility quadrupole time-of-flight mass spectrometry for the identification of non-volatile compounds migrating from 'natural' dishes

Although most new biomaterials for food industry applications are labelled '100% natural fabrication' and 'chemical-free', certain compounds may migrate from those materials to the food, compromising the organoleptic characteristics and safety of the product. In this work, the degree of compound migration from dishes made with four different biomaterials: bamboo, palm leaf, wood and wheat pulp was investigated. Migration tests were carried out using three food simulants, 10% ethanol (simulant A), 3% acetic acid (simulant B), and 95% ethanol (simulant D2). Unequivocal identification of non-intentionally added substances (NIAS) is challenging even when using high-resolution mass spectrometry techniques however, a total of 25 different non-volatile compounds from the migration tests were identified and quantified using Ultra-high performance liquid chromatography coupled to ion mobility quadrupole time-of-flight mass spectrometry (UPLC-IMS-MS). In the bamboo samples three oligomers, cyclic diethylene glycol adipate, 3,6,9,16,19,22-hexaoxabicyclo[22.3.1]-octacosa-1(28),24,26-triene-2,10,15,23-tetrone and 1,4,7,14,17,20-hexaoxacyclohexacosane-8,13,21,26-tetrone exceeded the specified limits of migration.

Rapid determination of ampyra in urine samples using dispersive liquid-liquid microextraction coupled with ion mobility spectrometry

Ampyra (AMP, 4-Aminopyridine) is a potassium channel blocker that attracts growing research interest due to its adverse effects at high doses. The fast analysis of AMP is challenging because it typically requires complex analytical techniques. In this research, we developed and validated a novel method to assess the fast and quantitative analysis of AMP from real samples. This method combines the strength of ion mobility spectrometry (IMS) for rapid detection and the dispersive liquid-liquid microextraction as a fast and effective preconcentration method for the preconcentration/extraction of AMP. In this method, Ag nanoparticles were used as modifier agents. Moreover, the proposed mechanism for interaction of AMP with AgNPs was investigated based on the quantum theory of atoms in molecules (QTAIM) analysis. Also, the sensitivity of the proposed method was improved through the application of a delay on the carrier gas flow after sample injection. Under the optimum conditions, the developed method detected AMP in the linear range of 0.4-16 μmol L^-1 with a detection limit of 0.12 µmol L^-1. Finally, the developed method was successfully employed to quantify AMP in urine samples. Method validation was performed by comparing our results with those obtained by HPLC-UV/Vis, confirming the applicability of the proposed method for the AMP analysis in real samples. The proposed method will open up a new door toward developing simple, fast, and effective analytical methods.

Critical assessment of the Kendrick mass defect analysis as an innovative approach to process high resolution mass spectrometry data for environmental applications

The growing application of high resolution mass spectrometry (HRMS) over the last decades has dramatically improved our knowledge about the occurrence of environmental contaminants. However, most of the compounds detected remain unknown and the large volume of data generated requires specific processing approaches. Therefore, this study presents the concepts of mass defect (MD), Kendrick mass (KM) and Kendrick mass defect (KMD) to the expert and non-expert reader along with relevant examples of applications in environmental HRMS data processing. A preliminary bibliometric overview indicates that the potential benefits of KMD analysis are rather overlooked in environmental science. In practice, a simple calculation allows transforming a mass from the IUPAC system (normalized so that the mass of ¹²C is exactly 12) to its corresponding KM normalized on a specific moiety such as CH₂ (the mass of CH₂ is exactly 14). Then, plotting the KMD according to the nominal KM allows revealing groups of compounds that differ only by their number of CH₂ moieties. For instance, data processing using KM and KMD was proven particularly useful to characterize natural organic matter in a sample, to reveal the occurrence of polymers as well as poly/perfluorinated alkylated substances (PFASs), and to search for transformation products (TPs) of a given chemical.

Enhancing spectral quality in complex environmental matrices: Supporting suspect and non-target screening in zebra mussels with ion mobility

Identification of bioaccumulating contaminants of emerging concern (CECs) via suspect and non-target screening remains a challenging task. In this study, ion mobility separation with high-resolution mass spectrometry (IM-HRMS) was used to investigate the effects of drift time (DT) alignment on spectrum quality and peak annotation for screening of CECs in complex sample matrices using data independent acquisition (DIA). Data treatment approaches (Binary Sample Comparison) and prioritisation strategies (Halogen Match, co-occurrence of features in biota and the water phase) were explored in a case study on zebra mussel (Dreissena polymorpha) in Lake Mälaren, Sweden's largest drinking water reservoir. DT alignment evidently improved the fragment spectrum quality by increasing the similarity score to reference spectra from on average (±standard deviation) 0.33 ± 0.31 to 0.64 ± 0.30 points, thus positively influencing structure elucidation efforts. Thirty-two features were tentatively identified at confidence level 3 or higher using MetFrag coupled with the new PubChemLite database, which included predicted collision cross-section values from CCSbase. The implementation of predicted mobility data was found to support compound annotation. This study illustrates a quantitative assessment of the benefits of IM-HRMS on spectral quality, which will enhance the performance of future screening studies of CECs in complex environmental matrices.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R² = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCS_H model (R² = 0.966) was ±4.05% with 95% confidence intervals. The CCS_H model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS_Na, R² = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models

The number of open access databases containing experimental and predicted collision cross section (CCS) values is rising and leads to their increased use for compound identification. However, the reproducibility of reference values with different instrumental designs and the comparison between predicted and experimental CCS values is still under evaluation. This study compared experimental CCS values of 56 small molecules (Contaminants of Emerging Concern) acquired by both drift tube (DT) and travelling wave (TW) ion mobility mass spectrometry (IM-MS). The TWIM-MS included two instrumental designs (Synapt G2 and VION). The experimental ^TWCCS_N2 values obtained by the TWIM-MS systems showed absolute percent errors (APEs) < 2% in comparison to experimental DTIMS data, indicating a good correlation between the datasets. Furthermore, ^TWCCS_N2 values of [M - H]^- ions presented the lowest APEs. An influence of the compound class on APEs was observed. The applicability of prediction models based on artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), both built using TWIM-MS data, was investigated for the first time for the prediction of ^DTCCS_N2 values. For [M+H]⁺ and [M - H]^- ions, the 95th percentile confidence intervals of observed APEs were comparable to values reported for both models indicating a good applicability for DTIMS predictions. For the prediction of ^DTCCS_N2 values of [M+Na]⁺ ions, the MARS based model provided the best results with 73.9% of the ions showing APEs below the threshold reported for [M+Na]⁺. Finally, recommendations for database transfer and applications of prediction models for future DTIMS studies are made.

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals' super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.

Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products

The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]⁺ adducts and 95.0% of CCS values for [M + Na]⁺ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]⁺ and [M + Na]⁺ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.

Are preserved coastal water bodies in Spanish Mediterranean basin impacted by human activity? Water quality evaluation using chemical and biological analyses

The Spanish Mediterranean basin is particularly susceptible to climate change and human activities, making it vulnerable to the influence of anthropogenic contaminants. Therefore, conducting comprehensive and exhaustive water quality assessment in relevant water bodies of this basin is pivotal. In this work, surface water samples from coastal lagoons or estuaries were collected across the Spanish Mediterranean coastline and subjected to target and suspect screening of 1,585 organic micropollutants by liquid chromatography coupled to ion mobility separation and high resolution mass spectrometry. In total, 91 organic micropollutants could be confirmed and 5 were tentatively identified, with pharmaceuticals and pesticides being the most prevalent groups of chemicals. Chemical analysis data was compared with data on bioanalysis of those samples (recurrent aryl hydrocarbon receptor (AhR) activation, and estrogenic receptor (ER) inhibition in wetland samples affected by wastewater streams). The number of identified organic contaminants containing aromatic rings could explain the AhR activation observed. For the ER antagonistic effects, predictions on estrogenic inhibition potency for the detected compounds were used to explain the activities observed. The integration of chemical analysis with bioanalytical observations allowed a comprehensive overview of the quality of the water bodies under study.

Benefits of Ion Mobility Separation in GC-APCI-HRMS Screening: From the Construction of a CCS Library to the Application to Real-World Samples

The performance of gas chromatography (GC) combined with the improved identification properties of ion mobility separation coupled to high-resolution mass spectrometry (IMS-HRMS) is presented as a promising approach for the monitoring of (semi)volatile compounds in complex matrices. The soft ionization promoted by an atmospheric pressure chemical ionization (APCI) source designed for GC preserves the molecular and/or quasi-molecular ion information enabling a rapid, sensitive, and efficient wide-scope screening. Additionally, ion mobility separation (IMS) separates species of interest from coeluting matrix interferences and/or resolves isomers based on their charge, shape, and size, making IMS-derived collision cross section (CCS) a robust and matrix-independent parameter comparable between instruments. In this way, GC-APCI-IMS-HRMS becomes a powerful approach for both target and suspect screening due to the improvements in (tentative) identifications. In this work, mobility data for 264 relevant multiclass organic pollutants in environmental and food-safety fields were collected by coupling GC-APCI with IMS-HRMS, generating CCS information for molecular ion and/or protonated molecules and some in-source fragments. The identification power of GC-APCI-IMS-HRMS for the studied compounds was assessed in complex-matrix samples, including fish feed extracts, surface waters, and different fruit and vegetable samples.

Discovery and Characterization of Phenolic Compounds in Bearberry (Arctostaphylos uva-ursi) Leaves Using Liquid Chromatography-Ion Mobility-High-Resolution Mass Spectrometry

The characterization and quantification of phenolic compounds in bearberry leaves were performed using hyphenated ion mobility spectroscopy (IMS) and a quadrupole time-of-flight mass spectrometer. A higher identification confidence level was obtained by comparing the measured collision cross section (^TWCCS_N2) with predicted values using a machine learning algorithm. A total of 88 compounds were identified, including 14 arbutin derivatives, 33 hydrolyzable tannins, 6 flavanols, 26 flavonols, 9 saccharide derivatives, and glycosidic compounds. Those most reliably reproduced in all samples were quantified against respective standards. Arbutin (47-107 mg/g), 1,2,3,4,6-pentagalloylglucose (6.6-12.9 mg/g), and quercetin 3-galactoside/quercetin 3-glucoside (2.7-5.7 mg/g) were the most abundant phenolic components in the leaves. Quinic acid and ellagic acid were also detected at relatively high concentrations. The antioxidant activity of the most abundant compounds was evaluated. A critical view of the advantages and limitations of traveling wave IMS and CCS for the discovery of natural products is given.

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.