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.

Migration of contaminants from printed masks for children to saliva simulant using liquid chromatography coupled to ion mobility-time of flight-mass spectrometry and gas chromatography-mass spectrometry.

The COVID-19 pandemic has led to children using polymeric FFP2 and polymeric surgical masks on a daily basis. Children often bite and suck on such masks as they wear them closed to their mouths. In this work, the migration of contaminants from printed and unprinted children`s masks to a saliva simulant has been studied. Liquid chromatography coupled to ion-mobility quadrupole time-of-flight mass spectrometry has been used for the detection and identification of non-volatile migrants. An orthogonal projection to latent structures - discriminant analysis (OPLS-DA) was applied to compare the data from the printed masks against the data from the unprinted ones. Headspace solid phase microextraction coupled to gas chromatography mass spectrometry was used to assess the migration of volatile compounds. Thirteen compounds were found in the masks with concentrations ranging from 5 ng/g to 254 ng/g. Toluene, chlorobenzene, irganox 1076 and 2-(2-butoxyethoxy)ethyl acetate were all found to migrate from the masks studied. Moreover, differences between the migrants from printed and unprinted FFP2 masks were found. Octocrylene, 4-(dimethylamine)benzoate, methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate and tris(3-methylphenyl)phosphate were found to migrate only from printed masks. Toluene that migrated from all the masks studied and tris(3-methylphenyl)phosphate, that migrated only from printed masks, have been listed as hazardous priority substances.

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.

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.

Ion-Mobility Quadrupole Time-of-Flight Mass Spectrometry: A Novel Technique Applied to Migration of Nonintentionally Added Substances from Polyethylene Films Intended for Use as Food Packaging.

Nontarget analysis of nonvolatile substances in complex samples is a very challenging task that requires powerful analytical techniques and experience of analyzing such samples. An extensive study was conducted in order to identify nonintentionally added substances (NIAS) migrating from 18 polyethylene (PE) samples intended to be in contact with food. The migration assays were performed in five simulants and analyzed by ultrahigh-performance liquid chromatography (UPLC) coupled to an ion-mobility separation (IMS) quadrupole-time-of-flight (QTOF) mass spectrometer. This experimental setup provides a novel and powerful tool for this type of nonvolatile and nontargeted analysis. Thirty-five compounds were identified, 17 of which were NIAS. Methyl and ethyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propanoate were found to be degradation products of either Irganox 1010 or Irganox 1076. Additionally, breakdown products including hexa-heptadecanamide, N,N'-1,2-ethanediylbis- and 11-eicosenamide were identified together with impurity reaction products, e.g., dibutyl amine or compounds of unknown origin like phosphine oxide, tributyl-. Forty-five percent of the detected compounds are in the positive list contained in Regulation 10/2011/EU, and their migration values were below their specific migration limits. The risk assessment for the rest of the compounds was carried out by comparing their migration values to the maximum concentration recommended by Cramer, e.g., ethyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propanoate and benzenepropanoic acid, 3,5-bis(1,1-dimethylethyl)-4-hydroxy-, 1,1'-[2,2-bis(hydroxymethyl)-1,3-propanediyl] ester (both class II toxicity), heptadecanamide, N,N'-1,2-ethanediylbis-, and phosphine oxide, tributyl- (both class III toxicity) were above the maximum concentration values for three samples that were migrated to ethanol 95%, and therefore, these samples are not suitable for food contact. The analytical tools and procedures used in this study are presented and discussed in detail.

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.

The detection and elucidation of oligomers migrating from biodegradable multilayer teacups using liquid chromatography coupled to ion mobility time-of-flight mass spectrometry and gas chromatography-mass spectrometry.

Biodegradable materials are increasingly being used in manufacturing processes due to their environmental benefits. In this work, a study has been performed to assess the migration of compounds from biodegradable multilayer teacups to a tea solution. Liquid chromatography in conjunction with ion-mobility quadrupole time-of-flight mass spectrometry has been used for the elucidation of non-volatile compounds. An orthogonal projection to latent structures-discriminant analysis has been carried out to compare the tea after migration against untreated tea used as blank. Headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry has been optimised to analyse the migration of volatile compounds. Eight migrants were identified in the tea, six of which were non-intentionally added oligomers. The degree of migration for hot tea ranged from 0.05 and 4.68 mg/kg, exceeding the specific migration limit. Nevertheless, the migration to cold tea was an order of magnitude lower (between 0.003 and 0.56 mg/kg).

Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.

The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such compounds. This study presents a workflow to identify nonvolatile migrates from FCMs based on liquid chromatography-ion mobility-high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. The applicability of this workflow was evaluated by screening the chemicals that migrated from polyamide (PA) spatulas. The number of candidate compounds was reduced by approximately 75% and 29% on applying RT and CCS prediction filters, respectively. A total of 95 compounds were identified in the PA spatulas of which 54 compounds were confirmed using reference standards. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.

A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials.

The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen (TWCCSN2) values for extractables and leachables from FCMs. The database contains a total of 1038 TWCCSN2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The TWCCSN2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H]+, [M + Na]+, and [M - H]- adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental TWCCSN2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The TWCCSN2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

The migration of NIAS from ethylene-vinyl acetate corks and their identification using gas chromatography mass spectrometry and liquid chromatography ion mobility quadrupole time-of-flight mass spectrometry.

An exhaustive migration study of eight corks, made of ethylene-vinyl acetate, was carried out to identify any non-volatile and volatile compounds using an untargeted approach. The challenge associated with the structural elucidation of unknowns was undertaken using both ultra-high-performance liquid chromatography coupled to an ion-mobility separation quadrupole-time of flight mass spectrometer and gas chromatography mass spectrometry. A total of fifty compounds were observed to migrate from the corks, and among these additives such as antioxidants (Butyl 4-hydroxybenzoate, Irganox 1010, Irganox 1075, Irgafos 168 and BHT) or lubricants (EBO and octadecanamide, N,N'-1,2-ethanediylbis-) were identified. A high proportion (84%) of the detected compounds was non-intentionally added substances (NIAS), and included several cyclic oligomers with different chain sequences. NIAS, such as 2,6-bis(1,1-dimethylethyl)-4-ethyl and 7,9-ditert-butyl-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione, break-down products, including hexa-, hepta- and nonadecanamide, N,N'-1,2-ethanediylbis-, and oxidation products were also identified. One cork was found to be unsuitable for use as a food contact material.

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 (TWCCSN2) 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.

The application of ion mobility time of flight mass spectrometry to elucidate neo-formed compounds derived from polyurethane adhesives used in champagne cork stoppers.

Polyurethane adhesives are used to bond agglomerated cork and natural disk cork to produce cork stoppers that are used in champagne bottles. These adhesives are manufactured by reacting polyols with an excess of diisocyanates. Isocyanates are highly reactive compounds that have a propensity to form non-intentionally added substances (NIAS) in the end product. In this work, ion mobility-time of flight-mass spectrometry was used to elucidate such NIAS, through the comparison of accurate mass spectra with the fragmentation patterns of proposed candidates. Twelve neo-formed compounds, including amines, amides and urethanes, resulting from the reaction of isocyanates with acetic acid and ethanol used as food simulants, were identified. Additionally, markers from champagne vs. champagne after its exposure to the adhesive were investigated using the supervised multivariate analysis method of Orthogonal Projection to Latent Structures - Discriminant Analysis. Four neo-formed compounds, resulting from the reaction of diisocyanates with malic acid or tartaric acid contained in the champagne, were identified for the first time in this work. All of the compounds identified were subsequently quantified using ultra-high pressure liquid chromatography coupled to a triple quadrupole mass spectrometer. Limits of detection were below 5 µg/kg in the food simulants and below 30 µg/kg in champagne samples. Migration levels ranged from 70 to 721 µg/kg, with most of them exceeding the specific migration limit established for Cramer class III compound (90 µg/kg).

The use of ion mobility time-of-flight mass spectrometry to assess the migration of polyamide 6 and polyamide 66 oligomers from kitchenware utensils to food.

Oligomers, are, in general, unknown components of the polymer. These oligomers can migrate from the polymer into the food and become a non-intentionally added substance to the food. In this work, ion mobility time-of-flight mass spectrometry has been used to identify oligomers migrating from kitchenware. The structure elucidation of oligomers from polyamide 6 and polyamide 66 was achieved through the analysis of accurate m/z values of adducts and collision cross section values of precursor ions together with high-energy fragmentation patterns. Additionally, a method to extract oligomers from sunflower oil, cooked beans, soup and whole milk has been developed. Extraction recoveries ranged from 87 to 102% and limits of detection were from 0.03 to 0.11 mg/kg. It was observed that the migration from kitchenware to real food was below the specified migration limit of 5 mg/kg. However, this limit was exceeded for food simulants, which therefore overestimated the oligomer migration.

Profiling of the known-unknown Passiflora variant complement by liquid chromatography - Ion mobility - Mass spectrometry.

Liquid Chromatography - Ion Mobility - Mass Spectrometry (LC-IM-MS) was utilized for non-targeted screening analysis to understand the variance in the composition of Passiflora species. Multivariate analysis was employed to explore a chemometric processing strategy for IM based Passiflora variant differentation. This approach was applied to the comparative analyses of extracts of the medicinal plants Passiflora alata, Passiflora edulis, Passiflora incarnata and Passiflora caerulea. In total, 255 occurrences of IM-MS resolved coeluting marker isomers and isobaric species were detected, providing increased coverage and specificity of species component markers compared to conventional LC-MS. A large proportion of medical plant phytochemical analysis information often remains redundant in that it is not phenotypic specific. Here, generation of Passiflora variant 'known-unknown' libraries has been used to compare Passiflora species to investigate unique variant features. Investigations of predicted collision cross section have enabled comparison of an element of the 'known-unknown' IM isomeric complement to be performed, facilitating a reduction in the number of possible variant unique isomeric identifications. In combination with spectral interpretation, it has been possible to resassign isomeric 'known-unknowns' as 'knowns'. The strategies employed illustrates the potential to facilitate identification of medicinal plant phytochemical components.

Comprehensive LC-MS E lipidomic analysis using a shotgun approach and its application to biomarker detection and identification in osteoarthritis patients.

A fast and robust method for lipid profiling utilizing liquid chromatography coupled with mass spectrometry has been demonstrated and validated for the analysis of human plasma. This method allowed quantification and identification of lipids in human plasma using parallel alternating low energy and high energy collision spectral acquisition modes. A total of 275 [corrected] lipids were identified and quantified (as relative concentrations) in both positive and negative ion electrospray ionization mode. The method was validated with five nonendogenous lipids, and the linearity (r(2) better than 0.994) and the intraday and interday repeatability (relative standard deviation, 4-6% and 5-8%, respectively) were satisfactory. The developed lipid profiling method was successfully applied for the analysis of plasma from osteoarthritis (OA) patients. The multivariate statistical analysis by partial least-squares-discrimination analysis suggested an altered lipid metabolism associated with osteoarthritis and the release of arachidonic acid from phospholipids.

Ion mobility quadrupole time-of-flight high resolution mass spectrometry coupled to ultra-high pressure liquid chromatography for identification of non-intentionally added substances migrating from food cans.

Sealants, incorporated in the lids of food cans to ensure the can is hermetically sealed, are formulated from a wide variety of compounds. These compounds and associated non-intentionally added substances (NIAS) could migrate to the food contained in the can. In this work, ion mobility quadrupole time-of-flight mass spectrometry coupled to ultra-high performance liquid chromatography (UHPLC-IM-QTOF-MS) has been used to obtain ion mobility filtered extracted ion chromatograms. Subsequently, accurate mass precursor ions and their fragments have been used to identify the compounds migrating from the sealant to the content of the cans. Moreover, the correlation between the collision cross-section (CCS) values and m/z of the compounds was used to increase the level of confidence of the identification. Seven compounds were found to have migrated to the food simulants. The compounds bis(2-hydroxy-3-tert-butyl-5-methylphenyl)dicyclopentane,1-tetradecanesulfonic acid, 1-pentadecanesulfonic acid, 1-hexadecanesulfonic acid and naphthalene-2-sulfonic acid (whose migration was over the specific migration limit established by the European Regulation 10/2011/EU) were identified as NIAS in the food simulants studied.

Generic dealkylation: a tool for increasing the hit-rate of metabolite rationalization, and automatic customization of mass defect filters.

The use of exact mass liquid chromatography/mass spectrometry (LC/MS) for drug metabolism studies has increased significantly in recent years. Firstly, exact mass measurements facilitate identification of standard biotransformations through the use of narrow window extracted ion chromatograms, which are typically highly selective relative to signals from matrix or dosing components. Secondly, novel metabolites can be characterized via elemental formula calculations and high-resolution product ion spectra. Furthermore, biological background ions can be removed by the use of mass defect filters (MDFs) which filter out ions based on the decimal component of their m/z value. Here, we describe an approach which we term 'generic dealkylation' that in association with other data interpretation tools adds significant value to the assignment process. Generic dealkylation uses a simple strategy to identify those bonds which have the potential to be cleaved by metabolism. In combination with standard phase 1 and phase 2 biotransformations, this allows creation of a chemically intelligent MDF which balances the need to remove matrix background with the requirement of avoiding filtering true metabolites. Secondly, generic dealkylation increases the hit-rate at which non-trivial (i.e. not covered by simple phase 1 oxidations or direct phase 2 conjugations) metabolites can be directly rationalized. The value of the generic dealkylation approach is illustrated by its application to determination of in vitro metabolic routes for two commercial drugs, nefazodone and indinavir.