Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials

Comprehensive profiling and development of a collision cross section database for milk oligosaccharides via orthogonal UPLC-cyclic ion mobility-mass spectrometry system

Human milk oligosaccharides (HMOs) have attracted immense interest in the infant formula industry for their health benefits. Herein, we utilized liquid chromatography-cyclic ion mobility-mass spectrometry (LC-cIM-MS) to develop a robust and multidimensional HMO profiling workflow. This workflow relies on a self-built glycan library, allowing high-throughput searching of oligosaccharides. cIM-MS demonstrated high resolving power in discriminating glycan isomers and increasing peak capacity. This also facilitated the accurate elucidation of most oligosaccharides at sequence levels. A remarkably diverse milk oligosaccharide profile (n = 98) was observed and enabled the discovery of distinctive chromatographic retention patterns. To provide supplementary selectivity for future routine assignment in the absence of standards, we further developed a comprehensive database of experiment-derived traveling wave collision cross section in nitrogen (TWCCSN2) for 98 HMOs, including isomer-resolved TWCCSN2 values. Finally, the profile revealed 64 oligosaccharides unique to human milk compared with infant formula, indicating the potential ingredients for formula improvement.

Determination of Collisional Cross Section Using Microscale High-Field Asymmetric Waveform ion Mobility Spectroscopy-Mass Spectrometry (FAIMS-MS)

RATIONALE

Collisional cross sections (CCS) are an important characteristic of gas-phase ions that are measured using ion mobility-mass spectrometry (IMS). Typically, CCS measurements are performed with drift-tube IMS or travelling-wave IMS. However. in a high-field asymmetric waveform ion mobility (FAIMS) device, ion heating effects make CCS determination more challenging. This research explores whether CCS can be predicted with microscale FAIMS by using known CCS standards.

METHODS

An Owlstone ultraFAIMS microscale FAIMS spectrometer was coupled to an Orbitrap Exactive mass spectrometer. Two different CCS standard mixtures (tetraalkylammonium halides [TAAHs] and poly-DL-alanine oligomers) were used to evaluate the system's potential to determine CCS. Test peptide bradykinin acetate and substance P were used to evaluate CCS determination accuracy for singly and doubly charged peptide species using external calibration with a series of poly-DL-alanine peptides for +1, +2 charge states.

RESULTS

Calibrations with excellent correlation coefficients (R2 = 0.99) for both TAAHs and poly-DL-alanine were obtained. Good accuracy of determination was achieved for bradykinin [M + 2H]2+ with a ± 0.5% difference between experimental and published CCS at a dispersion field (DF) strength of 250 Td; the model proved less accurate for bradykinin [M + H]+ (±1.4% at 240 Td). The accuracy of determination for the [M + H]+ and [M + 2H]2+ ions of substance P was within ± 5% and ± 3% at 250 Td, respectively, while at higher DF values, accuracy decreased to approximately 5%.

CONCLUSIONS

Distinct relationships were observed between CCS and transmission CF with both calibrants. Optimum accuracy was obtained at DF 240-260 Td. At lower DF, accuracy is reduced by insufficient resolution of analyte ions from solvent cluster adducts, while at higher DF values, poor transmission becomes a factor. Nevertheless, these data suggest microscale FAIMS can conduct CCS measurements with reasonable accuracy when the compound being measured has similar structural features to the CCS standards used.

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.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies

Ion mobility mass spectrometry (IM-MS) techniques have become highly valued as a tool for structural characterization of biomolecular systems since they yield accurate measurements of the rotationally averaged collision cross-section (CCS) against a buffer gas. Despite its enormous potential, IM-MS data interpretation is often challenging due to the conformational isomerism of metabolites, lipids, proteins, and other biomolecules in the gas phase. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS data. In this work, we present MassCCS, a parallelized open-source code for computing CCS of molecules ranging from small organic compounds to massive protein assemblies at the trajectory method level of description using atomic and molecular buffer gas particles. The performance of the code is comparable to other available software for small molecules and proteins but is significantly faster for larger macromolecular assemblies. We performed extensive tests regarding accuracy, performance, and scalability with system size and number of CPU cores. MassCCS has proven highly accurate and efficient, with execution times under a few minutes, even for large (84.87 MDa) virus capsid assemblies with very modest computational resources. MassCCS is freely available at https://github.com/cces-cepid/massccs.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Chemical contaminants from food contact materials and articles made from or containing wood and bamboo - a review

Due to recently introduced 'so-called' bio- and plant-based friendly food contact materials and articles (FCM/FCA), some neglected safety issues need to be raised. In this review, potential chemical contaminants from FCM/FCA made from or containing wood and bamboo are presented. Sources, migration, and analytical issues in determining contaminants including intentionally and non-intentionally added substances (IAS and NIAS, respectively) are reviewed. Most of the contaminants are components from melamine-formaldehyde-resin (MFR), paints and coatings, preservatives, and bleaching agents. Tableware made of MFR containing bamboo fibres as a filler are not always suitable for use as tableware since harmful amounts of melamine and formaldehyde can migrate from the tableware into food and even accelerate the degradation of certain polymers with which they are mixed. In addition, in the EU bamboo in plastic FCM is not authorized under Regulation (EU) 10/2011. Paints and coatings used to provide surface coverage for bamboo and wooden articles also pose a risk of migration of heavy metals. Limits on preservatives in wood FCM are covered by legislation in many countries, nevertheless their contamination should not be ignored. Some wood species are considered 'toxic' or contain 'toxic' constituents that should not be used in contact with food, which are worth considering for legislation. IAS analyses in bamboo and wooden FCM is generally not a problem, but has proven to be more challenging for NIAS. Due to a complex mixture of substances contained in plant-based materials, there is a need to improve databases for non-target screening of such chemicals.

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.

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.

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 (^TWCCS_N2) values for extractables and leachables from FCMs. The database contains a total of 1038 ^TWCCS_N2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The ^TWCCS_N2 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 ^TWCCS_N2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The ^TWCCS_N2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.