Ion Mobility-High-Resolution Mass Spectrometry (IM-HRMS) for the Analysis of Contaminants of Emerging Concern (CECs): Database Compilation and Application to Urine Samples

et al. 2022

J. Agric. Food Chem.

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 ( TW CCS N2 ) values for extractables and leachables from FCMs. The database contains a total of 1038 TW CCS N2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The TW CCS 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 TW CCS N2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The TW CCS N2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.

Section: Resultsmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

J. Agric. Food Chem.

“…IMS-QTof has proved to be a powerful tool for the analysis of small molecules. ,− The relatively high mass resolving power coupled to an ion mobility separation dimension increases peak capacity and can separate coeluting compounds. This is demonstrated in Figure , which should make clear the separation of the coeluting compounds quercetin 3-glucoside and rutin.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

J. Agric. Food Chem.

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 ( TW CCS 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 3glucoside (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.

“…A total of 2145 experimental traveling wave CCS ( TW CCS N2 ) and drift tube CCS ( DT CCS N2 ) values were collected from seven recent publications, 27 , 29 , 38 , 39 , 43 , 53 , 54 of which 1425 and 720 CCS values were for [M + H] + and [M + Na] + ions, respectively ( Table S1 ). The CCS values in the publication of Song and co-workers 43 were experimentally measured by injecting standards of chemicals associated with plastic food packaging.…”

Section: Methodsmentioning

confidence: 99%

“… 55 Additionally, many pesticides contain halogens in their structure, as such, the predictions of CCS values for halogenated compounds will be more accurate by including the pesticides in the CCS data set. The last two publications 27 , 54 mainly contain CCS values for organophosphorus flame retardants, compounds with a phosphate structure, which are common additives used in plastic materials. Since only three organophosphorus flame retardants were included in the previous self-built CCS database, 43 the addition of the CCS values from these two publications significantly expanded the chemical diversity of the current study.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

Environ. Sci. Technol.

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.