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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.

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A wide variety of adhesives can be used to manufacture multilayer food packaging materials. Since these materials are usually in direct contact with the packed food, compounds from the adhesive may migrate into it. Therefore it is important to determine the composition of the adhesives used. The main aims of this work were to determine the compounds present in the adhesives used in the food packaging, to study their migration to food simulants and finally to use these data to test a mathematical tool designed for predicting migration to food from laminates containing adhesives. For this purpose a total of 45 market samples of multilayer materials (laminates and other glued materials) produced with 29 different adhesives were studied. A total of 55 different compounds were detected in these adhesives, 57% of these compounds migrated into a dry food simulant (Tenax®) through the food contact layer. These data were also used to compare it with the theoretically estimated migration of the adhesive compounds using "upper-bound reference" values for the diffusion and partition coefficients implied in a multilayer migration model. In 93% of the cases the estimated migration results met or exceeded the experimental ones. This is an important requirement for testing the applicability of these theoretical migration estimations for compliance decisions which aim to protect the consumer's health

The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.