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

Abstract

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.

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

“…46 A similar reduction in false positives has been reported in other publications employing different CCS prediction tools. 70,71,125,154 The number of false positives eliminated by applying a CCS filter is dependent on the CCS tolerance used. A smaller CCS tolerance can eliminate more false positive candidates but also increase the risk of filtering out correct identifications.…”

“…A total of 35 compounds were identified, 17 of which were NIAS. Song et al used LC–IMS–QTOF, together with RT and CCS prediction tools, to develop a workflow for the identification of nonvolatile compounds migrating from plastic food contact materials; the authors stated that the use of predicted RT and CCS values can reduce the number of false positives in SSA. Wrona and co-workers used a LC–IMS–QTOF platform to study dishes made from biomaterials .…”

“…Bijlsma and co-workers showed that using predicted CCS values, with a tolerance of 6%, reduced the number of false positives by 5%–39% . A similar reduction in false positives has been reported in other publications employing different CCS prediction tools. ,,, …”

“…Several studies − ,,,,, have used LC–IMS–MS/MS platforms to characterize plastic-related chemicals in consumer products and environmental samples. Vera et al combined LC–IMS–QTOF with NTA to identify the nonvolatile substances migrating from polyethylene films used as food packaging.…”

“… 46 A similar reduction in false positives has been reported in other publications employing different CCS prediction tools. 70 , 71 , 125 , 154 …”