ArticleJournal of agricultural and food chemistry2022
Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools.
Article in Journal of agricultural and food chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Identifying Food Packaging Migrants: Current Analytical Capabilities, Challenges, and Future Prospects.Comprehensive reviews in food science and food safety · 2026Review
- Evaluating the chemical safety of bio-based and biodegradable food packaging materials through non-targeted LC-HRMS analysis and in vitro bioassays.Analytical and bioanalytical chemistry · 2026Article
- Reference library for suspect screening of environmental toxicants using ion mobility spectrometry-mass spectrometry.Communications chemistry · 2025Article
- Potential mammary carcinogens used in food contact articles: implications for policy, enforcement, and prevention.Frontiers in toxicology · 2024Article
- Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants.Environmental science & technology · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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