ReviewComprehensive reviews in food science and food safety2026
Identifying Food Packaging Migrants: Current Analytical Capabilities, Challenges, and Future Prospects.
Review in Comprehensive reviews in food science and food safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
Funding
Abstract
The migration of intentionally and non-intentionally added substances (IAS/NIAS) from food packaging into foodstuffs presents a significant challenge to consumer health and food safety. Accurate and comprehensive identification of these chemical migrants is therefore paramount. This review systematically summarizes recent advances in the analytical workflows used to identify these migrants. We critically evaluate the latest developments in both gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Special attention is given to cutting-edge techniques, such as comprehensive two-dimensional gas chromatography (GC × GC) for enhanced separation of complex mixtures, high-resolution filtering (HRF) for leveraging the dual advantages of gas chromatography coupled to high-resolution mass spectrometry (GC-HRMS) accurate mass measurements and conventional low-resolution spectral matching, and ion mobility spectrometry (IMS) for its unique ability to resolve isomers. Concurrently, we provide an in-depth critique of the evolving data analysis strategies, from conventional targeted analysis to the more comprehensive suspect and nontargeted screening approaches. The principles, advantages, and limitations of each workflow are discussed in the context of their application to food packaging materials. Then, the review dissects major bottlenecks, notably the scarcity of reference standards and comprehensive mass spectral libraries, which hinder confident identification. Looking forward, we highlight promising future directions, emphasizing that the synergistic integration of open-access mass spectral databases, adoption of novel analytical techniques, and machine learning-based molecular property prediction will facilitate the identification of IAS and NIAS in food packaging. In addition, integrating chemical analysis with bioassays will enable the prioritization of high-hazard chemicals, ultimately improving the safety evaluation of food packaging.
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