Identifying Food Packaging Migrants: Current Analytical Capabilities, Challenges, and Future Prospects

背景(考古学) 工作流程 鉴定(生物学) 生化工程 质谱法 优先次序 计算机科学 风险分析(工程) 稀缺 工艺工程 食品包装 气相色谱法 食品安全 环境科学 化学 资源(消歧) 食品接触材料 纳米技术 分类 数据科学 二维气体 分析技术 表征(材料科学) 系统工程 食物链 食品
作者
Xue‐Chao Song,Qi‐Zhi Su,Elena Canellas,Qin‐Bao Lin,Yu Zhou,Cristina Nerín
出处
期刊:Comprehensive Reviews in Food Science and Food Safety [Wiley]
卷期号:25 (3): e70474-e70474
标识
DOI:10.1111/1541-4337.70474
摘要

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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