000170421 001__ 170421
000170421 005__ 20260420103355.0
000170421 0247_ $$2doi$$a10.1111/1541-4337.70474
000170421 0248_ $$2sideral$$a148957
000170421 037__ $$aART-2026-148957
000170421 041__ $$aeng
000170421 100__ $$aSong, Xue-Chao
000170421 245__ $$aIdentifying food packaging migrants: current analytical capabilities, challenges, and future prospects
000170421 260__ $$c2026
000170421 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170421 5203_ $$aThe 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.
000170421 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T53-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-128089OB-I00
000170421 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000170421 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170421 700__ $$aSu, Qi-Zhi
000170421 700__ $$0(orcid)0000-0003-2638-9221$$aCanellas, Elena$$uUniversidad de Zaragoza
000170421 700__ $$aLin, Qin-Bao
000170421 700__ $$aZhou, Yu
000170421 700__ $$0(orcid)0000-0003-2685-5739$$aNerin, Cristina$$uUniversidad de Zaragoza
000170421 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica
000170421 773__ $$g25, 3 (2026), [32 pp.]$$pCompr. Rev. Food. Sci. Food Saf.$$tCOMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY$$x1541-4337
000170421 8564_ $$s3340696$$uhttps://zaguan.unizar.es/record/170421/files/texto_completo.pdf$$yVersión publicada
000170421 8564_ $$s2396095$$uhttps://zaguan.unizar.es/record/170421/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170421 909CO $$ooai:zaguan.unizar.es:170421$$particulos$$pdriver
000170421 951__ $$a2026-04-18-10:49:15
000170421 980__ $$aARTICLE