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

Xue-Chao Song1, Qi-Zhi Su2, Elena Canellas3

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Summary
This summary is machine-generated.

Identifying chemical migrants from food packaging is crucial for consumer safety. This review explores advanced analytical methods and data strategies for detecting intentionally and non-intentionally added substances (IAS/NIAS).

Keywords:
food packaging materialshigh‐resolution mass spectrometryidentificationmigrationnontargeted analysis

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Area of Science:

  • Analytical Chemistry
  • Food Safety Science
  • Materials Science

Background:

  • Chemical migration from food packaging poses risks to consumer health.
  • Accurate identification of intentionally and non-intentionally added substances (IAS/NIAS) is essential for food safety.
  • Current analytical methods face challenges in comprehensively identifying diverse chemical migrants.

Purpose of the Study:

  • To systematically review and critically evaluate recent advancements in analytical workflows for identifying chemical migrants from food packaging.
  • To discuss evolving data analysis strategies, from targeted to non-targeted screening.
  • To identify bottlenecks and propose future directions for improved migrant identification.

Main Methods:

  • Review of recent literature on analytical techniques for food packaging migrants.
  • Critical evaluation of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-high-resolution mass spectrometry (LC-HRMS).
  • Assessment of advanced techniques like comprehensive two-dimensional GC (GC×GC), high-resolution filtering (HRF), and ion mobility spectrometry (IMS).

Main Results:

  • Recent analytical workflows show enhanced capabilities for migrant identification.
  • Advanced techniques like GC×GC, HRF, and IMS offer improved separation and isomeric resolution.
  • Data analysis strategies are evolving towards more comprehensive screening approaches.
  • Scarcity of reference standards and spectral libraries remains a significant bottleneck.

Conclusions:

  • Synergistic integration of analytical techniques, databases, and machine learning is promising for future IAS/NIAS identification.
  • Combining chemical analysis with bioassays can help prioritize hazardous chemicals.
  • Improved analytical strategies are vital for enhancing the safety evaluation of food packaging materials.