Identification of Non-Intentionally Added Substances (NIAS) in Food Contact Materials Using APGC-Xevo G2-XS QTof and UNIFI Software

Importance of the Topic

Migration of substances from food contact materials can lead to exposure to both intentionally added substances and non-intentionally added substances (NIAS). Analytical screening and structural elucidation of NIAS are essential to safeguard public health, ensure compliance with regulatory limits, and maintain food quality.

Study Objectives and Overview

This work demonstrates the use of atmospheric pressure gas chromatography coupled with high-resolution quadrupole time-of-flight mass spectrometry (APGC-Xevo G2-XS QTof) and UNIFI software for non-targeted identification of NIAS in starch-based biopolymer packaging extracts. The main goals were to:

• Compare APGC-QTof performance against conventional EI-single quadrupole GC-MS.
• Apply UNIFI workflows (Binary Compare, componentization, elucidation, discovery) to streamline unknown compound identification.

Methodology and Instrumentation

Samples of starch-based biopolymer pellets were extracted ultrasonically with methanol and concentrated prior to GC injection. Key analytical parameters included:

• GC: DB-5MS column, pulsed splitless injection, oven program from 50 °C to 300 °C.
• MS (APGC): dry nitrogen charge transfer, MS^E acquisition over 50–650 m/z, corona current 2.2 µA, make-up gas N₂ at 300 °C.
• Comparative EI-MS: 70 eV ionization source with single quadrupole detector.
• Data analysis: UNIFI Scientific Information System featuring Binary Compare, 3D peak detection, Elemental Composition and Discovery tools.

Main Results and Discussion

• APGC-QTof detected a higher number of peaks than EI-MS, attributable to softer ionization and enhanced sensitivity.
• Binary Compare distinguished sample-specific NIAS from blank contaminants, even at trace levels.
• High-accuracy mass measurements and MS^E fragments confirmed known NIAS (e.g., cyclic adipate) and corrected misassignments from EI library matches.
• Elemental Composition and UNIFI componentization revealed molecular formulas and proposed fragmentation pathways for unknown peaks.
• Discovery workflows enabled identification of previously undetectable NIAS such as beta-tocotrienol.

Benefits and Practical Applications

The combination of APGC-QTof and UNIFI enhances the reliability of NIAS screening by reducing false positives, extending analyte coverage, and streamlining data review. This approach supports migration testing, risk assessment and quality control of food contact materials under EU regulations.

Future Trends and Applications

• Routine adoption of HRMS-APGC platforms in regulatory and industrial laboratories.
• Expansion of compound libraries with NIAS-specific spectral entries.
• Incorporation of machine learning algorithms for accelerated unknown identification.
• Extension of workflows to other complex matrices beyond packaging.

Conclusion

APGC-Xevo G2-XS QTof combined with UNIFI software offers a powerful, sensitive and versatile solution for comprehensive non-targeted analysis of NIAS in food contact materials, surpassing traditional EI-MS in both detection capability and confidence of structural assignments.

References

1. Regulation (EU) No 10/2011 of 14 January 2011 on plastic materials and articles intended to contact food. Official Journal of the European Union.
2. Clemente I, Aznar M, Nerin C, Bosetti O. Migration from printing inks in multilayer food packaging materials and pattern recognition with chemometrics. Food Addit Contam Part A. 2016.
3. Canellas E, Vera P, Nerin C. UPLC-ESI-Q-TOF-MS^E and GC-MS identification and quantification of NIAS from biodegradable packaging. Anal Bioanal Chem. 2015.
4. Felix JS, Isella F, Bosetti O, Nerin C. Analytical tools for identification of NIAS from polyurethane adhesives in multilayer packaging materials. Anal Bioanal Chem. 2012.
5. Yan DD, Wong YF, Tedone L, Shellie RA, Marriott PJ, Whittock SP, Koutoulis A. Chemotyping of new hop genotypes using GC×GC-QTof. J Chromatogr A. 2017.