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

Significance of the Topic

The presence of non-intentionally added substances (NIAS) in food contact materials (FCMs) poses a potential health risk due to migration into food. Regulatory frameworks such as EU Regulation 10/2011 require identification and control of both intentionally added substances (IAS) and NIAS. Non-targeted screening combined with high-resolution mass spectrometry is critical for comprehensive safety assessment of packaging materials, especially when substances are not present in spectral libraries or occur at trace levels.

Objectives and Study Overview

This application note demonstrates a robust workflow for screening, structural elucidation, and confirmation of NIAS in a novel starch-based biopolymer using Atmospheric Pressure Gas Chromatography (APGC) coupled to a Xevo G2-XS QTof mass spectrometer and UNIFI software. A direct comparison with traditional electron ionization (EI) GC-MS evaluates sensitivity and confidence in compound identification.

Instrumentation

• Waters APGC source with nitrogen make-up gas
• Waters Xevo G2-XS QTof mass spectrometer in sensitivity mode
• Agilent 7890A GC with 7683B autosampler and DB-5MS column (30 m × 0.25 mm × 0.25 µm)
• UNIFI Scientific Information System for data acquisition and processing

Methodology

Starch-based biopolymer pellets were extracted three times with methanol via ultrasonication at 40 °C. The combined extract was concentrated and injected (1 µL) under splitless pulsed conditions. Chromatographic separation used a 50 °C–300 °C oven program. APGC-QTof data were acquired in MS E mode, collecting accurate masses for precursor and fragment ions. A blank extract was analyzed in parallel, and UNIFI’s Binary Compare feature distinguished sample-specific components.

Main Results and Discussion

APGC-QTof detected a greater number of chromatographic peaks compared with EI-single quadrupole GC-MS, reflecting higher sensitivity and softer ionization. Binary Compare rapidly flagged unique sample components. Known compounds such as 1,6-Dioxacyclododecane-7,12-dione were confirmed by matching accurate mass of molecular and protonated ions within sub-ppm error. A misidentified EI peak (m/z 232.1817) was correctly assigned to a substituted naphthalene derivative via UNIFI’s Elemental Composition and Elucidation tools using isotope pattern matching and fragment logic. APGC-QTof also revealed a previously undetected peak at Rt 27.3 min, identified as beta-tocotrienol through UNIFI’s Discovery workflow and database searching.

Benefits and Practical Applications

• Enhanced detection of trace NIAS beyond spectral library limitations
• Accurate mass confirmation reduces false positives and misassignments
• Integrated UNIFI workflows (Binary Compare, Elemental Composition, Discovery) streamline data review and candidate selection
• Soft ionization by APGC preserves molecular ions for unambiguous formula determination

Future Trends and Applications

Further integration of ion mobility separation, comprehensive two-dimensional GC, and expanded spectral databases will enhance NIAS identification in complex matrices. Advances in software-driven automation and machine learning may enable real-time risk assessment of FCMs, supporting regulatory compliance and rapid screening in industrial quality control.

Conclusion

APGC-Xevo G2-XS QTof combined with UNIFI software offers a powerful platform for non-targeted screening and structural elucidation of NIAS in food packaging materials. Enhanced sensitivity, accurate mass measurement, and customizable workflows enable confident identification of known and novel migrants, improving safety evaluation of FCMs.

References

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