DENTIFYING NON-INTENTIONALLY ADDED SUBSTANCES (NIAS) IN FOOD CONTACT MATERIALS

Significance of the Topic

Non intentionally added substances NIAS in food contact materials present potential health risks that require comprehensive screening in quality control and research laboratories.

Aims and Overview of the Study

This work demonstrates the application of atmospheric pressure gas chromatography APGC combined with high resolution mass spectrometry and an automated structural elucidation workflow to detect and identify unknown volatile and semi volatile compounds in starch based biopolymer packaging.

Methodology and Used Instrumentation

Sample preparation involved ultrasonic extraction of biopolymer pellets with methanol followed by concentration under nitrogen.
Chromatographic and mass spectrometric analysis was performed in parallel using electron ionization EI and APGC soft ionization with MSE acquisition.

• Gas chromatograph Agilent 7890A with 7683B autosampler and DB-5MS column
• EI quadrupole mass spectrometer Agilent 5975B
• APGC source coupled to Waters Xevo G2 XS QTof operated in sensitivity mode
• UNIFI Scientific Information System for data processing and structural elucidation

Main Results and Discussion

APGC detected a significantly higher number of peaks compared to EI due to its higher sensitivity and softer ionization. Soft ionization preserved molecular ions enabling accurate mass based formula determination.

Key identifications included 1 6 dioxacyclododecane 7 12 dione and 1 2 3 4 tetrahydro 1 6 dimethyl 4 1 methylethyl naphthalene corrected from tentative EI matches. A third component was identified as e tocotrienol through fragment elucidation workflows. Automated componentization in UNIFI cleaned and aligned low and high collision energy spectra facilitating confident assignments.

Benefits and Practical Applications

Implementation of APGC QTof with UNIFI delivers:

• More comprehensive detection of NIAS at trace levels
• Improved confidence in structural assignments
• Reduction of manual data interpretation and false positives
• Complementary data to conventional EI MS screening

Future Trends and Potential Applications

Advances may include integration of ion mobility separation machine learning for spectral annotation expansion to non GC amenable NIAS and miniaturized sampling for in situ screening. Automation of workflows will further enhance throughput in industrial quality control.

Conclusion

The combined use of APGC soft ionization high resolution QTof and automated structural elucidation provides a powerful platform for screening and identifying non intentionally added substances in food contact materials overcoming limitations of classical EI based approaches.

References

Dreolin N Burt O Lai S Hancock P Identifying Non intentionally Added Substances in Food Contact Materials Waters Corporation 2018