Analysis of Packaging Materials using a Mass Spectral Based Chemical Sensor

Importance of the Topic

Packaging materials directly affect food and consumer products and must meet strict odor and flavor standards to avoid contamination. Reliable, fast and objective screening methods are essential for quality control on the production floor.

Study Objectives and Overview

We evaluated a mass spectral based chemical sensor (ChemSensor) coupled with multivariate data analysis to detect off-odors in packaging papers and polyethylene (PE), and to quantify trace levels of tri(propylene glycol) diacrylate (TPGDA) in beverage cartons. Three applications were investigated: specialty paper classification, PE granulate screening, and TPGDA detection in milk and orange juice cartons.

Methodology and Used Instrumentation

A static headspace sampling approach with an Agilent mass selective detector and GERSTEL Headspace ChemSensor was employed. Key steps included sample incubation (20–30 min at 80–120 °C), injection with split ratios, and mass spectrometry in scan or selected ion monitoring (SIM) mode. Multivariate models (SIMCA and k-nearest neighbors) were built using characteristic mass spectral fingerprints.

Main Results and Discussion

For specialty papers, PCA-based SIMCA and kNN models separated low-odor and high-odor classes with interclass distances above 8, confirming clear discrimination. PE granulate samples were classified with over 95 % accuracy compared to sensory panels, although extreme outliers required exclusion or adjusted probability thresholds. In TPGDA analysis using SIM mode, ions at m/z 55 and 113 provided selective detection of ppb-level residues, while additional stir bar sorptive extraction tests highlighted carton variability.

Benefits and Practical Applications

• Fast analysis (1 min runs) without chromatographic separations.
• Objective classification reduces reliance on subjective sensory panels.
• On-line screening potential for production environments.

Future Trends and Potential Applications

Integration of solid-phase microextraction or sorptive enrichment techniques may increase sensitivity for trace contaminants. Expanded chemometric libraries could support broader polymer types and flavor-active compounds. Miniaturized MS sensors promise further process-inline monitoring.

Conclusion

The mass spectral based ChemSensor offers a rapid, reliable alternative to GC/MS for quality control of packaging materials, enabling accurate odor classification and trace compound detection in production settings.

Used Instrumentation

• GERSTEL Headspace ChemSensor System (static headspace interface).
• Agilent 5973 Mass Selective Detector with ChemStation software.
• Stir bar sorptive extraction (SBSE) accessories for enrichment tests.

References

• Kolb B, Ettre LS. Static Headspace-Gas Chromatography Theory and Practice. Wiley-VCH; 1997.
• Marek T, Grollmann U. DIC Technical Review. 1999;5:93.