Determination of Aqueous Film Forming Foam (AFFF) Composition Using a Multivariate Analysis Approach in UNIFI Scientific Information System

Significance of the Topic

Aqueous film-forming foams (AFFFs) are critical in firefighting but contain complex mixtures of fluorinated and hydrocarbon surfactants that can migrate into the environment. Detailed compositional analysis supports environmental forensics, contaminant tracking, and risk assessment.

Study Objectives and Overview

This study aimed to differentiate seven industrial AFFF formulations using a non-targeted, data-independent acquisition approach combined with multivariate analysis (MVA). The workflow leverages UNIFI Scientific Information System and integrated EZ Info tools to identify unique chemical markers among AFFF samples and pooled composites.

Methodology

Samples of seven AFFFs were diluted in methanol and randomized injections were analyzed by UPLC-MS with alternating low and high collision energy (MSᵉ). Both positive and negative electrospray ionization modes were acquired. Data processing included principal component analysis (PCA) and supervised pairwise comparisons generating S-plots to highlight markers specific to individual formulations. Structural elucidation involved elemental composition calculation, isotopic pattern matching, and fragment ion analysis.

Used Instrumentation

• UPLC System: ACQUITY UPLC I-Class with BEH C18 column (2.1×50 mm, 1.7 µm)
• Mass Spectrometer: Xevo G2-XS QTof operated in MSᵉ mode
• Ionization: Electrospray positive and negative modes
• Software: UNIFI 1.9 with EZ Info 3.0 and Discovery Toolset

Key Results and Discussion

Quality control injections of known standards confirmed mass accuracy under 5 ppm and retention time stability. PCA score plots revealed clear clustering: most AFFFs grouped together while one formulation (AFFF3) was distinctly separated in both polarities. Loadings plots identified exact mass–retention time pairs (markers) driving sample differentiation. S-plots from pairwise comparisons flagged markers strongly associated with specific foams. Subsequent structural elucidation proposed sulfate, hydrocarbon, and fluorinated species as unique markers, supported by fragment ion matching.

Advantages and Practical Applications

The described approach enables rapid, non-targeted screening of complex mixtures, identification of formulation-specific markers, and streamlined data review within a single software environment. It supports environmental monitoring, forensic source tracing, and product quality control.

Future Trends and Potential Applications

Advances in chemometric algorithms and high-performance computing will enhance discrimination of increasingly complex matrices. Integration with machine learning models and expanded spectral databases will improve marker identification. High-throughput workflows may be extended to other environmental matrices and emerging contaminants.

Conclusion

The combined use of MSᵉ acquisition, multivariate analysis, and discovery tools within UNIFI provides a powerful framework to characterize AFFF compositions. This strategy offers efficient identification of unique chemical markers and facilitates environmental forensics and contaminant tracking.

References

1. Rotander A, Kärrman A, Toms LM, Kay M, Mueller J, Ramos MJG. Environmental Science & Technology Letters. 2015;49(4):2434–2442.