Efficient Data Analysis with Peakintelligence - Application for Analyzing Per- and Polyfluoroalkyl Substances (PFAS)

Importance of the Topic

Per- and polyfluoroalkyl substances (PFAS) are synthetic, heat-resistant, and chemically inert compounds widely used in coatings, firefighting foams, and surface treatments. Their environmental persistence, bioaccumulation potential, and toxicity have led to global regulatory scrutiny. As legislation tightens across regions, analytical laboratories face growing volumes of PFAS data that demand efficient, accurate processing.

Objectives and Overview of the Study

This application note demonstrates the use of Peakintelligence, an AI-based peak integration software, for LC-MS/MS analysis of PFAS. The primary goals were to compare its performance against conventional parameter-driven algorithms, assess its impact on analysis speed and accuracy, and evaluate its suitability for routine PFAS monitoring in complex matrices.

Methodology and Instrumentation

• Sample Matrix: Industrial wastewater containing 72 PFAS compounds (including 31 internal standards).
• Chromatographic Platform: Shimadzu LCMS-8045/8050/8060RX triple quadrupole mass spectrometers.
• Software: Peakintelligence peak integration module, trained on approximately 13,000 expert-annotated chromatograms using deep learning techniques.
• Comparison: Traditional integration requiring manual parameter tuning versus fully automated AI-driven integration with no user-defined settings.

Key Results and Discussion

• False Detections: Traditional algorithms produced false peaks in 26 of 72 compounds; Peakintelligence eliminated all false detections.
• Signal-to-Noise Performance: Reliable detection and integration of low-intensity peaks without manual adjustments.
• Analysis Time: Average processing time reduced from 2 minutes to 45 seconds per dataset. Projected analysis time for 100 samples dropped from 3.3 hours to 1.3 hours.
• Reproducibility: Removal of user-dependent parameters minimized variability and standardized results across operators.

Benefits and Practical Applications

• Labor Savings: Significant reduction in manual review and correction workload.
• Operator Independence: Intuitive workflow enables inexperienced users to achieve expert-level results.
• Scalable Compliance: Meets increasing PFAS monitoring demands driven by expanding regulations.

Future Trends and Opportunities

With regulatory agencies broadening PFAS lists, data complexity will escalate. AI-powered integration tools can evolve to support continuous learning for new analytes and integration with laboratory information management systems (LIMS). Cloud-based deployments may facilitate real-time monitoring and cross-laboratory collaboration.

Conclusion

Peakintelligence delivers a robust, AI-driven solution for PFAS peak integration on Shimadzu LC-MS platforms. It provides flawless accuracy, substantial time savings, and consistent performance without manual parameter configuration, addressing the growing analytical burden in environmental and regulatory testing.