Collision Cross Section: A New Identification Point for a “Catch All” Non-Targeted Screening Approach

Importance of the Topic

Monitoring pesticide residues in food is essential to ensure consumer safety and regulatory compliance. Traditional high-resolution mass spectrometry methods rely on accurate mass, fragment ions, retention time, and isotope patterns, but complex matrices and inter-laboratory variability can lead to false positive and negative results. Introducing collision cross section (CCS) measurements via ion mobility spectrometry adds an orthogonal identification parameter to improve confidence and efficiency in non-targeted screening workflows.

Objectives and Study Overview

This study evaluated CCS values derived from ion mobility drift times as a new identification point in a generic “catch all” screening approach. The goals were to demonstrate matrix-independent CCS reproducibility, assess inter-laboratory method transferability, and quantify the impact of CCS filtering on reducing false detections in pesticide residue analysis.

Methodology and Instrumentation

Used Instrumentation:

• UPLC: Waters ACQUITY UPLC I-Class with BEH C18 column (100 × 2.1 mm, 1.7 µm) at 45 °C, gradient from 2% to 99% acetonitrile (0.1% formic acid).
• IMS-MS: Waters SYNAPT G2-S HDMS, ESI+, drift gas N₂, wave velocity 650 m/s, wave height 40 V; mass range 50–1200 Da; acquisition 5 spectra/s; MassLynx and UNIFI software.

Sample Preparation:
Ten grams of homogenized pear, ginger, leek, mandarin or proficiency test (FV-13) material were extracted with 20 mM ammonium acetate in methanol, filtered, diluted to 100 mL, and spiked with 50 pesticide standards.

Main Results and Discussion

• Drift time versus m/z plots for 50 pesticides showed complete overlap across pear, ginger, leek, and mandarin, confirming matrix-independent CCS values.
• A one-tailed t-test demonstrated statistically identical drift time distributions between solvent standards and matrix-matched samples (p < 0.001).
• Inter-laboratory UPLC retention time variability reached ±0.3 min for boscalid across five global labs, underscoring the challenge of generic RT windows.
• Application to FV-13 proficiency test: initial component assignment (20 ppm mass, ±0.5 min RT, 10% CCS) yielded 29 candidates; sequential filtering by 10 ppm mass cut reduced to 22, fragment matching to 10, and 2% CCS tolerance to 8 true positives with zero false detections.

Benefits and Practical Applications

• Orthogonal CCS criterion reduces reliance on stringent RT and mass windows, lowering false positives and negatives.
• Generic “catch all” workflows accelerate method transfer between instruments and laboratories.
• Decreases the need for costly retention time confirmation with pure standards.
• Enhances throughput in routine food safety screening and proficiency testing.

Future Trends and Applications

• Expansion of publicly accessible CCS libraries to cover broader analyte classes.
• Integration of CCS filtering into regulatory guidelines for non-targeted screening.
• Coupling CCS data with machine learning algorithms for automated compound annotation.
• Application of CCS-driven workflows to environmental, clinical, and metabolomic screening.

Conclusion

Incorporating CCS measurements from ion mobility spectrometry into UPLC-MS screening enables a robust, orthogonal identification metric that significantly reduces false detections. This approach supports generic, high-throughput pesticide residue analysis across diverse matrices and laboratories, minimizing resource-intensive confirmation steps and enhancing confidence in non-targeted workflows.

Reference

• SANCO/12571/2013: Method Validation and Quality Control Procedures for Pesticide Residues Analysis in Food and Feed.