Improvement to Targeted & Untargeted Pesticide Residue Analysis: Fast and Flexible Analyte Finding For GC-MS and GCxGC-MS

Importance of the Topic

The rapid increase in pesticide use across diverse food commodities demands analytical approaches capable of both targeted and untargeted screening. Integrating comprehensive data processing with advanced chromatographic methods accelerates method development, reduces sample injections, and enhances detection of emerging residues.

Objectives and Study Overview

This study demonstrates a streamlined workflow for simultaneous targeted and non-targeted pesticide residue analysis using GC-MS and GC×GC-MS. It highlights the creation of a dynamic target compound list, automated data processing in ChromaTOF software, and the benefits of two-dimensional chromatography in overcoming matrix interferences.

Methodology and Instrumentation

The analytical protocol employed QuEChERS spinach extracts spiked with a multi-residue pesticide mix (0.2–2000 ng/g). Instrumentation included:

• LECO Pegasus® BT 4D mass spectrometer with StayClean® ion source
• Agilent 7890A GC with LECO secondary oven and quad-jet thermal modulator
• Primary column: Rxi-5MS (30 m × 0.25 mm × 0.25 µm); secondary column: Rtx-200 (1 m × 0.25 mm × 0.25 µm)
• Scan range 45–560 m/z; acquisition rate 8 Hz (1D) and 280 Hz (GC×GC)
• Injection: 1 µL splitless at 225 °C; carrier gas He at 1.4 mL/min

Main Findings and Discussion

Linearity data for over 30 halogenated pesticides showed coefficients of determination above 0.998 with limits of quantitation as low as 0.2 ng/g. The ChromaTOF workflow uses Target Analyte Finding (TAF) for known compounds and Non-Target Deconvolution (NTD) for unexpected peaks. A user library with retention index information streamlines target list updates. GC×GC provided superior peak focusing and separation; for example, Chloroneb quantitation in 1D was hindered by coeluting matrix, while GC×GC delivered improved sensitivity, accuracy, and spectral fidelity.

Benefits and Practical Applications

• Reduced method development time through automated target list generation and deconvolution
• Single-injection acquisition of full mass spectra for both targeted and non-targeted compounds
• Enhanced spectral clarity for library matching and reliable identification
• Improved chromatographic resolution with GC×GC, enabling trace-level quantitation in complex matrices

Future Trends and Applications

Advances in data processing automation and machine learning will further refine non-target screening, enabling real-time identification of emerging contaminants. Coupling high-throughput GC×GC-MS with cloud-based libraries can support global monitoring networks and regulatory compliance. Miniaturized and portable platforms may extend these capabilities to field-based analysis.

Conclusion

This integrated workflow delivers a fast, flexible, and robust approach for pesticide residue analysis. By combining targeted screening and non-target deconvolution in a single method and leveraging GC×GC for enhanced resolution, laboratories can achieve comprehensive monitoring with fewer injections and greater confidence in quantitation and identification.