Pesticide Screening in Strawberries Using the Agilent 8860 GC with the Agilent 5977B GC/MSD and SureTarget Deconvolution

Importance of the Topic

Trace-level monitoring of pesticide residues in fruits like strawberries is critical for ensuring food safety and regulatory compliance.
The complexity of food matrices demands sensitive and selective analytical workflows to detect hundreds of possible contaminants quickly and reliably.

Study Objectives and Overview

This study evaluates a two-stage screening approach using an Agilent 8860 GC coupled with an Agilent 5977B GC/MSD and automatic deconvolution software to identify pesticides in strawberry extracts.
Local retail and farmer’s market samples were prepared using QuEChERS extraction and analyzed to demonstrate method feasibility and performance.

Sample Preparation and Methodology

Samples were homogenized under liquid nitrogen and extracted with acetonitrile following the EN 15662 QuEChERS protocol.
Pulsed splitless injection minimized analyte degradation and improved transfer efficiency of active pesticides.
Retention time locking (RTL) to a chlorpyrifos-methyl standard ensured highly reproducible retention times within ±0.1 minute across runs.
Agilent MassHunter Unknowns Analysis software performed automated spectral deconvolution and library screening against a >1,000 compound RTL pesticide library and the NIST 17 database.

Used Instrumentation

• Agilent 8860 GC with pulsed splitless inlet and low pressure drop (LPD) inlet liner (p/n 5190-2295)
• Agilent 5977B GC/MSD with stainless steel EI source and performance turbo pump
• Agilent J&W HP-5ms Ultra Inert column (30 m × 0.25 mm, 0.25 µm)
• Helium carrier gas, oven program: 80 °C (1.5 min), 40 °C/min to 120 °C, then 5 °C/min to 310 °C
• Agilent MassHunter Unknowns Analysis, Pesticide Personal Compound Database and Library (PCDL), NIST 17 library

Main Results and Discussion

The total ion chromatogram of a representative extract revealed significant coeluting matrix compounds, which were effectively resolved by the deconvolution algorithm.
Nine pesticides—including fenhexamid, cyprodinil, pyrimethanil, and fludioxonil—were identified with high library match scores (>90) and retention time deviations <0.07 min.
A broader NIST 17 screen produced over 400 preliminary hits, which required careful filtering by match score and retention index; false positives such as sarin were ruled out based on RI discrepancies and spectral considerations.
Estimated detection limits for reliable identification were below or near maximum residue tolerances set by regulatory agencies, confirming the method’s suitability for compliance testing.

Benefits and Practical Applications

• Rapid, cost-effective screening of complex food matrices for hundreds of pesticides
• High confidence in compound identification through RTL and spectral deconvolution
• Broad applicability with minimal method development for new sample types
• Interoperability across GC/MS platforms and seamless integration with quantitation workflows

Future Trends and Potential Applications

Advancements may include integration with high-resolution or tandem mass spectrometry for enhanced selectivity, expansion of curated deconvolution libraries, and machine learning–driven data interpretation.
Online sample preparation, real-time data processing, and portable GC/MS devices could enable field screening of produce at critical control points in the supply chain.

Conclusion

The combination of pulsed splitless GC/MSD, retention time locking, and automated spectral deconvolution offers a robust platform for comprehensive pesticide screening in strawberries.
This workflow delivers reliable detection at levels set by regulatory agencies and can be readily adapted to other food commodities with complex matrices.

Reference

1. Westland J.; Stevens J. Optimal Method for the Analysis of Pesticides in a Variety of Matrices. Agilent Technologies Application Note 5991-7303EN, 2017.
2. Chen K.; Nieto S.; Stevens J. GC/Q-TOF MS Surveillance of Pesticides in Food. Agilent Technologies Application Note 5991-7691EN, 2017.
3. Andrianova A.; Westland J.; Quimby B. Quantitation of Pesticides in Strawberries at Tolerance Levels Using Agilent 8890/7000D and 8890/7010B. Agilent Technologies Application Note 5994-0799EN, 2019.
4. US Environmental Protection Agency. Index to Pesticide Chemical Names, Part 180 Tolerance Information. December 12, 2012.
5. USDA AMS S&T MPD. Pesticide Data Program (PDP) Database Search Application – User Guide. January 2019.