A Blind Study of Pesticide Residues in Spiked and Unspiked Fruit Extracts Using Deconvolution Reporting Software

Importance of the Topic

Rapid and reliable screening of pesticide residues in food is essential for protecting public health and ensuring compliance with regulatory limits. Complex sample matrices such as fruit extracts often interfere with target compound confirmation in full-scan GC/MS, making manual data review time-consuming and prone to variability. Automated deconvolution approaches can address these challenges by accelerating data processing and improving confidence in trace-level determinations.

Objectives and Study Overview

This blind study assessed the performance of Agilent’s Deconvolution Reporting Software (DRS) coupled to a mass selective detector for identifying pesticide residues in both spiked and unspiked fruit extracts. Six extracts (orange, lettuce, apple as controls spiked with 20–40 pesticides; real samples of grapes, orange, apple) were evaluated without prior knowledge of spike identities or levels. Key goals included comparing detection rates, false positive/negative occurrences, and processing time versus manual confirmation.

Methodology

The workflow combined three techniques:

• Retention Time Locking to align target compounds without daily calibration.
• AMDIS deconvolution to extract individual spectra from complex total ion chromatograms.
• NIST02 library searching of deconvoluted spectra for final confirmation.

The default DRS settings processed each chromatogram in approximately one minute, automating peak finding, qualifier ion evaluation, and library matching for up to 567 pesticides and endocrine disruptors in a single 1–2 minute GC/MS run.

Used Instrumentation

Gas chromatograph: Agilent 6890N GC with HP-5MS column (30 m × 0.25 mm i.d. × 0.25 µm); helium carrier gas at 1.9 mL/min.
Injector: PTV, septumless head, ramp from 90 °C to 250 °C.
Oven program: 70 °C (2 min) → 150 °C → 200 °C → 280 °C.
Mass spectrometer: Agilent 5973 inert MSD in EI scan mode (40–550 amu), with source at 230 °C, quad at 150 °C.

Results and Discussion

In the three blind-spiked samples, DRS detected and confirmed 93% of database-listed pesticides, with only a few polar compounds below recovery thresholds. Manual review required over 30 minutes per sample, while DRS delivered results in roughly one minute with minimal false positives or negatives. In real fruit extracts, DRS confirmed the customer’s reported residues and identified additional compounds (e.g., diphenylamine) that had gone undetected. Adjustments to AMDIS sensitivity recovered certain borderline analytes, illustrating the method’s flexibility.

Benefits and Practical Applications

• Substantial time savings through automated deconvolution and reporting.
• High confidence in compound identification via multi-tiered confirmation (RT locking, qualifier ions, library search).
• Ability to screen hundreds of pesticides in a single run without targeted ion monitoring.
• Reduced operator dependency and reproducible results across varying matrix backgrounds.

Future Trends and Potential Applications

Integration of high-resolution mass spectrometry and expanded spectral libraries will further enhance deconvolution accuracy and sensitivity. Machine learning algorithms may refine component extraction and reduce false identifications. Real-time data processing and cloud-based reporting could enable on-site screening in agricultural fields or at ports of entry.

Conclusion

The Agilent MSD-DRS platform demonstrates a robust, rapid, and reliable solution for comprehensive pesticide screening in complex food matrices. By automating deconvolution and library matching, it delivers accurate results in minutes, significantly outperforming traditional manual confirmation workflows.

References

Philip L. Wylie; Michael J. Szelewski; Chin-Kai Meng; Christopher P. Sandy. Comprehensive Pesticide Screening by GC/MSD Using Deconvolution Reporting Software. Agilent Technologies Application Note 5989-1157EN, 2004.