Sample Cleanup Methods for Multiresidue Pesticide GC/MS/MS Analysis in Avocado

Importance of the Topic

Avocado is a challenging matrix for pesticide residue analysis due to its high lipid content (15–20 %). Effective removal of lipids and other high-molecular-weight interferences is essential to ensure accurate, reliable detection of low-level pesticide residues by GC/MS/MS. Traditional gel permeation chromatography (GPC) offers good cleanup but is time-consuming, solvent-intensive, and requires specialized equipment. The development of Enhanced Matrix Removal—Lipid (EMR—Lipid) sorbent for dispersive solid-phase extraction (dSPE) addresses these limitations, offering a simplified workflow for high-throughput laboratories.

Objectives and Study Overview

This work compares two cleanup approaches following QuEChERS extraction of avocado: Gel Permeation Chromatography (GPC) and Agilent Bond Elut EMR—Lipid dSPE. Key objectives include:

• Evaluating matrix removal efficiency by full-scan GC/MS chromatograms
• Comparing recovery and reproducibility for 38 pesticides spiked at 5, 50, and 300 ppb levels
• Assessing solvent consumption, sample throughput, and total processing time for each method

Methodology and Instrumentation

The study employed the AOAC 2007.01 QuEChERS extraction protocol with 1 % acetic acid in acetonitrile on 15 g homogenized avocado. The extract was split for parallel cleanup:

1. EMR—Lipid dSPE: Addition of water, vortex, centrifugation, transfer to EMR—Lipid tube, salt addition, vortex, centrifugation, and direct GC/MS/MS analysis.
2. GPC cleanup: Filtration, rotary evaporation, reconstitution in ethyl acetate/cyclohexane (1:1), automated GPC run (90 mL discard, 120 mL collect, 20 mL column wash), solvent evaporation, reconstitution, and GC/MS/MS injection.

Used Instrumentation

• Gilson GX-271 Automated GPC Cleanup System with Bio-Beads S-X3 column
• Agilent 7890A GC with HP-5ms Ultra Inert 15 m × 0.25 mm, 0.25 µm column, midcolumn backflush
• Agilent 7693A autosampler
• Agilent 7000B Triple Quadrupole GC/MS, electron impact mode, multiple reaction monitoring (MRM)

Main Findings and Discussion

Matrix removal measured by total ion chromatogram area showed ~77 % cleanup with EMR—Lipid versus ~65 % with GPC. Across three avocado varieties, lipid levels varied (5.3–31.1 %), affecting cleanup efficiency. Recovery and precision at 50 ppb demonstrated that 36 of 38 pesticides achieved 70–120 % recovery with RSD < 20 % for both methods. At the lowest level (5 ppb), EMR—Lipid outperformed GPC for some labile or low-mass analytes (e.g., phorate, iprodione). Highly lipophilic compounds (high log P) such as DDT and bifenthrin showed slightly reduced recovery with EMR—Lipid, likely due to stronger sorbent interaction.

Benefits and Practical Applications

• EMR—Lipid reduces sample preparation time by ~90 %, from ~118 min to ~16 min per sample.
• Solvent consumption drops dramatically, eliminating the ~230 mL required by GPC per sample.
• Batch processing of multiple samples is feasible with EMR—Lipid, increasing laboratory throughput.
• Comparable or improved recovery and precision for most pesticide classes make EMR—Lipid a robust choice for high-fat matrices.

Future Trends and Potential Applications

Emerging trends include integration of EMR sorbents into automated platforms for unattended, high-throughput analysis. Further development of selective sorbent chemistries may extend cleanup strategies to other challenging matrices (e.g., nuts, fish tissue). Coupling with high-resolution MS or next-generation detectors will enhance sensitivity for ultra-trace contaminants. Data-driven method optimization and machine-learning-assisted cleanup protocols could further streamline workflows.

Conclusion

EMR—Lipid dSPE provides a rapid, efficient alternative to GPC for cleanup of high-lipid food matrices in multiresidue pesticide GC/MS/MS analysis. It achieves comparable matrix removal and analytical performance while drastically reducing solvent use and processing time. Laboratories seeking higher throughput without sacrificing data quality will benefit from adopting EMR—Lipid cleanup.

Reference

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