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Multiresidue Analysis of Pesticides in Avocado with Agilent Bond Elut EMR—Lipid by GC/MS/MS

Applications | 2015 | Agilent TechnologiesInstrumentation
GC/MSD, GC/MS/MS, Sample Preparation, GC/QQQ, Consumables
Industries
Food & Agriculture
Manufacturer
Agilent Technologies

Summary

Importance of the Topic


High-fat food matrices such as avocado pose significant challenges in pesticide residue analysis due to lipid-related interferences that compromise chromatographic performance and quantitation accuracy. Effective matrix removal is essential to meet regulatory validation criteria and maintain instrument robustness, particularly when using gas chromatography coupled to tandem mass spectrometry (GC/MS/MS).

Objectives and Study Overview


This study evaluates Agilent Bond Elut Enhanced Matrix Removal—Lipid (EMR-Lipid) dispersive solid-phase extraction (dSPE) as part of a QuEChERS AOAC workflow for the multiresidue analysis of 23 GC-amenable pesticides in avocado. Comparisons were made against traditional cleanup sorbents C18/PSA and zirconia-based materials to assess matrix removal efficiency, analyte recovery, precision, and system performance over multiple injections.

Methodology and Instrumentation


Sample preparation followed an optimized QuEChERS AOAC extraction: 15 g homogenized avocado mixed with 15 mL acetonitrile (1 % acetic acid), salted extraction, and centrifugation. Cleanup employed either EMR-Lipid dSPE plus polishing salts (NaCl/MgSO₄) or conventional C18/PSA and zirconia sorbents. Extracts were analyzed by GC/MS/MS in MRM mode. Calibration standards (1–400 ng/g) and quality control samples (5, 50, 300 ng/g) were prepared in matrix-matched format, using isotopically labeled internal standards.

Instrumentation Used


  • Agilent 7890A Gas Chromatograph with pulsed cold splitless inlet and backflush capability
  • Agilent 7693B Autosampler
  • Agilent 7000C Triple Quadrupole GC/MS system
  • Agilent Bond Elut EMR-Lipid and Final Polish dSPE tubes

Main Results and Discussion


EMR-Lipid achieved approximately 95 % matrix removal by full-scan GC/MS, compared to 36 % for C18/PSA and 55 % for zirconia. Cleaner extracts yielded improved signal-to-noise, fewer interferences in MRM chromatograms, and more consistent peak integration. Analyte recoveries with EMR-Lipid ranged from 65 % to 123 %, comparable to C18/PSA and superior to zirconia for most compounds. Method validation delivered accuracy between 70 % and 120 % (one compound at 67 %) and precision below 20 % RSD for all analytes. Over 100 consecutive injections of fortified avocado, EMR-Lipid extracts maintained RSDs <15 % for 91 % of analytes, whereas C18/PSA and zirconia-treated samples showed greater variability due to matrix buildup.

Benefits and Practical Applications


  • Exceptional lipid removal enhances GC/MS/MS reliability and extends maintenance intervals
  • High analyte recoveries and precision support stringent regulatory requirements
  • Simple integration into existing QuEChERS workflows without additional time or solvent penalties
  • Reduced data review and reruns owing to fewer interferences

Future Trends and Opportunities


Extending EMR-Lipid cleanup to other high-fat commodities (nuts, seeds, animal tissues) and diverse analyte classes (e.g., nonvolatile, thermally labile pesticides) may further streamline multiresidue methods. Integration with high-throughput GC or LC–MS/MS platforms and automated sample prep systems could drive additional gains in productivity and data quality.

Conclusion


The Agilent Bond Elut EMR-Lipid dSPE method provides a fast, robust, and highly effective cleanup solution for multiresidue pesticide analysis in avocado by GC/MS/MS. By delivering superior matrix removal, excellent analyte recovery, and outstanding reproducibility, this approach enhances laboratory throughput and analytical confidence for high-fat food testing.

Reference


  1. Anastassiades M, Lehotay SJ, Štajnbaher D, Schenck FS. AOAC Int. 2003;86:412–431.
  2. Lehotay SJ, Mastovská K, Lightfield AR. J. AOAC Int. 2005;88:615–629.
  3. Chamkasem N, Ollis LW, Harmon T, Mercer G. J. Agric. Food Chem. 2013;61:2315–2329.
  4. Hildmann F, Gottert C, Frenzel T, Kempe G, Speer K. J. Chromatogr. A 2015;1403:1–20.
  5. Lehotay SJ. Mass Spec. Food Safety Methods in Mol. Biol. 2011;747:65–91.
  6. Sapozhnikova Y, Lehotay SJ. Anal. Chim. Acta 2013;758:80–92.
  7. Morris BD, Schriner RB. J. Agric. Food Chem. 2015;63:5107–5119.
  8. Wong JW. J. Agric. Food Chem. 2011;59:7636–7646.
  9. Hayward DG, Wong JW. Anal. Chem. 2013;85:4686–4693.
  10. Saito K, Sjödin A, Sandau CD, Davis MD, Nakazawa H, Matsuki Y, Patterson DG Jr. Chemosphere 2004;57:373–381.
  11. Kegley SE, Hill BR, Orme S, Choi AH. PAN Pesticide Database; Pesticide Action Network, North America, 2014.
  12. Szelewski MJ, Quimby B. Agilent Application note 5989-1716EN, 2004.
  13. Meng CK. Agilent Application note 5990-9453EN, 2011.
  14. Zhao L, Lucas D. Agilent Application note 5991-6098EN, 2015.
  15. European Commission. SANCO/12571/2013 Guidance Document on Analytical Quality Control and Validation Procedures for Pesticide Residues Analysis in Food and Feed. Brussels; 2013.

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