Advantages of Reversed Sandwich Injection for Pesticide Residue Analysis

Importance of the Topic

The accurate quantitation of pesticide residues across diverse food matrices is critical for food safety, regulatory compliance and public health. Matrix effects can bias analytical results, making matrix-matched calibration essential. However, preparing multiple matrix-matched standards is time-consuming, labor-intensive and prone to human error. The reversed 3-layer sandwich injection technique addresses these challenges by automating internal standard addition and simultaneous matrix incorporation, streamlining calibration workflows while enhancing data quality.

Objectives and Study Overview

This work aimed to demonstrate the advantages of the Agilent 7693A autosampler’s reversed 3-layer switch injection for pesticide residue analysis. Four representative food commodities (extra virgin olive oil, black loose leaf tea, fresh baby spinach and organic honey) were evaluated. Over 50 pesticides were monitored by GC–QQQ MS/MS using a single set of calibration standards delivered via sandwich injection, assessing linearity, precision, detection limits and matrix effects.

Methodology and Instrumentation Used

Sample Preparation

• QuEChERS extraction adapted to each matrix with appropriate dispersive SPE cleanup to remove pigments, sugars or fats.
• Calibration standards and internal standard (ISTD) prepared in solvent; matrix layers introduced by sandwich injection.

Instrumentation

• Agilent 7693A Automatic Liquid Sampler configured for reversed 3-layer switch (order L3, L1, L2).
• Agilent 7890B GC with Multimode Inlet and splitless ultra inert liner; dual 15 m × 0.25 mm HP-5ms UI columns connected via purged ultimate union for mid-column backflush.
• Agilent 7010A Triple Quadrupole MS/MS operated in dMRM mode; electron energy 70 eV; source temperature 280 °C; optimized collision gas flows.

Main Results and Discussion

• Linearity: Over 85 % of pesticides achieved R² ≥ 0.991 over 1.25–62.5 ppb in all matrices.
• Precision: Repeat injections at 1.25 ppb showed ≤ 30 % RSD for 85 % of analytes; lower variance observed when matrix was drawn last (bottom layer) in sandwich injection.
• Sensitivity: Method detection limits (MDLs) and limits of quantitation (LOQs) were below 0.1 ppb for most analytes, with matrix layering protecting active inlet sites and reducing analyte loss.
• Matrix Effects: Introducing the matrix first to the inlet preferentially occupied active sites, improving peak shape, reproducibility and linearity compared to traditional injection sequences.

Benefits and Practical Applications

• Eliminates the need for separate matrix-matched standard preparation for each commodity.
• Automates internal standard addition, reducing pipetting errors and sample handling.
• Enhances throughput by enabling one-pass calibration across multiple food types.
• Improves quantitation accuracy and lowers detection limits by mitigating matrix-induced active-site adsorption.

Future Trends and Opportunities

• Extension of sandwich injection strategies to derivatization protocols or multi-analyte workflows.
• Integration with high-resolution mass spectrometry and ultrafast chromatography for broader residue screening.
• Application to more complex matrices (e.g., fatty tissues, fermented products) and emerging contaminants.
• Development of standardized software workflows for automatic sequence generation and data processing.

Conclusion

The reversed 3-layer sandwich injection on the Agilent 7693A autosampler offers a robust, efficient and reproducible approach for pesticide residue calibration in diverse food matrices. By automating matrix and internal standard layering, it simplifies workflows, reduces human error and delivers high sensitivity and precision, making it a valuable tool for regulatory and quality-control laboratories.

References

1. Zhao L.; Szelewski M. Analysis of Fruit and Vegetable Pesticides by GC/MS/MS Using Agilent Inert Flow Path; Agilent Technologies Application Note 5991-3234EN, 2013.
2. Anastassiades M.; Lehotay S.J.; Štajnbaher D.; Schenck F.S. J. AOAC Int. 2003, 86, 412–431.
3. Lehotay S.J.; Mastovská K.; Lightfield A.R. J. AOAC Int. 2005, 88, 615–629.
4. Westland J.; Stevens J. An Optimal Method for the Analysis of Pesticides in a Variety of Matrices; Agilent Technologies Application Note 5991-7303EN, 2016.
5. Wylie P. Using Sandwich Injections to Add Matrix, Internal Standards and/or Analyte Protectants for GC/Q-TOF Analysis of Pesticide Residues; Presentation at NACRW 2016.