Screening of Pesticides in Lipid-rich Food Matrices by Using High Resolution GC/Q-TOF and Accurate Mass Pesticide Library

Importance of the Topic

The reliable detection of pesticide residues in lipid‐rich food matrices such as oils, avocados and fish is critical to ensure food safety and to comply with stringent regulatory maximum residue levels (MRLs). Conventional targeted methods may overlook unexpected or emerging compounds, whereas high resolution GC/Q‐TOF with accurate mass measurement enables untargeted full‐scan screening, improving both the breadth of detectable analytes and confidence in identification.

Objectives and Study Overview

This study aimed to develop and validate a comprehensive workflow using a high resolution GC/Q‐TOF system coupled with an accurate mass pesticide library for the screening of a broad range of pesticide classes in lipid‐rich food samples. Key objectives included evaluating detection capabilities at trace levels across complex matrices and assessing method precision, accuracy and long‐term stability.

Methodology

Sample Preparation and Spiking

• Extraction of peanut oil, avocado and salmon samples using QuEChERS followed by EMR‐Lipid dSPE cleanup and dry‐down steps.
• Spiking of 120 pesticide standards at 5 and 10 ng/mL; additional matrix‐matched calibration in avocado over 5–200 ng/mL.

Data Acquisition and Analysis

• Full‐spectrum EI acquisition on an Agilent 7200 Series GC/Q‐TOF with mid‐column backflushing and retention time locking.
• Automated screening using a curated accurate mass library and “Find by Fragment” workflow in MassHunter Qualitative Analysis.
• Manual extraction of extracted ion chromatograms (EICs) for compounds not found automatically.

Used Instrumentation

• Agilent 7200 Series GC/Q‐TOF with MMI inlet and dual HP-5 MS UI columns.
• Helium carrier gas; mid‐column backflush configuration; transfer line at 280 °C.
• Mass range 45–550 m/z at 4 GHz high resolution, acquisition rate 5 spectra/sec.
• Data processing with MassHunter Qualitative and Quantitative Analysis software.

Main Results and Discussion

Detectability and Identification

• Out of 120 spiked pesticides, ≥110 compounds were reliably detected in each matrix at both 5 and 10 ng/mL by automated analysis.
• For those not automatically flagged, manual EIC extraction allowed the identification of most remaining compounds.

Performance Metrics

• Mass accuracy better than 5 ppm for over 90 % of detected pesticides across matrices of low to high complexity.
• Retention time repeatability (SD ≤0.01 min) and response RSDs mostly in single‐digit percentages at both spike levels.
• Ion ratio variance within ±30 % of library values, meeting regulatory identification criteria.
• Long‐term stability over 36 injections in avocado showed consistent response and mass accuracy.
• Calibration in avocado yielded linear responses (R2 > 0.99) for 105 pesticides over 5–200 ng/mL.

Benefits and Practical Application

The combination of high resolution accurate mass GC/Q‐TOF and an extensive pesticide library offers:

• Comprehensive untargeted screening for known and emerging residues.
• Enhanced confidence through accurate mass, retention time locking and ion ratio confirmation.
• Lower screening detection limits and robust performance in complex lipid matrices.
• Streamlined data review with automated workflows and potential transfer to quantitative analysis.

Future Trends and Potential Applications

Emerging opportunities include:

• Integration of machine learning algorithms for improved deconvolution and identification of unknown contaminants.
• Expansion of accurate mass libraries to cover additional pesticide classes and degradation products.
• Automation of sample prep and data processing for higher throughput in routine monitoring.
• Application of this workflow to other challenging matrices such as dairy, processed foods and environmental samples.

Conclusion

This work demonstrates that high resolution GC/Q‐TOF combined with an accurate mass pesticide library provides a powerful, fit‐for‐purpose screening platform for trace pesticide residues in lipid‐rich foods. The method achieves low detection limits, high mass accuracy, precise retention time control and robust long‐term stability, making it suitable for regulatory and quality assurance laboratories.