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Automated dispersive liquid-liquid micro extraction (DLLME) for GC-MS/MS analysis of semi-volatile compounds in water

Applications | 2024 | Thermo Fisher ScientificInstrumentation
Sample Preparation, GC/MSD, GC/MS/MS, GC/QQQ
Industries
Environmental
Manufacturer
Thermo Fisher Scientific

Summary

Significance of the topic


Trace levels of pesticides and polyaromatic hydrocarbons in water pose serious environmental and public health risks. Regulatory frameworks such as the EU Water Framework Directive and national programs demand highly sensitive, reliable, and cost-effective analytical methods. Automation of sample preparation and analysis can reduce labor time, solvent consumption, and variability, while delivering the low limits of quantitation required for potable and environmental water monitoring.

Objectives and overview of the study


This application note evaluates an automated dispersive liquid–liquid microextraction (DLLME) workflow, directly coupled on-line to GC-MS/MS, for simultaneous determination of 70 pesticides and PAHs in water. The goals are to demonstrate sub-ppt sensitivity, compliance with stringent EU and Italian directives, full method automation, and overall gains in productivity and data quality.

Methodology and instrumentation


  • Sample preparation: 5 mL water aliquots are placed in 9.5 mL high-recovery vials. 25 µL of internal standards and surrogates are added automatically, followed by 100 µL methanol (dispersive solvent) and 400 µL dichloromethane (extraction solvent). A 60 s vortex and 90 s centrifugation break the emulsion and concentrate analytes in the organic phase.
  • Instrumentation: Thermo Scientific TriPlus RSH SMART robotic autosampler executes all DLLME steps and an on-line 25 µL large volume injection via an iConnect PTV injector on a TRACE 1610 GC. The TSQ 9610 triple quadrupole MS with AEI source provides Timed-SRM detection.
  • Chromatography and detection: GC oven ramp from 40 °C to 320 °C in 22 min; helium carrier at 0.8 mL/min; MS SmartTune and EPA 8270 compliant tuning ensure reliable quantitation.

Main results and discussion


  • Linearity: Nine-point calibration from 0 to 1 µg/L yielded R2 > 0.997 for all targets, demonstrating a consistent response across sub-ppt to ppb levels.
  • Limits of quantitation and detection: Method LOQs ranged from 0.0005 to 0.02 µg/L. MDLs for key PAHs were below 0.0002 µg/L, achieving sub-ppt detection.
  • Repeatability: Ten replicate extractions at 0.01 µg/L produced RSDs below 10% for all compounds, meeting stringent validation criteria.

Benefits and practical applications


  • Solvent and sample volume reduction: DLLME uses <1 mL of organic solvent versus >10 mL in SPE and lowers sample volume from hundreds of milliliters to 5 mL.
  • Labor and throughput: Automated workflow halves analyst time and doubles daily sample throughput to ~48 samples per 24 h, with 24/7 unattended operation.
  • Method consolidation: A single automated assay covers both pesticides and PAHs in diverse water matrices, simplifying laboratory logistics and data management.

Future trends and applications


Emerging contaminants such as newer flame retardants or insecticides can be added to the SRM method for forward-looking monitoring. Further integration with LIMS, advanced data analytics, and AI-driven peak deconvolution will enhance robustness and support rapid regulatory reporting.

Conclusion


The automated DLLME-GC-MS/MS approach using the TriPlus RSH SMART autosampler and TSQ 9610 achieves sub-ppt sensitivity, high precision, and regulatory compliance while drastically reducing solvents, sample volumes, and manual labor. This unified workflow increases laboratory efficiency, data quality, and readiness for future monitoring challenges.

Reference


  1. Directive 2000/60/EC and 2008/105/EC of the European Parliament (2013 amendment).
  2. UK Chemical Investigation Program Phase 2 (CIP2) regulations.
  3. Thermo Scientific On-demand webinar: Cost savings and increased data quality through automation.
  4. SampleQ automated workflow scripting documentation.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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