Exploring the advantages of automated Sample Preparation and GC-TOF for SVOC & Pesticide Analysis in Environmental Waters

Exploring the advantages of automated Sample Preparation and GC-TOF for SVOC & Pesticide Analysis in Environmental Waters

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Recent advances in the sensitivity and selectivity and modern GC-MS instrumentation combined with the development of laboratory robotic systems have made it possible to miniaturise and automate traditional sample preparation methods without compromising compound limits of detection. In particular the application of fully automated Dispersive Liquid/Liquid Microextraction (DLLME) shows great promise for the extraction of a very wide range of organic pollutants from aqueous matrices. Combination of miniaturised sample preparation with GC-TOF instrumentation enables the simultaneous trace level analysis of a wide range of environmentally significant analytes with greatly reduced labour costs. This has the potential to deliver major efficiency and productivity gains for busy commercial laboratories. This presentation focuses on the use of an automated DLLME procedure followed by analysis on a LECO Pegasus BT GC-MS system to deliver ng/L (ppt) level detection of a wide range of pesticide and SVOC compounds from less than 10mls of sample.

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John Quick

ALS Environmental

John Quick been working as an analytical chemist in the water and environmental sector for 30 years. He has previously worked for Thames Water, the Environment Agency and Severn Trent Laboratories. He has specialist knowledge of the analysis of environmental samples for trace level organic pollutants using modern analytical techniques and recently led development of a series of methods for the ultra-low level detection (sub ng/L) of organic pollutants in surface waters and sewage samples to meet the requirements of the UK Chemical Investigation Programme (CIP) and the EU Water Framework Directive (WFD). He is currently working on the miniaturisation and automation of analytical methodology using robotic techniques.

IET 36.2 Mar/Apr 2026

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