Green Innovations in Volatile Chemical Analysis: Advancing Sustainable Methods for Environmental, Material, and Food Samples with Thermal Desorption GC-MS

Reliable analysis of environmental, material, and food and beverage samples is crucial for maintaining supply chain integrity, ensuring public health, and meeting sustainability targets. Measuring volatile and semi-volatile organic compounds (VOCs and SVOCs) is essential to global regulations to:
- Ensure the quality of environmental air, soil, and water.
- Limit emissions from construction materials, consumer products, and food packaging.
- Detect adulterants in food and beverages.

Despite varied sample types, environments, and target compounds, analysts face common challenges in developing sustainable methods. Low VOC concentrations often necessitate pre-concentration before analysis, typically via gas chromatography-mass spectrometry. Historic pre-concentration methods have relied on large volumes of hazardous solvents or liquid cryogenic cooling, hindering compliance with green chemistry principles.

This presentation demonstrates how modern thermal desorption-based pre-concentration supports sustainable analytical methods by eliminating solvents and liquid cryogen, minimizing waste, maximizing sample throughput, promoting automation, and targeting sustainable and renewable materials. We showcase examples from environmental air analysis where requirements of US EPA methods for toxic compounds in ambient air (TO-15 and TO-17) were exceeded without liquid cryogenic cooling, achieving up to 40% faster analysis times with hydrogen carrier gas produced from a generator to replace helium cylinders. Additionally, we illustrate how automated high-capacity sorptive extraction eliminates lengthy sample preparation steps and significantly reduces solvent waste in the quality control analysis of hard seltzers.

These technologies are integral to advancing sustainable practices in analytical chemistry, satisfying some of the Green Chemistry and Green Sample Preparation (GSP) principles by preventing waste and maximizing sample throughput.