Evaluation of a new Dynamic Background Compensation Algorithm for Improving Aroma Characterisation by Thermal Desorption – GC-MS

Flavour and fragrance profiles typically comprise contributions from a wide range of volatile organic compounds (VOCs) and those at the lowest concentration can have the most profound effect on perceived aroma. Historically, this has made it difficult to carry out meaningful odour profiling using standard sample preparation methods and GC-MS instrumentation. Conventional GC-MS technology is therefore often coupled with analytical thermal desorption to provide both automated sampling versatility and selective concentration. However, quantitative and qualitative analysis of olfactory components at the lowest levels can still be compromised by chromatographic anomalies such as; column bleed, extended solvent tails, air/water interference and unresolved sample matrix components. To address this issue, a new software algorithm has been developed for reprocessing stored GC-MS data and removing background interference. It uses an innovative dynamic approach to distinguish and eliminate mass ions due to the chromatographic background as it changes throughout a run. Such dynamic background compensation (DBC) should be of tangible benefit for a wide range of GC-MS studies, allowing trace target compounds to be detected more reliably against complex and variable analytical interference. Application of the new algorithm to odour profiling by automated TD-GC-MS is described here with examples. The benefits to qualitative and quantitative analysis of trace olfactory compounds are evaluated.