Automated Static and Dynamic Headspace Analysis with GC-MS for Determination of Abundant and Trace Flavour Compounds in Alcoholic Beverages Containing Dry Extract
Direct injection for gas chromatographic profiling of alcoholic beverages is usually preferable, but where spirits and liquors contain appreciable amounts of non-volatile material, some mode of pre-treatment may be required to avoid both inlet and column contamination. This consideration applies in particular to products aged for extended periods in wooden barrels and especially products containing added sugar, as volatile artefacts from sugar decomposition in the hot injection port can also complicate the chromatogram.
How feasible is it to integrate this Bayesian graph-theory methodology into existing commercial chromatography software workflows for semi-automated data curation in routine analytical laboratories?
Gabriel Vivó-Truyols outlines the benefits and challenges of combining graph theory with Bayesian statistics to automate peak assignment and alignment.
This week, Chromatography Online’s coverage spanned lipidomics-based early cancer detection, career paths in separation science, machine learning and uncertainty-driven approaches to LC method development, new EU wastewater rules for pharmaceutical micropollutants, automated Separation Quality Factor (SQF) evaluation in chromatography data systems, and solutions to GLP-1 peptide analysis challenges.
Gabriel Vivó-Truyols from the Spanish Council for Scientific Research, Madrid, Spain. discusses his work on using graph theory and Bayesian statistics for peak assignment and chromatographic alignment.
Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC–qTOF–MS) reveal metabolic shifts in tricuspid regurgitation.