LCGC TV: Kevin Schug on Vacuum Ultraviolet Detection for Gas Chromatography
Gas chromatography (GC) is often regarded as a mature technology and some say that the application base has become routine; however, conventional detectors may not be able to keep up with the speed of separations that can be achieved today. In this new video Kevin Schug from the University of Texas Arlington, USA, talks about the development of a new vacuum ultraviolet (UV) detector, from the initial idea to the fundamentals of its operation.
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.