Food contact materials contain low molecular weight additives and processing aids which can migrate into foods leading to trace levels of contamination. Food safety is ensured through regulations, comprising compositional controls and migration limits, which present a significant analytical challenge to the food industry to ensure compliance and demonstrate due diligence. Of the various analytical approaches, LC-MS/MS has proved to be an essential tool in monitoring migration of target compounds into foods, and more sophisticated approaches such as LC-high resolution MS (Orbitrap) are being increasingly used for untargeted analysis to monitor non-intentionally added substances. This podcast will provide an overview to this area, illustrated with various applications showing current approaches being employed.
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.