Advances in HPLC: An LCGC International Supplement
HPLC is being reinvented on every front—smaller, greener instrumentation; smarter, AI-assisted method development; next-generation column materials; and new approaches to age-old method challenges. This special supplement from LCGC International brings together leading voices in separation science to explore exactly where the field is headed, from capillary-scale systems built for sustainability to data tools that cut through messy LC–MS/MS data sets. Whether you're developing methods for complex biopharmaceuticals or just want to stay ahead of where liquid chromatography is going next, this supplement delivers the research and expert insight to keep you there.
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.