he United States Pharmacopeia (USP)
has designations for all columns stationary phases used in the monograph methods. These designations
outline the stationary phase type, i.e. fully porous or solid-core, and any ligand attachments, i.e. C18 or
Phenyl to be used.1 However, beyond that no column specifics are given. With a multitude of columns
that fit into the different designations, understanding that not all columns are the same is vital when
selecting a stationary phase for a monograph method. This application note examines three columns
that all fit into the L1 designation when analyzing paracetamol impurities. Selectivity differences
between the columns are considered in relation to the impurities.
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.