Automating data processing and analysis

Gas chromatography–mass spectrometry (GC–MS) and ion chromatography profile wasabi and mustard heat and acidity.

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?

Reversed-phase high-performance liquid chromatography (RP-HPLC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) show frequent milking alters cobra venom toxin makeup.

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 (GC) analysis ties prenatal mindfulness to shifts in placental fatty acids.

Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC–qTOF–MS) reveal metabolic shifts in tricuspid regurgitation.