Analysis of 40 Per- and Polyfluoroalkyl Substances by US EPA Method 1633 Part 3: Application to Sand, Soil, Sediment, Sludge, Biosolid and Other Solid Samples
Download Part 3 to explore complete method protocols, performance data, and insights for PFAS analysis in soils and sediments. Part 4 will cover PFAS analysis in fish and animal tissue samples.
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.