Gabriel Vivó-Truyols outlines the benefits and challenges of combining graph theory with Bayesian statistics to automate peak assignment and alignment.
News|Articles|September 15, 2025
Analyzing Explosive Traces Using 2D-LC
Author(s)Will Wetzel
Fact checked by: John Chasse
Advertisement
Advertisement
In a recent study led by Bob Pirok and Rick S. van den Hurk of the Van ’t Hoff Institute for Molecular Sciences at the University of Amsterdam a novel heart-cutting two-dimensional liquid chromatography (2D-LC) workflow capable of simultaneously separating and detecting both inorganic and organic explosive residues from a single injection was developed (1). This latest innovation has implications for post-blast forensic analysis.
Related to this article
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.
Leon Niezen and Gert Desmet discuss uncertainty-driven LC method development, peak overlap, and moving from in silico to real data.
Leon Niezen and Gert Desmet describe how predictive uncertainty can make automated LC method development more reliable.
Sam Whitmarsh explores how retention-time models handle interlaboratory data, novel chemotypes, unusual phases and ionisable compounds, and what labs need to do to build useful in-house models.
Sam Whitmarsh describes a practical workflow for predicting reversed-phase LC retention times using ensemble machine learning and open chromatographic data.
Viktoriia Turkina discusses toxicity prediction for unknown LC–HRMS features using machine learning.
ChromSoc’s Data Analytics and AI in Chromatography symposium will debate whether AI could ever replace chromatographers, alongside pharma case studies.
Advertisement
Advertisement