Gabriel Vivó-Truyols outlines the benefits and challenges of combining graph theory with Bayesian statistics to automate peak assignment and alignment.

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.