
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 (Spain, 1975) graduated in chemistry from the University of Palma de Mallorca, and obtained his PhD at the University of Valencia. His PhD was awarded the “DL Massart award”. He joined the team of Peter Schoenmakers at the University of Amsterdam, where he worked for 10 years as a post-doc and later on as an assistant professor. He combined his academic activities with his own spin-off company, focusing on both academia and industry in the area of algorithms for chromatographic data treatment. Gabriel currently works as a scientific researcher at the National Spanish Research Council (CSIC) in Madrid (Spain). He is busy developing algorithms for automated analysis of chromatography and mass spectrometry data.

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.

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.

LCGC Europe
We have entered a new stage in the era of accelerations. Moore’s law continues its expansion, increasing exponentially the computer power available. Other accelerations are remarkable, particularly easy access to cloud computing and the expansion and influence of artificial intelligence to practically all sectors of our society.

November 1st 2017