
News|Articles|June 15, 2026
Exploring the Limits of Mycotoxin Analysis
Author(s)Lidija Kenjeric, Alasdair Matheson
During her PhD at BOKU University Vienna, Lidija Kenjerić developed a multi-class UHPLC–MS/MS quantitative method covering 931 mycotoxins and secondary metabolites within an 11-min runtime, providing a scalable solution for routine testing, regulatory, and emergency response.
Advertisement
Advertisement
In the paper “Exploring the Limits of UHPLC–MS/MS with Polarity Switching Towards the Quantification of 931 mycotoxins and Other Secondary Metabolites in Cereal-based Food”, what was the scientific and regulatory rationale for developing an 11-min ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC–MS/MS) method capable of quantifying 931 mycotoxins and secondary metabolites in cereal-based foods?1
Related to this article

Max Besenhard explains how hybrid mechanistic and machine learning models can make HPLC method development quicker, more robust and ready for autonomous digital twins.

Sebastian Böcker discusses taking retention prediction beyond reversed-phase to HILIC, what changes elution order in method transfer, and sharing data via RepoRT.

Sebastian Böcker discusses how predicting retention order, not time, makes liquid chromatography retention time prediction transferable across laboratories.

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.
Advertisement
Advertisement


