Advances in chromatographic technology depend to a large extent on the development of
novel stationary phases with enhanced separation efficiency to meet the ever-growing …

Liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics is
constantly challenged by large-scale and unambiguous metabolite annotation in complex …

There is an increasing need for comparable and harmonized retention times (t R) in liquid
chromatography (LC) among different laboratories, to provide supplementary evidence for …

The demand for rapid column screening, computer-assisted method development and
method transfer, and unambiguous compound identification by LC/MS analyses has pushed …

Food fraud is currently a growing global concern with far‐reaching consequences. Food
authenticity attributes, including biological identity, geographical origin, agricultural …

Retention time prediction in high-performance liquid chromatography (HPLC) is the subject
of many studies since it can improve the identification of unknown molecules in untargeted …

In metabolomics, retention prediction methods have been developed based on the structural
and physicochemical characteristics of analytes. Such methods employ regression models …

Machine learning is a popular technique to predict the retention times of molecules based
on descriptors. Descriptors and associated labels (eg, retention times) of a set of molecules …

Retention time (RT) prediction contributes to identification of small molecules measured by
high-performance liquid chromatography coupled with high-resolution mass spectrometry …

The combination of retention time (RT), accurate mass and tandem mass spectra can
improve the structural annotation in untargeted metabolomics. However, the incorporation of …