Liquid chromatography (LC) plays an important role in almost all public and private sectors. It is thus not surprising LC represents of one of the largest analytical fields in terms of resource requirements. For this reason the field is still significantly in development. This has led to the advent of high-resolution separations and multi-dimensional chromatography. However, the analytical separations technology is evolving faster than the ability of humanity to employ it effective.
Indeed, separations are often performed under suboptimal conditions and technological capabilities remain unused. Because expert knowledge and method development time are increasingly scarce, methods are often inefficient. Exploiting the full technological capabilities of liquid-phase separation technology requires deep knowledge and great time investments.
To tackle this, Pirok started the UPSTAIRS (Unleashing the Potential of Separation Technology to Achieve Innovation in Research and Society) project in 2022 using funding from the Dutch Research Council (NWO). The goal of the project is to improve the accessibility of state-of-the-art separation technology by connecting chromatographic theory to practice using computational methods. The group aims to develop the “AutoLC” workflow in which unsupervised method development becomes possible for (2D-)LC-MS methods.
In hisf eature article on machine learning and method development, CAST researcher Gerben van Henten along with Tijmen Bos and Bob Pirok examines the role of computational methods and machine learning to facilitate automated method development. Method optimization strategies that can simultaneously optimize the large number of parameters involved are therefore of great interest to chromatographers [1].
The article focuses particularly the implementation of computer-aided workflows for the optimization of kinetic and thermodynamic parameters in LC, as well as on the possibilities to conduct this in a closed-loop fashion.
In the feature article on machine learning and method development, Van Henten et al. return to the fundamental equation that quantifies resolution into three components: retention, selectivity and chromatographic efficiency. Different strategies are discussed and place into context of this distinction (Figure).
The article was published in the June edition of LC-GC Europe which was released at the largest conference for LC separations HPLC2023 in Düsseldorf, Germany. The article can be read free of charge here.
References
- Approaches to Accelerate Liquid Chromatography Method Development in the Laboratory Using Chemometrics and Machine Learning, G.B. van Henten, T.S. Bos, B.W.J. Pirok, LCGC Europe, 2023, 36(6), 202-209, DOI: 56530/lcgc.eu.rh7676j5.