Approaches to Accelerate Liquid Chromatography Method Development in the Laboratory Using Chemometrics and Machine Learning

Liquid chromatography (LC) is the single largest analytical field in terms of people involved and money spent. LC is crucial for almost all public and private sectors and the technique has seen tremendous technological advancements. Nevertheless, 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. Method optimization strategies that can simultaneously optimize the large number of parameters involved are therefore of great interest to chromatographers. This review examines different workflows that have been designed and used to facilitate and/or automate method development. In particular, focus is paid to 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. Finally, the opportunities to use machine learning to achieve these goals is addressed.