Simulated Impact of Machine Learning on 2D-LC Optimization

The chromatographic community has for long pondered the promise of machine learning. CAST member Jim Boelrijk simulated the feasibility of Bayesian Optimization for automating method development in comprehensive 2D-LC.

The prospect of simplified method development for 1D and 2D-LC separations has long been sought for. Indeed, past CAST publications, but also those by many other groups, have investigated the classical approach used also extensively for 1D separations using empirical retention models. Meanwhile, machine-learning tools have emerged as an alternative across STEM fields. It is thus not surprising that its application has been of interest to several groups in the chromatographic community.

Together with dr. Patrick Forré from the Institute of Informatics at the University of Amsterdam, as well as researchers from the Van ‘t Hoff Institute for Molecular Sciences dr. Bernd Ensing (Computational Chemistry) and CAST member dr. Bob Pirok (Analytical Chemistry), PhD candidate Jim Boelrijk (Institute of Informatics) studied the feasibility of using Bayesian Optimization for the optimization of method development in 2D-LC separations.

For any machine learning tool to operate effectively within the context of method optimization, the use of a chromatographic response function or objective function is of paramount importance. Such functions quantify a particular quality descriptor that represent the performance of the separation method. Known examples in the field of 2D separations are peak capacity and orthogonality. However, maximised peak capacity or orthogonality does not necessarily translate into a high information yield. Resolution has also been investigated but its use is impaired by scaling issues. Consequently, the present study employed the concept of connected components (Figure 1). 

Figure 1. Example of labelling of a chromatogram by the chromatographic response function. Blue dots denote components separated with resolutions higher than 1 from all other peaks; red dots denote peaks that are within proximity to neighbors and are clustered together, illustrated by the red lines.

The simulated method development cycles yielded a larger number of separated peaks clusters (connected components) relative to the random and grid search algorithms (Figure 2).

Figure 2. Comparison of the random search, grid search and Bayesian optimization algorithm for sample A (top-left), B (top-right), C (bottom-left) and D (bottom-right) for 100 trials. The vertical black dashed line shows the maximum observed in the grid search (out of 11,664 experiments), while the blue and orange bars denote the best score out 104 iterations for the random search and Bayesian optimization algorithm, respectively.

The study by Boelrijk demonstrated that Bayesian optimization is a viable method for optimization of chromatographic experiments with many method parameters, and therefore also for direct experimental optimization of simple to moderate separation problems. This study was conducted under a simplified chromatographic reality (Gaussian peaks and equal concentration of analytes, generated compounds). Boelrijk thus remains interested to continue this research by working towards actual direct experimental optimization.

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