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Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization.

Jim Boelrijk1, Bernd Ensing2, Patrick Forré1

  • 1AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands; AMLab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, the Netherlands.

Analytica Chimica Acta
|January 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian optimization algorithm for automated gradient program development in liquid chromatography (LC). The novel approach efficiently optimizes complex sample separations with minimal measurements.

Keywords:
Bayesian optimizationClosed-loop method developmentExperimental designLiquid chromatographyMachine learning

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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Computational Chemistry

Background:

  • Complex samples demand advanced analytical techniques for comprehensive characterization.
  • Automated method development in chromatography is crucial for efficiency and reproducibility.
  • Existing methods may struggle with complex matrices or lack unsupervised optimization capabilities.

Purpose of the Study:

  • To develop and evaluate a Bayesian optimization algorithm for automated, unsupervised gradient program optimization in liquid chromatography (LC).
  • To investigate both single-objective and multi-objective Bayesian optimization strategies for complex separations.
  • To assess the algorithm's performance and flexibility in optimizing chromatographic conditions.

Main Methods:

  • Development of a Bayesian optimization algorithm tailored for LC, utilizing a Gaussian process model with a novel covariance kernel.
  • Direct interfacing of the algorithm with the chromatographic system for unsupervised learning.
  • Application of single-objective and multi-objective Bayesian optimization to separate complex dye mixtures (n>18 and n>80).
  • Investigation of performance differences using a retention modeling example.

Main Results:

  • Both single-objective and multi-objective Bayesian optimization strategies achieved satisfactory separation optima in fewer than 35 measurements.
  • The multi-objective strategy demonstrated power and flexibility in exploring the Pareto front for complex separations.
  • The multi-objective approach facilitated trade-offs between multiple objectives without requiring prior knowledge.

Conclusions:

  • Bayesian optimization offers a robust and efficient strategy for automated method development in liquid chromatography, particularly when retention modeling is challenging.
  • The multi-objective Bayesian optimization approach provides significant advantages in flexibility and handling trade-offs for complex analytical challenges.
  • While scalable, the optimization's performance may be limited by the number of simultaneously optimized parameters.