Bob Pirok on the Human Factor in Machine Learning for Automated Method Development

At Pittcon 2026 in San Antonio, Texas, USA, LCGC International spoke with 2026 LCGC International Emerging Leader Award Winner—Bob Pirok from the University of Amsterdam, Amsterdam, The Netherland — about his presentation: Machine Learning Algorithms in Automation for LC–MS Method: An Update on Bayesian Optimization Kernels.¹

In this video interview, Pirok responds to the following questions:

• Many machine-learning approaches assume the algorithm will discover the optimum by itself. In chromatography we already know a lot about retention, selectivity, and gradient effects. How important is it to encode chromatographic knowledge into these algorithms rather than relying purely on data-driven optimization?

• If we look 10 years ahead, do you think chromatographic method development will still primarily be done by humans, or will automated systems become the default?

Pirok’s research focuses on applying chemometrics and machine learning (ML) to analytical chemistry, particularly in multidimensional chromatography and automated method development and has published highy cited papers in this are.^2,3,4,5,6

His lecture explored how Bayesian optimization (BO) algorithms can automate LC–MS method development.¹ In these systems, the algorithm iteratively selects experimental conditions, analyzes the resulting chromatographic performance, and learns how to propose improved conditions in a closed-loop workflow. Platforms such as AutoLC have demonstrated that fully unsupervised method development is feasible.

References

1. Pittcon LCGC International Lifetime Achievement and Emerging Leader Award Session: https://app.swapcard.com/event/pittcon-2026/planning/UGxhbm5pbmdfNDMxODgzNg==
2. Bos, T. S.; Boelrijk, J.; Molenaar, S. R. A.; et al. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography. Anal Chem 2022, 94 (46), 16060–16068. DOI: 10.1021/acs.analchem.2c03169
3. Boelrijk, J.; Ensing, B.; Forré, P.; Pirok, B. W. J.
   Closed-Loop Automatic Gradient Design for Liquid Chromatography Using Bayesian Optimization. Anal Chim Acta 2023, 1242, 340789.
   DOI: 10.1016/j.aca.2022.340789
4. Molenaar, S. R. A.; Bos, T. S.; et al. Computer-Driven Optimization of Complex Gradients in Comprehensive Two-Dimensional Liquid Chromatography. J Chromatogr A 2023, 1707, 464306. DOI: 10.1016/j.chroma.2023.464306
5. Boelrijk, J.; Molenaar, S. R. A.; Bos, T. S.; et al. Enhancing LC×LC Separations through Multi-Task Bayesian Optimization. J Chromatogr A 2024, 1726, 464941. DOI: 10.1016/j.chroma.2024.464941
6. Bos, T. S.; Desport, J. S.; Buijtenhuijs, A.; et al. Composition Mapping of Highly Substituted Cellulose-Ether Monomers by LC–MS and Probability-Based Data Deconvolution. J Chromatogr A 2023, 1689, 463758. DOI: 10.1016/j.chroma.2022.463758
   
   

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