Research project
Liquid Chromatography
PhD research of Jim Boelrijk
Two-dimensional liquid chromatography (2DLC) is a powerful technique to separate and detect trace molecular compounds in complex samples (think of proteins, antibodies, oils, paints, etc.). The separation is based on the difference in “retention” time that it takes for each compound to be carried by a solvent (mobile phase) through a column filled with a material (stationary phase) that interacts with the injected compounds based on a chosen property, such as molecular size, charge or hydrophilicity, as schematically drawn on the right. 2DLC is combined with a detection method such as UV-VIS, IR, or mass spectroscopy (MS), that identifies the separated molecules. As the compounds can have extremely low concentrations (e.g. protein biomarkers, plant hormones, food contaminants), the signals of interest are often buried in the noise of the data and information is being lost. Typical datasets contain several gigabytes of data per measurement.
Analyzing and extracting all relevant information from such data is a challenging task. In addition, 2DLC method development is daunting as there are numerous parameters one can vary in order to achieve optimal separation. Successful implementation of the technique requires months of costly and cumbersome development. In response to this, algorithms are being developed to model chromatographic interaction of analyte molecules with the chemical moieties of the stationary phase, so as to allow prediction of optimal chromatographic conditions. In this project, we will look at the application of AI techniques, such as Bayesian inference, Bayesian optimization and deep learning to tackle parameter optimization, peak detection, baseline correction, and more.
Joint work with Bob Pirok, Patrick Forré, and Bernd Ensing.
Papers
Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization
Jim Boelrijk, Bernd Ensing, Patrick Forré, and Bob Pirok
Analytica Chimica Acta 1242, 340789 (2023)Multi-objective optimization via equivariant deep hypervolume approximation
Jim Boelrijk, Bernd Ensing, and Patrick Forré
ICLR, The Eleventh International Conference on Learning Representations (2023)Bayesian Optimization of Comprehensive Two-dimensional Liquid Chromatography Separations
Jim Boelrijk, Bob Pirok, Bernd Ensing, and Patrick Forré,
J. Chromatogr. A 1659, 462628 (2021)