Key Points:
- Surrogate modelling is emerging as a powerful tool in chromatographic method development, enabling more efficient experimentation, guiding optimization strategies, and supporting predictive analysis.
- Schug highlights how machine learning-based surrogate models can enhance chromatographic system performance, offering potential advantages over traditional methods such as response surface modeling, and opening doors for real-time control, predictive maintenance, and data-driven decision-making in industrial settings.
In this video interview, LCGC International spoke with Kevin Schug, a long-standing and active member of the LCGC International Editorial Advisory Board. Schug is a professor and the Shimadzu distinguished professor of analytical chemistry in the Department of Chemistry and Biochemistry at The University of Texas at Arlington (UTA), in Texas, USA.
This discussion focuses on the role of surrogate modelling in chromatographic method development and process optimization—an area of growing interest as analytical workflows become increasingly data-driven and resource-conscious. Drawing from his recent work, Schug outlines how surrogate models can be applied to enhance experimental efficiency, guide optimization strategies, and support predictive capabilities in chromatographic systems.
This interview will be of particular interest to researchers and practitioners engaged in SE-SFC and other complex chromatographic techniques who are exploring advanced modelling tools to address challenges in method development and system performance.
Schug spoke to LCGC International at HPLC 2025 to answer the following questions:
- The second part of your talk focused on surrogate modelling? What is surrogate modelling and what application areas did you investigate with this approach?
- What benefits can surrogate modelling potentially benefit chromatographers?
- How can surrogate modeling be adapted for optimizing chromatographic separation processes where experimental runs are expensive or time-consuming?
- In what ways might surrogate-assisted optimization outperform traditional response surface methodologies commonly used in chromatography method development?
- Could high-fidelity simulations of mass transfer or diffusion in chromatographic columns be effectively approximated by surrogates, and what would be the limitations?
- What role could machine learning-based surrogate models play in real-time control or predictive maintenance of chromatographic systems in an industrial setting?
Kevin A. Schug is a professor and the Shimadzu distinguished professor of analytical chemistry in the Department of Chemistry and Biochemistry at The University of Texas at Arlington (UTA), Texas, USA. He received his B.S. degree in chemistry in 1998 from the College of William and Mary and his Ph.D. degree in chemistry from Virginia Tech in 2002. From 2003–2005, he performed postdoctoral research at the University of Vienna in Austria. Since joining UTA in 2005, his research has been focused on the theory and application of separation science and mass spectrometry for solving a variety of analytical and physical chemistry problems in the fields of forensic, environmental, pharmaceutical, biological, and energy research. He has over 220 peer-reviewed publications and over 700 presentations, posters, and invited talks to his group’s credit. He has been the primary mentor and research advisor to more than 40 graduate and 80 undergraduate students. Schug has received several research awards, including the 2009 Emerging Leader Award in Chromatography by LCGC Magazine and the 2013 American Chemical Society Division of Analytical Chemistry Young Investigator in Separation Science Award. In 2024, he received the Silver Jubilee Medal from The Chromatographic Society (ChromSoc) (UK). For his teaching, he received the 2014 University of Texas System Regents’ Outstanding Teaching Award, and in 2017, he was awarded the J. Calvin Giddings Award for Excellence in Analytical Chemistry Education by the American Chemical Society. He is a fellow of both the University of Texas System and the U.T. Arlington’s Academy of Distinguished Teachers. He is also a fellow of U.T. Arlington’s Distinguished Service Leaders.
