Modeling Synthetic Chromatograms in JMP 19: Updates and New Applications (2026-EU-PO-2745)

Chromatographic techniques such as HPLC, GC, and CGE are essential for analytical workflows across industries. However, optimizing these methods remains challenging due to numerous parameters and the complexity of chromatograms. Traditionally, performance metrics such as resolution or peak-to-valley ratios are extracted and modeled, but linking these metrics back to the full chromatogram is often difficult.

In collaboration with Chris Gotwalt at JMP, we developed an innovative approach to model synthetic chromatograms in-silico using design of experiments (DOE). Leveraging JMP 19’s Functional Data Explorer and generalized regression, individual peaks can be identified, modeled, and visualized. The Profiler then enables interactive optimization, allowing users to simulate the impact of DOE parameters on entire chromatograms for the first time in JMP.

This poster not only demonstrates the implementation in JMP 19, but also presents recent updates and new real-world application examples. The approach provides a deeper understanding of chromatographic behavior, supports method development, and addresses evolving regulatory expectations such as ICH Q14. By integrating advanced modeling with JMP’s interactive capabilities, this workflow offers a powerful, practical solution for modern chromatographic method optimization.