Chromatographic methods such as HPLC, GC, and CGE are essential for analytics across various industries. Optimizing these methods to ensure high accuracy and precision is crucial but challenging due to numerous parameters and complex chromatograms. Often, chromatographic targets (e.g., resolution, peak-to-valley) are extracted and modeled, but interpreting these results and their impact on the chromatogram is difficult.

In collaboration with Chris Gotwalt at JMP, we have developed a novel approach to model synthetic chromatograms in-silico based on design of experiments (DOE). We demonstrate how individual peaks in chromatograms can be identified using JMP Functional Data Explorer and modeled via the Generalized Regression platform. Subsequently, the synthetic chromatograms are visualized and optimized in the Profiler.

This innovative approach allows the impact of various DOE parameters to be simulated on complete chromatograms for the first time in JMP. It showcases JMP’s interactive capabilities, offering a new understanding of chromatographic methodologies and addressing new regulatory requirements, such as ICH Q14. We demontrate the potential of this feature, which is expected to be rolled out in JMP 19, with two real-world examples.