Functional Data Analysis for HPLC Optimization Case Study

This case study presents the application of functional data analysis and functional design of experiments (FDOE) to optimize an analytical method for the quantification of two biological components using high-performance liquid chromatography (HPLC). The study focuses on improving the separation of two closely related sophorolipid biosurfactants that co-elute on the chromatogram, making quantification challenging. Key tasks include applying statistical design of experiments to identify optimal HPLC settings and using functional data analysis to understand how curve shapes change with varying input factors. The experimental design involves altering the mobile phase flow rate and column temperature to enhance peak separation. The analysis utilizes tools such as the Graph Builder and Functional Data Explorer to visualize data and model the chromatograms effectively. The study aims to characterize the variation in peak shapes and identify the best settings for achieving clear separation of the target compounds, ultimately enhancing the reliability of the HPLC method.