Abstract
This study focuses on refining Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for chromatographic profiling to analyze chemical changes in Serpocaulon sessilifolium extracts from the Costa Rican rainforest. High-Performance Liquid Chromatography (HPLC) with a diode array detector (DAD) and Mass Detector were employed, where traditional analyses often discard valuable spectral data beyond the maximum absorption wavelength. To optimize the analysis, Principal Component Analysis (PCA) were used to select the optimal number of components for MCR-ALS. Fern extracts, stored under varying conditions -refrigeration, warm temperatures, and UV light exposure- are analyzed over time to study their chemical stability. The decomposition identifies key chemical constituents, revealing that warmer conditions and UV exposure accelerate degradation, with significant shifts in chemical composition observed over time. MCR-ALS analysis allows detailed tracking of chemical changes, showing emerging peaks and shifts in concentration, particularly in the more reactive compounds, enhancing resolution and overcoming challenges such as peak interference and co-elution. The study highlights the differences between UV-absorption data and mass spectrometry, where mass spectrometry offers more detailed resolution but requiring greater computational resources. The use of both methods provides a comprehensive understanding of the chemical dynamics of the extracts. This research demonstrates the potential of MCR-ALS, combined with advanced statistical tools, for improving chromatographic analysis and contributing to botanical and natural product research.