SciX 2025: October 5-10, 2025

Principal Component Analysis (PCA) is a powerful statistical technique used to simplify complex datasets by reducing their dimensionality while retaining most of the original variability. Applied to spectral data, it can review hidden trends not observed by traditional one by one spectral examination.

Using the Trendfinder application with the KnowItAll software, we present PCA performed on several database collections including: IR, HNMR, MS.  It can also be applied to Raman, UV-Vis, and chromatographic data sets as well. We successfully distinguished between different sample types without prior knowledge, proved effectiveness across diverse spectroscopic methods, and revealed meaningful relationships between experimental parameters and spectral characteristics.