We present a comprehensive analysis of spectral datasets across various techniques (IR. Raman, GC-MS, LC-MS, NMR, UV-Vis) using Principal Component Analysis (PCA) that reveals underlying patterns and correlations that remain invisible when examining individual spectral measurements in isolation. This study demonstrates how PCA can enhance scientific discovery by extracting insights from high-dimensional data. Our findings show that PCA successfully identifies spectral clusters and feature correlations, demonstrating its value for improving analytical workflows.
