Pharmaceutical separations are necessary to mass-produce novel treatments for emerging diseases. Rare, heterogeneous surface chemistry hampers separations by generating chromatographic tails that cause peak overlap. The chemical source of tailing is thoroughly detailed in microscopic terms by the stochastic theory but difficult to assess from macroscale measurements that guide optimization. Chemometric-driven chromatogram analysis that achieves a microscale understanding of surface chemistry could help direct tuning of column chemistry to reduce tailing. Here, we improve upon previous graphical metrics with our own metric, the Distribution Function Ratio (DFR), which is compatible with stochastic theory and capable of bridging the gap between macroscale chromatograms and microscale surface chemistry. Further, we prove the DFR can provide predictive analysis of surface chemistry, an application that could be used in online chromatographic instruments. Establishing an analytical metric that is simple to implement provides mechanistic, chemometric guidance for future development of surface chemistry in chromatographic columns.