Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride ( CaF 2 ) and mirrored stainless-steel ( SS ) slides. Raw SS spectra were more intense than CaF 2 , and routine dewaxing left prominent paraffin peaks on CaF 2 that were largely absent on SS . Extending CaF 2 xylene immersion to 48 h reduced key paraffin peaks by 11–23 %, aligning its biochemical profile with SS . Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65–0.66), while CaF 2 improved from 0.41–0.57 to 0.65–0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF 2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.