Abstract
Abstract
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.