Digital Dewaxing of Raman Hyperspectral Images: Application to Paraffin‐Embedded Skin Biopsies

Raman spectral imaging is a powerful tool for studying the molecular composition of biological samples, typically preserved in paraffin to protect their molecular structures. However, the intense signal from paraffin can interfere with the analysis, necessitating the removal of pure paraffin pixels for accurate imaging. Extended multiplicative signal correction (EMSC) has recently emerged as an effective method to neutralize the paraffin signal in recorded Raman spectral images. This article introduces a novel methodology combining EMSC with multivariate analysis techniques to separate paraffin and tissue pixels. Blind source separation techniques such as independent component analysis (ICA), non‐negative matrix factorization (NMF), principal component analysis (PCA), and singular value decomposition (SVD) are applied to Raman images acquired solely on paraffin to model and extract the pure paraffin component accurately. To validate our approach, we employ k‐means clustering on the corrected spectra obtained through the proposed method and compare the results with those from the traditional EMSC method that does not utilize pure paraffin components modeled by blind source separation techniques. The deparaffinized spectra are then used to construct Raman images of human tissues, which are compared with hematoxylin‐eosin (H&E) stained tissues for verification. This study demonstrates the potential of Raman spectroscopy, combined with EMSC and blind source separation techniques, as a digital dewaxing tool for analyzing paraffin‐embedded tissues.