Utilizing Machine Learning for Raman Spectral Data Analysis of Brain Tissue

Raman spectroscopy has shown great promise in classifying biomedical samples. While conventional spectral analysis techniques have been employed to extract meaningful patterns from Raman data, emerging developments in machine learning (ML) offer new opportunities to advance the field. We employed Raman spectroscopy (RS) to analyze hippocampal tissue sections from rats subjected to chronic constriction injury (CCI) exposed to Cannabidiol (CBD) vapor versus Sham controls. Through examination of spectral features in the Raman data, we detected distinct molecular bond signatures indicative of changes in key biomolecules, including proteins, lipids, and nucleic acids. These signatures offer insights into CBD vapor-induced biochemical alterations in the hippocampus and have potential relevance to neuropathic pain mechanisms linked to CCI. Our Raman-based approach enabled us to capture information about biochemical modifications in the hippocampal region exposed to CBD vapes. We applied supervised machine learning (ML) models (Random Forest, SVM) to classify Raman spectra obtained from the brain tissues. The machine learning algorithms effectively captured patterns in the spectral data, enabling us to accurately differentiate between treated and control groups. Our models achieved high classification performance, showing that CBD vapor exposure induces distinct biochemical alterations in brain tissue detectable via Raman spectroscopy. Our results support the use of ML as a powerful tool for spectral analysis in complex biological systems.