The goal of this project is to explore the predictive diagnostic value of the complex spectra generated by metabolites adsorbed onto the HP Surface Emission Raman Spectroscopy substrates from cancerous and healthy cervical tissue in lab. The project has two primary components. The laboratory component entails cell culturing and preparation of the samples for data collection. Then when the data is collected, machine learning algorithms will be used to distinguish healthy from cancerous cell by the SERS spectra.