Denoising and principal component analysis of amplified raman spectra from red blood cells with added silver nanoparticles

Incubated erythrocytes with and without silver nanoparticles (AgNP) were analyzed by Raman spectroscopy, resulting in two Raman spectra datasets. AgNP were added to red blood cells (RBC) in order to enhance the Raman signals. This technique is known as surface-enhanced Raman scattering (SERS). A comparison was made between the Raman spectra with and without AgNP, to test if the SERS had taken place. Since Raman and SERS spectra are considered to be cumbersome due to the noises presented, we applied denoising criteria for detection and removal of noises like cosmic rays, shot, and fluorescence contribution. After this, the principal component analysis (PCA) was performed, in order to reduce the dimensions of the spectra being studied. Only the main key components necessary for a better interpretation of these spectra were considered. All of those noises had to be removed prior to the statistical analysis, to make sure the analysis was really based on the Raman measurements and not on other effects. As a result, RBC Raman spectra with and without AgNP got denoised, obtaining an improvement in its resolution for a better signal reading and data interpretation. Also, the first principal components (PC) were selected from each dataset under scrutiny, based on the weight of their information and their spectrum readability. In conclusion, we were able to represent the given reference system with a more affordable and smaller dimension in which information loss was minimal.