Estimating retinal blood oxygenation from diffuse reflectance spectra of semi-infinite tissue using principal component analysis

Diffuse reflectance spectroscopy (DRS) is a promising technique for non-invasive monitoring of tissue oxygen saturation (StO2). However, the interpretation of DRS data can be complicated by the presence of confounding factors such as the volume fraction of blood, tissue scattering, and lipid content which both absorb and scatter. Principal component analysis (PCA) is a multivariate statistical method that can help overcome these challenges by extracting relevant information from complex datasets and providing new dimensions used to estimate parameters such as concentrations. In this study, we present a PCA-based algorithm for estimating retinal StO2 from DRS measurements. We evaluated the performance of our algorithm using simulated data and experimental measurements on a retinal tissue phantom model. Our results show that the PCA-based algorithm can estimate the value of StO2 with a root-mean-square error of 6.38% in the presence of confounding factors. Our study demonstrates the potential of PCA as a powerful tool for extracting the concentration of components from complex DRS.