Multivariate analysis of hyperspectral stimulated Raman scattering microscopy images

Coherent Raman scattering microscopy is an attractive new technology for label-free imaging. Both coherent anti-Stokes (CARS) and stimulated (SRS) offer the possibility to record hyperspectral imaging data. While analysis of spontaneous data multivariate methods are nowadays routinely employed, until date most interpreted using univariate analysis. In this work, we report a quantitative comparison performance different used SRS from model samples. Our show all samples that outperform Using metrics quantify method performance, find tested, curve resolution (MCR) gives best results. We combination with selection essential components based on first-order autocorrelation, simple workflow MCR-based