Marc Monthioux

Removing background signals is a common preprocessing step, but it is not without drawbacks. In X-ray diffraction data, background correction can artificially symmetrize diffraction peaks, which becomes a critical issue for lamellar materials such as graphenic carbon when the Laue indices lie in the plane (e.g., the 10 and 11 peaks). We discuss several approaches to background correction and their implications for the resulting data. In Raman spectroscopy, defects activate the phonon density of states, leading to higher intensity below the D band than above the G band, with respect to the Raman shift. After discussing the linear and circular polarization on the Raman selection rules, we show how flattening the background—a widely used measure of disorder—alters the I_D/I_G ratio. Finally, principal component analysis (PCA) provides a useful preliminary exploration of data structure; however, because its components may include negative contributions, it cannot be directly applied to spectral decomposition. In contrast, non-negative component decomposition offers an optimal way to preserve the Raman background, even in the presence of luminescence. We confirm our analysis with ANOVA p-values.