  Motivated by the analysis of spectrographic data, we introduce a functional
graphical model for learning the conditional independence structure of spectra.
Absorbance spectra are modeled as continuous functional data through a cubic
B-spline basis expansion. A Gaussian graphical model is assumed for basis
expansion coefficients, where a sparse structure is induced for the precision
matrix. Bayesian inference is carried out, providing an estimate of the
conditional independence structure between frequency bands of the spectrum. The
proposed model is applied to the analysis of the infrared absorbance spectra of
strawberry purees.
