Gaussian graphical modeling for spectrometric data analysis

Motivated by the analysis of spectrometric data, a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spectrum is introduced. The spectra are modeled as continuous functional data through a B-spline basis expansion and a Gaussian graphical model is assumed as a prior specification for the smoothing coefficients to induce sparsity in their precision matrix. Bayesian inference is carried out to simultaneously smooth the curves and to estimate the conditional independence structure between portions of the functional domain. The proposed model is applied to the analysis of infrared absorbance spectra of strawberry purees.

Subjects

Bayesian inference

Birth-death process

Functional data analysis

Model selection

Spectrum analysis

DDC Class

004: Informatik

510: Mathematik

Funding Organisations

Università Cattolica del Sacro Cuore, Italy

More Funding Information

Research supported by sharing.city.college, organized by ahoi.digital, and funded by the Ministry of Science, Research, Equalities and Districts of the Free and Hanseatic City of Hamburg. Funding: the research of the fourth, fifth and sixth author has been partially supported by a grant from Università Cattolica del Sacro Cuore, Italy (track D1).

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http://rightsstatements.org/vocab/InC/1.0/

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2103.11666.pdf

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4.22 MB

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Gaussian graphical modeling for spectrometric data analysis