Statistical approach for solid-state NMR spectra of cellulose derived from a series of variable parameters

Principle component analysis (PCA) was used to extract components from the solid-state nuclear magnetic resonance (NMR) spectra of bacterial cellulose (BC). Polymers such as cellulose have several domain structures, and their structure and dynamics are reflected in the variety of solid-state spectra derived from different parameters. The complexity of the obtained spectra makes the analysis of spectra from relaxation measurements difficult. Multivariate analysis, such as PCA, has been suggested as an option to improve NMR spectral analyses. In this study, we attempted to extract peak components from cross polarization (CP) experiment data from the variable contact time spectra of BC by using PCA. The extracted peaks were annotated according to previous reports, and the existence of crystalline Iα was clearly recognized. The important components of the BC structure (that is, crystalline Iα, amorphous form and mobility) were separated in the PCA-loading plot and CP curve. A multivariate analysis was used to extract components in solid-state nuclear magnetic resonance (NMR) spectra from bacterial cellulose (BC). Polymers such as cellulose have several domain structures, and their structure and dynamics are reflected in the variety of solid-state spectra derived from different parameters. Multivariate analysis, such as principle component analysis (PCA), is suggested as an option to improve analyses of complex NMR spectra from relaxation measurements. In this study we demonstrate the extraction of peak components using PCA from cross polarization experiment data with variable contact time spectra of BC.