Application of multivariate spectral analyses in micro-Raman imaging to unveil structural/chemical features of the adhesive/dentin interface.

This study presents the application of multivariate analyses to analyze micro-Raman spectral imaging data in reference to the adhesive/dentin interface as well as comparison with univariate analysis. The univariate statistical methods, such as mapping of specific functional group peak intensities, do not always detect functional group positions and quantities due to peak overlapping. A comprehensive chemical analysis of the adhesive/dentin interface, along with the multivariate statistical methods, principal component analysis, and fuzzy c-means clustering, is studied. Compared to univariate analysis, multivariate methods present the entire hyperspectral information from the specimen in a concise and uncorrelated way. Apart from the ease with which information can be extracted and presented, multivariate methods also highlight minute and often important variations in the spectra that are difficult to observe using univariate methods. The results show for the first time the clear chemical and structural classifications in the adhesive/dentin interface at successively greater resolutions.