Principal component analysis for dental shade color

Objectives tional 3-dimensional color spaces such as L*a*b* or L*C*h have a limitation in that colors of materials can only be separated on the same hyperplane. Therefore, it would be useful to find appropriate axes for dental color analysis by analyzing spectral data itself, rather than conventional 3-dimensional color spaces. s pectral data are detailed color spectra with narrow spectral bands over a continuous spectral range. We acquired hyperspectral data of the shade guides without specular reflection, and standardized them as reflectance data. Then, reflectance data were weighed by luminous efficiency function, and used in principal component analysis (PCA). Principal components (PCs) and their contribution, and values of respective shades to respective PCs were calculated as PC scores. s te contribution rate of 1st to 3rd PCs were approximately 100%, which meant shade colors were very similar to each other. Respective PCs showed specific figures, and values of shades showed sequences unique to each PC, which were independent of each other; values to the 1st PC showed gradual changes with change in shade numbering, values to the 2nd PC showed relatively high scores on opaque shades, values to the 4th PC showed lower scores on B and C group shades, and values to the 6th PC showed differences between manufacturers. icance PCA, we could find axes independent of the conventional 3-dimensional color spaces. These axes reflected certain changes which are not detected on conventional color spaces. Our methods are taking into account color matching under any illumination by focusing on the spectra themselves, and we can discuss about components of the teeth from spectra of resulting principal components. By applying our method to conventional systems, it would help diagnose color differences of dental materials.