In this study, two All-Ceramic (AC) materials—Empress 2 (EMP) (Ivoclar Vivadent AG, Schaan, Liechtenstein) and In-Ceram ALUMINA (ICA) (Vita Zahnfabrik, Bad Säckingen, Germany)—were analyzed, along with the effects of 3 luting agents—viz. Zinc Phosphate cement (ZNPO, PhospaCEM PL, Ivoclar Vivadent AG, Schaan, Liechtenstein), Glass Ionomer Cement (GIC, Ketac-Cem Radiopaque, ESPE Dental AG, Seefeld, Germany), and Compolute (COMP, ESPE Dental AG, Seefeld, Germany)—on the final color, using the CIELab system. Color differences (DeltaL, Deltaa, Deltab, and DeltaE) were calculated for samples with luting agents and for samples without luting agents with standard white and black backgrounds, with the use of a spectrophotometer, Luci 100 (Dr. Lange, Berlin, Germany). One-way ANOVA for DeltaL, Deltaa, Deltab, and DeltaE within both the AC systems, with and without luting agents, showed significant contributions of the background (p < 0.05). EMP was seen to be more translucent than ICA. Darker ceramics showed less color variation. Luting agents altered the final color of the restoration. ZNPO was least translucent, followed by GIC and COMP. Marginal increases in thicknesses of ICA samples (0.4 mm) do not show a statistically significant color difference. No method exists to predict the outcome of an AC restoration based on consideration of the luting agent and the background color.

We investigate methods for the recovery of reflectance spectra from the responses of trichromatic camera systems and the application of these methods to the problem of camera characterization. The recovery of reflectance from colorimetric data is an ill-posed problem, and a unique solution requires additional constraints. We introduce a novel method for reflectance recovery that finds the smoothest spectrum consistent with both the colorimetric data and a linear model of reflectance. Four multispectral methods were tested using data from a real trichromatic camera system. The new method gave the lowest maximum colorimetric error in terms of camera characterization with test data that were independent of the training data. However, the average colorimetric performances of the four multispectral methods were statistically indistinguishable from each other but were significantly worse than conventional methods for camera characterization such as polynomial transforms.