Analysis of color differences in stained contemporary esthetic dental materials

KOÇAK E. F., EKREN O., Johnston W. M., UÇAR Y.

JOURNAL OF PROSTHETIC DENTISTRY, vol.126, no.3, pp.438-445, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 126 Issue: 3
  • Publication Date: 2021
  • Doi Number: 10.1016/j.prosdent.2020.08.006
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
  • Page Numbers: pp.438-445
  • Dokuz Eylül University Affiliated: No


Statement of problem. Although contemporary analytical methods are available for application to data which exhibit a lack of equality of variances or a lack of normality in the error distribution, little guidance is provided for selecting the methods of data handling and analysis which best fit color difference data for stained esthetic materials. Purpose. The purposes of this in vitro study were to apply information criteria of analysis of variance (ANOVA) methods of differing error distributions and covariance structures when analyzing color differences to determine the degree of alienation among 3 Commission Internationale de l'Eclairage (CIE) color difference formulae to assess the linearity of relationships among these formulae and to independently assess differences among various computer-aided design and computer-aided manufacture (CAD-CAM) materials in any color change after common forms of staining over time. Material and methods. Hybrid ceramic, resin nanoceramic, feldspathic-ceramic, and lithium-disilicate ceramic specimens (N=128) were subjected to staining from water, tea, coffee, and red wine over 1, 7, and 30 days, with color differences calculated from baseline. Akaike information criteria (AIC) and Bayesian information criteria (BIC) values were determined for Gaussian and lognormal error distributions at covariance structures of standard variance components and compound-symmetry. The analysis of variance used to analyze any significant effects on these color differences was the one with the lowest AIC and BIC values. Then, for each solution, day, and CIE color difference formula, any significant difference in the color differences between all pairs of materials was found by Bonferroni-corrected Student t tests. Those statistically significant pairwise comparisons where the larger of the color differences met or exceeded the acceptability threshold were labeled as statistically and visually noteworthy. Results. For this color difference data set, the lognormal error distribution and the covariance structure of compound symmetry provided the best AIC and BIC. Because the interaction between material, solution, and day was statistically significant (P<.001), pairwise comparisons were made between all pairs of materials for each level of solution and day studied. Noteworthy differences were identified, where hybrid ceramic and resin nanoceramic each had color changes after staining in coffee and red wine that were greater than each of feldspathic-ceramic and lithium-disilicate ceramic. Conclusions. AIC and BIC values evaluate distinctively the Gaussian and lognormal error distributions when analyzing highly varying color differences. Although there is a high linear correlation between the 3 color difference formulae studied, each formula is unique, and each represents a different assessment of the perceived color difference. CAD-CAM materials, staining liquids, and time points affected the notable color changes.