Reflectance and color prediction of dental material monolithic samples with varying thickness

Objective: To assess accuracy of reflectance reconstruction and color estimation of different dental materials with varying thicknesses using Principal Component Analysis (PCA).Method: A1, A2, A3, A3.5, B2, C2 and D2 shades and 5 thicknesses (within 0.5-2.5 mm range) of Vita Suprinity (VS-PC) and Vitapan Dentine (VD), were used. Reflectance measurements were performed over black background using a non-contact spectroradiometer with CIE 45 circle/0 circle geometry. A PCA based algorithm was proposed to reconstruct spectral data and color of samples, using both extrapolation and interpolation approaches. Root Mean Square Error (RMSE), Goodness of Fit (GFC), correlation coefficient (R2) as well as Delta E00 with corresponding 50:50% acceptability and perceptibly thresholds (AT and PT) were used as performance assessment.Results: The interpolation approach provided an average RMSE = 0.01 and GFC > 0.999 when comparing predicted and measured spectral reflectances for both materials, while for the extrapolation approach RMSE = 0.02 and GFC > 0.999. Interpolation approach also resulted in lower overall mean color difference Delta E00 = 0.8 (Delta E00 = 0.9 for VS-PC and Delta E00 = 0.7 for VD), while using extrapolation approach resulted in higher overall mean color difference Delta E00 = 1.6, although below the AT (Delta E00 = 1.8 for VS-PC and Delta E00 = 1.5 for VD). Correlation values between predicted and measured spectral reflectances of R2 = 0.987 andSignificance: The proposed PCA-based algorithm is able to efficiently predict reflectance spectrum and color of monolithic samples of different dental materials with varying thickness. It can be used to optimize dental materials manufacturing processes and to improve chromatic accuracy of clinical dental restorations. (c) 2021 The Academy of Dental Materials. Published by Elsevier Inc. All rights reserved.

Automated summary

By utilizing Principal Component Analysis (PCA), the accuracy of reflectance reconstruction and color estimation for different dental materials with varying thicknesses can be effectively assessed. The proposed PCA-based algorithm demonstrates its ability to predict the reflectance spectrum and color of monolithic dental samples, offering potential benefits for optimizing dental material manufacturing processes and enhancing chromatic accuracy in clinical dental restorations.