Predictive algorithms for determination of reflectance data from quantity of pigments within experimental dental resin composites. Issue 2 (December 2015)

• Record Type: Journal Article
• Authors: Ghinea, Razvan; Pecho, Oscar; Herrera, Luis; Ionescu, Ana; Cardona, Juan; Sanchez, María; Paravina, Rade; Perez, María
• Is Part Of: Biomedical engineering online. Volume 14:Issue 2(2015)
• Page Start: 1
• Page End: 14
• Publication Date: 2015-12
• Subjects: Experimental dental composites -- predictive algorithms -- reflectance -- color coordinates; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/1475-925X-14-S2-S4
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background Being able to estimate (predict) the final spectrum of reflectance of a biomaterial, especially when the final color and appearance are fundamental for their clinical success (as is the case of dental resin composites), could be a very useful tool for the industrial development of these type of materials. The main objective of this study was the development of predictive models which enable the determination of the reflectance spectrum of experimental dental resin composites based on type and quantity of pigments used in their chemical formulation. Methods 49 types of experimental dental resin composites were formulated as a mixture of organic matrix, inorganic filler, photo activator and other components in minor quantities (accelerator, inhibitor, fluorescent agent and 4 types of pigments). Spectral reflectance of all samples were measured, before and after artificial chromatic aging, using a spectroradiometer. A Multiple Nonlinear Regression Model (MNLR) was used to predict the values of the Reflectance Factors values in the visible range (380 nm-780 nm), before and after aging, from % Pigment (%P1, %P2, %P3 and %P4) within the formulation. Results The average value of the prediction error of the model was 3.46% (SD: 1.82) across all wavelengths for samples before aging and 3.54% (SD: 1.17) for samples after aging. The differences found between the predicted and measured values of the chromatic coordinates are smaller than the acceptability threshold and, in some cases, are even below the perceptibility threshold. Conclusions Within the framework of this pilot study, the nonlinear predictive models developed allow the prediction, with a high degree of accuracy, of the reflectance spectrum of the experimental dental resin composites.