A Concise Review on Color Match Prediction Models

In order for a color matching model to be able to predict the correct and precise amounts of colorants to achieve a certain color, numerous sets of information regarding the effective parameters of the colored system is required. These parameters include the absorbance and scattering coefficients of colorants, relations between total and internal reflection of a colored layer, basic assumptions relating to incident light interactions with and without the colored layer, geometry and size of the scattering particles, etc. Additionally a colored layer could be transparent, translucent or opaque. Various models such as physical, numerical, intelligent and hybrid models attempt to present a correct prediction of the amounts of components to give the precise color through determining the contribution of each effective parameter which may lead to new formulae. In the present study, a variety of such models and their various applications are reviewed. Most of the well-known physical models such as Kubelka-Munk, Four- Flux, Many-Flux, Chandrasekhar, inverse adding doubling, Monte Carlo, representative layer theory and even intelligent models such as artificial neural network models for optimal usage are herewith described.