Rheological analyses and artificial neural network as optimization tools to predict the sensory perception of cosmetic emulsions

Abstract

Pharmaceutical, cosmetic and personal care products are mainly based on emulsions and their rheological behavior can be a critical factor for successful use. Thus, rheological analysis is a promising tool, since the stability, sensory aspects and processing parameters can be assessed. This work presents the rheological analyses of 39 samples of emulsions and the use of data obtained in a tool based on artificial neural networks (ANN), in order to predict the sensory performance of cosmetic emulsions. The storage (G') and loss (G") moduli, yield stress and thixotropy were measured experimentally and used in the ANN model. The correlation of the results obtained in the simulations with sensory tests performed with consumers showed accuracy of 60-84%. The reported results demonstrate that the prediction of sensory perception based on rheological analyses offers a very useful strategy for further studies and can support the development of new products in less time.