Predicting textural attributes and frictional characteristics of topical formulations based on non-linear rheology

Abstract

A number of analytical procedures (Spectrum Descriptive Analysis (SDA), Quantitative Descriptive Analysis (QDA), and Check All That Apply (CATA)) are used for characterizing sensorial attributes of topical formulations. However, these techniques are evaluated by expert panels/consumers, which are subjective, expensive and time consuming. Despite widespread use of these methods, the techniques do not necessarily aid in the development of innovative formulations and understanding of consumer liking. In addition, the hedonic attributes of a product can significantly dominate the sensation. Therefore, a more rapid, quantitative, and objective approach is required for understanding the formulation factors that governs sensory perception at different points of consumer application. Rheological evaluations of topical formulations were carried out under steady and oscillatory (SAOS and LAOS) rheology using a commercial rheometer. Friction measurements were performed using an in-house built tribometer on non-biological skin model to investigate how surface properties are influenced by application of different topical formulations. Further, a broad range of instrumental texture measurements was performed to characterize the formulations. Principal component analysis was used for dimensionality reduction of the instrumental data. Supervised machine learning was performed on the closely related PCA parameters to develop predictive models. We identified physical parameters relevant to different perceptual attributes by comparing a range of commercial topical formulations with various compositions using rheological and tribological methods. Multi-variate machine learning models were developed where several key instrumental textural attributes and skin feel could be predicted from data obtained from linear and non-linear rheological measurements. Our study shows rheological analysis based on a few material parameters can be effectively used for predicting instrumental sensory attributes that is of relevance to consumer care industry. The machine learning models may benefit development of innovative formulations, valorisation of new materials and serve as a platform for mapping commercial formulations for product optimization. The models can likely shorten design cycle time by screening through large volume of formulations and short list only those with highest potential to be evaluated by a very specialized and expensive human test panel.