Viscosity prediction of lipophilic semisolid emulsion systems by neural network modelling.
Previously published data (Gasperlin et al., 1998) on viscoelastic behaviour of lipophilic semisolid emulsion systems and the prediction of their physical stability by neural network modelling are analysed in further detail. Most attention has been paid to viscosity, which with storage (G') and loss modulus (G"), is one of the most important rheological parameters influenced by structure. Complex dynamic viscosity (eta*) was measured by oscillatory rheometry. The viscosity dependence of the lipophilic semisolid emulsions on the ratio of the particular components was defined by the neural network (error back-propagation algorithm), linear and incomplete polynomial models of higher orders. Polynomial models were used to complement the neural network model and to determine the relationship between variables. Since the viscosity was expressed in the whole measured frequency range, modelling was more complex and indirect modelling was introduced. The determined models were tested and the results confirm their usefulness for the explanation and prediction of the rheological characteristics of emulsion systems. The trained and tested neural network model proved to be a highly effective and applicable tool for predicting the viscosity of a lipophilic semisolid emulsion system of given composition.
Gasperlin
M
M
University of Ljubljana, Faculty of Pharmacy, 1000, Ljubljana, Slovenia.
Tusar
L
L
Tusar
M
M
Smid-Korbar
J
J
Zupan
J
J
Kristl
J
J
D004655:Emulsions; D008432:Mathematical Computing; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010577:Petrolatum / Q000737:chemistry*; D011237:Predictive Value of Tests; D012212:Rheology; D012828:Silicones / Q000737:chemistry*; D013501:Surface-Active Agents / Q000737:chemistry*; D014783:Viscosity; D014867:Water / Q000737:chemistry*
D004655:Emulsions; D012828:Silicones; D013501:Surface-Active Agents; D014867:Water; D010577:Petrolatum