[Prediction of formability of flavonoid extract granules by dry granulation based on design space and RBFNN]

In this paper, a flavonoid extract powder properties-process parameters-granule forming rate prediction model was constructed based on design space and radial basis function neural network(RBFNN) to predict the formability of flavonoid extract gra-nules. Box-Behnken experimental design was employed to explore the mathematical relationships between critical process parameters and quality attributes. The design space of critical process parameters was developed, and the accuracy of the design space was verified by Monte Carlo method(MC). Design Expert 10 was used for Box-Behnken design and mixture design. Scutellariae Radix mixed powder was prepared and its powder properties were measured. The mixed powder was then subjected to dry granulation and the granule forming rate was determined. The correlations between powder properties were analyzed by variance influence factor(VIF), and principal component analysis(PCA) was performed for the factors with strong collinearity. In this way, a prediction model of powder properties-process parameters-granule forming rate was established based on RBFNN, the accuracy of which was evaluated with examples. The results showed that the model had a good predictive effect on the granule forming rate, with the average relative error of 1.04%. The predicted value and the measured value had a high degree of fitting, which indicated that model presented a good prediction ability. The prediction model established in this study can provide reference for the establishment of quality control methods for Chinese medicinal preparations with similar physical properties.