An Investigation Into the Effect of Process Parameters of Sedi Herba<\i> Extract Granulation in Fluid Bed on Granule Size Distribution by Response Surface Model and Partial Least Squares Model

OBJECTIVE To explore the particle size distribution of granules prepared by fluid bed granulation via response surface regression model (RSM) and partial least square regression model (PLS). METHODS Sedi Herba extract was granulated by fluid bed granulation. Box-Behnken design in RSM was utilized to study the effects of binder addition rate (X₁)， the liquid to solid ratio (X₂) and air temperature (X₃) on particle size distribution. Moreover， RSM and PLS were employed to explore the influence of process parameters on particle size distribution. RESULTS The results demonstrated that both of RSM and PLS could fit the fluid bed granulation well. Furthermore， RSM model exhibited better model fitting precision and prediction ability. CONCLUSION We could understand the process of fluid bed granulation profoundly based on experimental design and the different statistical models. A robust and high prediction model could be achieved， which could provide reliable basis and technical support for further production.