A multivariate statistical approach to analyze the impact of material attributes and process parameters on the quality performance of an auger dosing process

The purpose of this work is to develop a statistical methodology that identifies the correlations among critical material attributes, critical process parameters, and critical quality attributes of a volumetric vertical micro-auger dosing process. This contributes to improving the accuracy and consistency in delivering of powders by using a well-designed dosing system. In this, shear cell analysis and various empirical tests are used to characterize 30 material attributes of ten different powders ranging between free-flowing and cohesive ones and widely used in the drug industry. Dosing experiments using three different augers and three rotational speeds are performed using Design of Experiments to study the impact of all the attributes on four quality-related attributes, such as the feeding accuracy. The analysis is carried out using principal component analysis, which reveals the (dis)similarities between the powders and identifies overarching attributes. Thereafter, regression analysis is applied to find possible correlations among the studied factors. As a result, the 30 material attributes and four process parameters could be reduced to six independent principal components that described 85.8% of the overall explained variance in the data set. Thus, the demonstrated methodology allows to optimize the auger feeding process and can be used within a Quality by Design dosing approach.