A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity.

The successful implementation of near-infrared spectroscopy (NIRS) in process control of powder blending requires constructing an inclusive spectral database that reflects the anticipated voluntary or involuntary changes in processing conditions, thereby minimizing bias in prediction of blending behavior. In this study, experimental design was utilized as an efficient way of generating blend experiments conducted under varying processing conditions such as humidity, blender speed and component concentration. NIR spectral data, collected from different blending experiments, was used to build qualitative models for prediction of blend homogeneity. Two pattern recognition algorithms: Soft Independent Modeling of Class Analogies (SIMCA) and Principal Component Modified Bootstrap Error-adjusted Single-sample Technique (PC-MBEST) were evaluated for qualitative analysis of NIR blending data. Optimization of NIR models, for the two algorithms, was achieved by proper selection of spectral processing, and training set samples. The models developed were successful in predicting blend homogeneity of independent blend samples under different processing conditions.