Real-time component-based particle size measurement and dissolution prediction during continuous powder feeding using machine vision and artificial intelligence-based object detection

This work presents a system, in which machine vision combined with artificial intelligence-based image analysis was used to determine the component-based particle size distribution of pharmaceutical powder blends. The blends consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP). Images of powders were recorded with a digital camera in-line during feeding from a continuous feeder. The component-based particle size distributions determined with the system correlated well with those measured using a microscope as a reference method. This novel method proved to be effective in the real-time determination of particle size distribution of different components in the same blend. It was also possible to predict the in vitro dissolution profile of capsules filled with this blend by using the measured particle size distribution of ASA as input in a population balance model. The method could provide valuable information on the blends used in the pharmaceutical industry and could play a key role in the development of pharmaceutical quality control.