A deep learning approach to perform defect classification of freeze-dried product

Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visual inspection, this method poses typical challenges and shortcomings that can be addressed with innovative techniques. While many cosmetic defects can occur, some are considered more critical than others as they can be harmful to the patient or affect the drug's efficacy. With the rise of artificial intelligence and computer vision technology, faster and more reproducible quality control is possible, allowing real-time monitoring on a continuous manufacturing line. In this study, several continuously freeze-dried samples were prepared using formulations and process settings that lead deliberately to specific defects faced in freeze-drying as well as defect-free samples. Two approaches (i.e. patch-based approach and multi-label classification) capable of handling high-resolution images based on Convolutional Neural Networks were developed and compared to select the optimal one. Additional visualization techniques were used to enhance model understanding further. The best approach achieved perfect precision and recall on critical defects, with a prediction time of less than 50 ms to make a decision on the acceptance or rejection of vials generated.