Image Analysis for In-line Measurement of Multidimensional Size, Shape, and Polymorphic Transformation of L-Glutamic Acid Using Deep Learning-Based Image Segmentation and Classification

In situ tracking of the crystallization process through image segmentation has been developed and has encountered many challenges including improvement of in situ image quality, optimization of algorithms, and increased computation efficiency. In this study, a new method based on computer vision was proposed using the state-of-the-art deep learning technology to track crystal individuals. For the model compound L-glutamic acid, two polymorphic forms with different morphologies were segmented and classified during a seeded polymorphic transformation process. Information such as counts, size, surface area, crystal size distribution, and morphology of alpha- and beta-form crystals was extracted for the individual crystals during the process. A comparative analysis was conducted with traditional process analytical technologies such as in situ Raman and focus beam reflection measurement. Results show a high accuracy of segmentation and classification technique and a reliable tracking of crystals evolution. The image processing speed of up to 10 frames per second makes the proposed approach suitable for in situ tracking and control of crystallization and particulate processes. Our work in this study attempts to bridge the gap between the advanced imaging analysis technology that is available today and the specific needs of solution crystallization, to track, count, and measure the individual crystals.