| Institution: | 1. Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, Minnesota, USA;2. Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, Minnesota, USA;3. Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota, USA |
| Abstract: | Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real-time, in situ analysis using machine learning (ML)-assisted digital inline holography (DIH). Our ML model uses a customized YOLOv5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of ML-driven DIH approach as a sensitive and versatile diagnostic tool for real-time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale. |
