Snapshot HoloSpec: dispersion-coded 4D feature learning for waterborne particle monitoring

Abstract We present a compact, cost-effective, and highly accurate dispersion-based four-dimensional feature learning (3D spatial and 1D spectral) method for real-time monitoring of water-suspended microparticles, requiring neither sample preparation nor explicit reconstruction. By jointly leveraging digital holography and spectral imaging, the system uses a single prism to compress holograms with transmission spectral information onto an imaging sensor. Coupling an optical model with deep learning, our system recovers the 3D holographic information and class identity for six waterborne particle types, exemplified by microplastics, from a single snapshot. This enables all-in-one reconstruction of both geometry and identity. Both supervised and unsupervised pipelines outperform hologram-only baselines, achieving a macro F 1 of 98.10%. The F 1 score combines precision and recall, offering a comprehensive evaluation of classification performance. With the unsupervised feature-extraction route, our method improves F 1 by 15%, greatly reducing reliance on labeled data. The approach enables long-duration, real-time monitoring in dynamic aquatic environments without sample pretreatment or labeling, and it extends readily from transmission spectroscopy to other spectral modalities.