Explainable deep-learning detection of microplastic fibers via polarization-resolved holographic microscopy

Reliable identification of microplastic fibers is crucial for environmental monitoring but remains analytically challenging. We report the first explainable deep-learning framework for classifying microplastic and natural microfibers using exclusively polarization-based features obtained from polarization-resolved digital holographic microscopy. From multiplexed holograms, the complex Jones matrix of each fiber was reconstructed to extract polarization eigen-parameters describing optical anisotropy. Statistical descriptors of nine polarization characteristics formed a 72-dimensional feature vector for a total of 296 fibers spanning six material classes, including polyamide 6, polyethylene terephthalate, polyamide 6.6, polypropylene, cotton, and wool. The designed fully-connected deep neural network achieved an accuracy of 96.7 % on the validation data, surpassing that of common machine-learning classifiers. Explainable artificial intelligence analysis with Shapley additive explanations identified eigenvalue-ratio quantities as dominant predictors, revealing the physical basis for classification. An additional reduced-feature model with the preserved architecture, exploiting only these most significant eigenvalue-based characteristics, retained high accuracy (93.3 %), thereby confirming their dominant role while still outperforming common machine-learning classifiers. These results establish polarization-based features as distinctive optical fingerprints and demonstrate an explainable deep-learning approach for automated microplastic fiber identification.