Shape‐Encoded Hydrogel Sensor Particles Enable Multiplex Odorant Detection Through Deep‐learning Classification

Abstract Simultaneous detection of multiple odorants is a major challenge in the development of portable, cell‐based biohybrid sensors, primarily due to the difficulty of distinguishing between different sensor cell types. Here, a strategy that encodes odorant sensor cell types using the shape of hydrogel particles, enabling shape‐based identification through deep learning is reported. Each particle shape corresponds to a unique sensor cell type expressing a distinct odorant receptor (OR). A convolutional neural network is trained to classify these shapes with high accuracy, and the resulting shape identification scheme is applied to time‐lapse fluorescence images of mixed particles exposed to single odorants. This enabled reliable assignment of particle identity and extraction of shape‐specific fluorescence signals. Distinct odorant‐dependent responses are observed, consistent with the known ligand specificities of the corresponding ORs. While this study focuses on individual odorants, the shape‐based approach provides a position‐independent, scalable method for multiplexed odorant detection. This framework supports the development of compact, high‐throughput biohybrid sensors for safety, environmental monitoring, and diagnostic applications.