AI‑driven photonic noses: from conventional sensors to cloud‑to-edge intelligent microsystems
Hong Zhou, Hemin Zhang, Ruirong Zhang, Xichen Yuan, Honglong Chang
Abstract
The photonic nose is an emerging class of optical sensing systems designed to mimic the olfactory capabilities of a human nose. Evolving from conventional chemical and gas sensors, photonic noses leverage optical phenomena to achieve high sensitivity and fast, label-free analysis of chemical volatiles. This review provides an in-depth analysis of the evolution and current state of photonic nose technologies, particularly focusing on their integration with artificial intelligence (AI) and machine learning (ML). We first discuss key optical sensing and fabrication methods, including colorimetry, refractive index sensing, spectroscopy, and integrated photonic devices. Then, the role of ML algorithms in photonic noses is highlighted, and the integration of photonic noses into cloud-to-edge computing systems is also explored, demonstrating intelligent microsystem designs capable of on-chip real-time analytics and distributed data processing. Additionally, we highlight representative application scenarios where AI-driven photonic noses show significant advantages, including environmental monitoring, early-stage medical diagnostics, and ensuring food quality and safety. A concise comparative analysis between photonic noses, electronic noses, and analytical instruments is provided. Finally, this review identifies the remaining challenges in AI-driven photonic noses and offers insights into future development pathways toward smarter, miniaturized, and more robust photonic sensing systems.