Artificial Sensing: AI-Driven Electronic Nose for Real-Time Gas Leak Detection and Food Spoilage Monitoring

Olfactory dysfunction poses a significant threat to health and safety by hindering the detection of hazards like gas leaks and spoiled food, highlighting the limitations of subjective and non-real-time traditional methods. This study presents Scent Sense, an AI-driven Electronic nose (E-nose) system designed for real-time odor classification and food quality assessment. The system integrates multi-channel MQ-series gas sensors with a Seeed Studio Wio Terminal, leveraging near-sensor computing and machine learning for accurate detection. The development followed a structured, multi-phase approach: (1) gathering requirements from industry experts and individuals with olfactory impairments; (2) designing and integrating hardware and software components; (3) implementing machine learning models (Support Vector Machine, Random Forest, Artificial Neural Network) in Python and hardware interfacing in C++; (4) conducting rigorous testing across unit, integration, and real-world scenarios; and (5) deploying trained neural networks on the Edge Impulse platform for real-time inference. Sensor data underwent preprocessing, feature extraction, and exploratory data analysis before training and evaluation. Experimental results demonstrate 100% accuracy in gas leak detection and 99% accuracy in food spoilage classification, with sensitivity levels reaching 0.01 ppm for H₂S and NH₃ detection. Additionally, the system provides continuous real-time freshness tracking, ensuring reliable food monitoring. Future work will focus on enhancing sensor diversity, improving model generalization, and extending applications to industrial safety, environmental monitoring, and smart home automation.