Advancing Electronic Noses With Transformers: Real‐Time Classification of Hazardous Odors and Food Freshness

Real‐time detection of gas leaks and food spoilage poses significant challenges, particularly in settings where human olfactory perception is unreliable. This paper introduces Artificial Sensing, a novel AI‐powered electronic nose (E‐nose) system developed to accurately classify odors and evaluate food freshness in indoor environments. The system combines MQ‐series sensors and the Grove Multichannel Gas Sensor v2, integrated with a Seeed Studio Wio Terminal for edge‐level processing. To effectively handle the variability and complexity of time‐series sensor data, we propose a Time‐Series Transformer model enhanced with Multi‐Scale Adaptive Convolution (MAC) and multi‐head self‐attention, enabling robust feature extraction across temporal scales. Additionally, a multimodal fusion strategy incorporating ambient temperature and humidity improves model resilience. A comprehensive dataset comprising 4040 gas leak samples and 4080 food spoilage samples (milk and chicken) was collected under varied conditions. The proposed model achieved F1‐scores of 99.3% for gas leak detection and 94.8% for spoilage classification, surpassing traditional classifiers (SVM, Random Forest, ANN) and baseline ViT models by 7%–12%. Deployed on the Edge Impulse platform, the system demonstrated real‐time responsiveness (120 ms latency) and low power consumption. These findings highlight the promise of Transformer‐based architectures for edge‐level environmental monitoring and food safety applications.