Odors Detection and Recognition Based on Intelligent E-Nose

Abstract

Electronic noses have become more common as a result of developments in sensor technology, machine learning (ML), and Artificial Intelligence (AI). At the moment, the majority of e-nose research is conducted in laboratories; access from other locations is not possible with e-nose. Very few real-time smell detection applications, including tiny drones equipped with commercial gas sensors or biosensors made of insect antennas, have been created. The scope of this work is to design an intelligent E-Nose for odors detection. Smell Inspector developer kit has been used to get measurements. The Smell Inspector consists of sensors for temperature and humidity in addition to four smell iX16 chips. The Smell Inspector creates digital fragrance fingerprints using a variety of separate gas detectors. The approach for object recognition and classification using artificial intelligence techniques is presented in this paper. These techniques include Machine Learning (ML), clustering, and regression algorithms. Using the K-Nearest Neighbor (KNN) algorithm, the experimental results' array sensors were able to recognize; clean air, onion, garlic, coffee, spices, lemon, vinegar, gasoline, petrol, diesel, and perfumes with 78% accuracy rate.