Open AccessSmart Electronic Nose Enabled by an All‐Feature Olfactory Algorithm
Author(s) -
Fang Cong,
Li Hua-Yao,
Li Long,
Su Hu-Yin,
Tang Jiang,
Bai Xiang,
Liu Huan
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200074
Subject(s) - electronic nose , computer science , artificial intelligence , odor , feature (linguistics) , feature extraction , convolutional neural network , olfactory system , pattern recognition (psychology) , process (computing) , olfaction , computer vision , psychology , linguistics , philosophy , neuroscience , psychiatry , biology , operating system
An electronic nose (e‐nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature‐based odor recognition algorithms rely on domain‐specific expertise, which may limit the performance due to information loss during the feature extraction process. Inspired by human olfaction, a smart electronic nose enabled by an all‐feature olfactory algorithm (AFOA) is proposed, whereby all features in a gas sensing cycle of semiconductor gas sensors, including the response, equilibrium, and recovery processes are utilized. Specifically, our method combines 1D convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information. It is further demonstrated that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. Results show that the e‐nose simply comprising of six semiconductor gas sensors achieves superior performances to state‐of‐the‐art methods on the Chinese liquor data. Ablation studies reveal the contribution of each sensor in odor recognition. Therefore, a deep‐learning‐enabled codesign of sensor arrays and recognition algorithms can reduce the heavy demand for a huge amount of highly specialized gas sensors and provide interpretable insights into odor recognition dynamics in an iterative way.