biostudies-literatureUnknown10(1)Yue XNational Science and Technology Major Project from the Minister of Science and Technology of ChinaThe olfactory sensory system of Drosophila has several advantages, including low power consumption, high rapidity and high accuracy. Here, we present a biomimetic intelligent olfactory sensing system based on the integration of an 18-channel microelectromechanical system (MEMS) sensor array (16 gas sensors, 1 humidity sensor and 1 temperature sensor), a complementary metal‒oxide‒semiconductor (CMOS) circuit and an olfactory lightweight machine-learning algorithm inspired by Drosophila. This system is an artificial version of the biological olfactory perception system with the capabilities of environmental sensing, multi-signal processing, and odor recognition. The olfactory data are processed and reconstructed by the combination of a shallow neural network and a residual neural network, wiMicrosystems & nanoengineering153https://www.ebi.ac.uk/biostudies/studies/S-EPMC11520895biostudies-literatureA Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments.PMC11520895Yang HQin NTao THLi ZJiang HZhao FChen HYue XWang JLiu WZhang PfalseA Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments.The olfactory sensory system of Drosophila has several advantages, including low power consumption, high rapidity and high accuracy. Here, we present a biomimetic intelligent olfactory sensing system based on the integration of an 18-channel microelectromechanical system (MEMS) sensor array (16 gas sensors, 1 humidity sensor and 1 temperature sensor), a complementary metal‒oxide‒semiconductor (CMOS) circuit and an olfactory lightweight machine-learning algorithm inspired by Drosophila. This system is an artificial version of the biological olfactory perception system with the capabilities of environmental sensing, multi-signal processing, and odor recognition. The olfactory data are processed and reconstructed by the combination of a shallow neural network and a residual neural network, wi2024-01-01T00:00:00Z2024 Oct2026-07-16T00:25:11.291Z2025-04-06T15:21:57.513ZS-EPMC115208953946800510.1038/s41378-024-00752-y