Paradigm Migration and Interdisciplinary Convergence Study of Electronic Nose Odor Recognition Algorithms

DOI

10.2991/978-94-6463-823-3_9How to use a DOI?

Keywords

Electronic Noses; Bio-inspired Adaptive Systems; Paradigm Shift; Interdisciplinary Fusion; Neuromorphic Hardware

Abstract

Electronic nose technology has shown great potential in medical diagnosis and industrial monitoring by simulating the biological olfactory mechanism, but its industrialization has been constrained by the fragmentation of algorithms and mechanisms for a long time. This paper systematically analyzes the evolution path of e-nose algorithms over the past 40 years, specifically analyze the technology paradigm migration laws of feature engineering, end-to-end learning and adaptive learning, and compare the differences between different paradigms in terms of key indexes such as gas mixture recognition rate, temperature drift error, and energy consumption efficiency. It is found that: feature engineering achieves > 30% cross-scene error due to dimensional catastrophe and lack of dynamic response; end-to-end learning realizes 89.7% mixed-gas decoupling rate through CNN-Transformer architecture, but neglects Langmuir adsorption equation to cause industrial temperature drift; adaptive learning breaks through the bottleneck of environmental robustness with the help of federated synergy mechanism, but it is constrained by the efficiency of pulse coding. The study reveals that algorithmic evolution is essentially a gradual approximation of the cross-scale dynamics of biological olfaction, while interdisciplinary fragmentation and engineering simplification bias are the core obstacles to industrialization. The article concludes with a summary of the whole paper, and looks forward to future breakthroughs in chemical semantic understanding and metabolism-perception co-evolution mechanisms.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ciermuzhen Aoma
PY  - 2025
DA  - 2025/08/31
TI  - Paradigm Migration and Interdisciplinary Convergence Study of Electronic Nose Odor Recognition Algorithms
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 94
EP  - 103
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_9
DO  - 10.2991/978-94-6463-823-3_9
ID  - Aoma2025
ER  -