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Data-centric artificial olfactory system based on the eigengraph

Author

Listed:
  • Seung-Hyun Sung

    (Yonsei University
    Daejeon Metropolitan Office of Education)

  • Jun Min Suh

    (Research Institute of Advanced Materials, Seoul National University
    Massachusetts Institute of Technology)

  • Yun Ji Hwang

    (Yonsei University)

  • Ho Won Jang

    (Research Institute of Advanced Materials, Seoul National University
    Seoul National University)

  • Jeon Gue Park

    (Tutorus Labs Inc.
    College of Education, Seoul National University)

  • Seong Chan Jun

    (Yonsei University)

Abstract

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

Suggested Citation

  • Seung-Hyun Sung & Jun Min Suh & Yun Ji Hwang & Ho Won Jang & Jeon Gue Park & Seong Chan Jun, 2024. "Data-centric artificial olfactory system based on the eigengraph," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45430-9
    DOI: 10.1038/s41467-024-45430-9
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    References listed on IDEAS

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    1. Shumao Cui & Haihui Pu & Spencer A. Wells & Zhenhai Wen & Shun Mao & Jingbo Chang & Mark C. Hersam & Junhong Chen, 2015. "Ultrahigh sensitivity and layer-dependent sensing performance of phosphorene-based gas sensors," Nature Communications, Nature, vol. 6(1), pages 1-9, December.
    2. Anees Abrol & Zening Fu & Mustafa Salman & Rogers Silva & Yuhui Du & Sergey Plis & Vince Calhoun, 2021. "Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    3. Mohammad Mehdi Pour & Andrey Lashkov & Adrian Radocea & Ximeng Liu & Tao Sun & Alexey Lipatov & Rafal A. Korlacki & Mikhail Shekhirev & Narayana R. Aluru & Joseph W. Lyding & Victor Sysoev & Alexander, 2017. "Laterally extended atomically precise graphene nanoribbons with improved electrical conductivity for efficient gas sensing," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
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