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Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

Author

Listed:
  • Farel Ahadyatulakbar Aditama

    (Department of Physics, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram, Indonesia)

  • Lalu Zulfikri

    (Department of Physics, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram, Indonesia)

  • Laili Mardiana

    (Department of Physics, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram, Indonesia)

  • Tri Mulyaningsih

    (Department of Biology, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram, Indonesia)

  • Nurul Qomariyah

    (Department of Physics, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram, Indonesia)

  • Rahadi Wirawan

    (Department of Physics, Faculty of Mathematics and Natural Sciences, University of Mataram, Mataram, Indonesia)

Abstract

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 -1], while the poor-quality agarwood has an output of [-1 1].

Suggested Citation

  • Farel Ahadyatulakbar Aditama & Lalu Zulfikri & Laili Mardiana & Tri Mulyaningsih & Nurul Qomariyah & Rahadi Wirawan, 2020. "Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 66(3), pages 97-103.
    • Handle: RePEc:caa:jnlrae:v:66:y:2020:i:3:id:26-2020-rae
    DOI: 10.17221/26/2020-RAE
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