Odor Sensor System Using Chemosensitive Resistor Array and Machine Learning
| Autor: | Akio Oki, Masaya Nakatani, Rui Yatabe, Yosuke Hanai, Atsushi Shunori, Bartosz Wyszynski, Hiroaki Oka, Kiyoshi Toko, Atsuo Nakao, Takashi Washio |
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| Rok vydání: | 2021 |
| Předmět: |
Sensor system
Materials science business.industry 010401 analytical chemistry Channel data Chip Machine learning computer.software_genre 01 natural sciences Computer Science::Other 0104 chemical sciences law.invention Software Odor Electrical resistance and conductance law Artificial intelligence Electrical and Electronic Engineering Resistor business Instrumentation computer |
| Zdroj: | IEEE Sensors Journal. 21:2077-2083 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2020.3016678 |
| Popis: | In this study, we developed an odor sensor system using chemosensitive resistors, which outputted multichannel data. Mixtures of gas chromatography stationary materials (GC materials) and carbon black were used as the chemosensitive resistors. The interaction between the chemosensitive resistors and gas species shifted the electrical resistance of the resistors. Sixteen different chemosensitive resistors were fabricated on an odor sensor chip. In addition, a compact measurement instrument was fabricated. Sixteen channel data were obtained from the measurements of gas species using the instrument. The data were analyzed using machine learning algorithms available on Weka software. As a result, the sensor system successfully identified alcoholic beverages. Finally, we demonstrated the classification of restroom odor in a field test. The classification was successful with an accuracy of 97.9%. |
| Databáze: | OpenAIRE |
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