Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement
Practical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-han...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/21/6190 |
| _version_ | 1797549249465942016 |
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| author | Gaku Imamura Genki Yoshikawa |
| author_facet | Gaku Imamura Genki Yoshikawa |
| author_sort | Gaku Imamura |
| collection | DOAJ |
| description | Practical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-hand measurement, the comprehensive development of a measurement system including hardware, measurement protocols, and data analysis is necessary. In this study, we developed palm-size wireless odor measurement devices equipped with Membrane-type Surface stress Sensors (MSS) and investigated the effect of measurement protocols and feature selection on odor identification. By using the device, we measured vapors of liquids as odor samples through the free-hand measurement in different protocols. From the measurement data obtained with these protocols, datasets of transfer function ratios (TFRs) were created and analyzed by clustering and machine learning classification. It has been revealed that TFRs in the low-frequency range below 1 Hz notably contributed to vapor identification because the frequency components in that range reflect the dynamics of the detection mechanism of MSS. We also showed the optimal measurement protocol for accurate classification. This study has shown a guideline of the free-hand measurement and will contribute to the practical implementation of machine olfaction in society. |
| first_indexed | 2024-03-10T15:11:57Z |
| format | Article |
| id | doaj.art-61e0604ce7f849aeacdf9c617eb43c1b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:11:57Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-61e0604ce7f849aeacdf9c617eb43c1b2023-11-20T19:15:09ZengMDPI AGSensors1424-82202020-10-012021619010.3390/s20216190Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand MeasurementGaku Imamura0Genki Yoshikawa1International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), World Premier International Research Center Initiative (WPI), 1-1 Namiki, Tsukuba 305-0044, JapanCenter for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, JapanPractical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-hand measurement, the comprehensive development of a measurement system including hardware, measurement protocols, and data analysis is necessary. In this study, we developed palm-size wireless odor measurement devices equipped with Membrane-type Surface stress Sensors (MSS) and investigated the effect of measurement protocols and feature selection on odor identification. By using the device, we measured vapors of liquids as odor samples through the free-hand measurement in different protocols. From the measurement data obtained with these protocols, datasets of transfer function ratios (TFRs) were created and analyzed by clustering and machine learning classification. It has been revealed that TFRs in the low-frequency range below 1 Hz notably contributed to vapor identification because the frequency components in that range reflect the dynamics of the detection mechanism of MSS. We also showed the optimal measurement protocol for accurate classification. This study has shown a guideline of the free-hand measurement and will contribute to the practical implementation of machine olfaction in society.https://www.mdpi.com/1424-8220/20/21/6190machine olfactionMembrane-type Surface stress Sensor (MSS)Transfer Function Ratio (TFR)free-hand measurement |
| spellingShingle | Gaku Imamura Genki Yoshikawa Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement Sensors machine olfaction Membrane-type Surface stress Sensor (MSS) Transfer Function Ratio (TFR) free-hand measurement |
| title | Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement |
| title_full | Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement |
| title_fullStr | Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement |
| title_full_unstemmed | Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement |
| title_short | Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement |
| title_sort | development of a mobile device for odor identification and optimization of its measurement protocol based on the free hand measurement |
| topic | machine olfaction Membrane-type Surface stress Sensor (MSS) Transfer Function Ratio (TFR) free-hand measurement |
| url | https://www.mdpi.com/1424-8220/20/21/6190 |
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