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Article

Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors

Robotics Laboratory, Universitat de Lleida, Jaume II, 69, 25001 Lleida, Spain
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Academic Editor: Gagaoudakis Emmanouil
Sensors 2022, 22(3), 1120; https://doi.org/10.3390/s22031120
Received: 23 December 2021 / Revised: 19 January 2022 / Accepted: 27 January 2022 / Published: 1 February 2022
(This article belongs to the Special Issue Thin Film Gas Sensors)
The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications. View Full-Text
Keywords: electronic nose; eNose; MOX; principal component analysis; k-nearest neighbor electronic nose; eNose; MOX; principal component analysis; k-nearest neighbor
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MDPI and ACS Style

Palacín, J.; Rubies, E.; Clotet, E.; Martínez, D. Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors. Sensors 2022, 22, 1120. https://doi.org/10.3390/s22031120

AMA Style

Palacín J, Rubies E, Clotet E, Martínez D. Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors. Sensors. 2022; 22(3):1120. https://doi.org/10.3390/s22031120

Chicago/Turabian Style

Palacín, Jordi, Elena Rubies, Eduard Clotet, and David Martínez. 2022. "Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors" Sensors 22, no. 3: 1120. https://doi.org/10.3390/s22031120

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