ArticleSensors (Basel, Switzerland)2022
Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Lightweight Multi-Scale Convolutional Network with Gramian Angular Field Encoding for VOC Classification.Sensors (Basel, Switzerland) · 2026Article
- An Integrated IoT-Based Multi-Sensor Framework for Real-Time Indoor Environment and Safety Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Detection ofSensors (Basel, Switzerland) · 2025Article
- Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness.Sensors (Basel, Switzerland) · 2023Article
- Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader.Micromachines · 2023Article
- Classification of Three Volatiles Using a Single-Type eNose with Detailed Class-Map Visualization.Sensors (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
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
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