A rapid discreteness correction scheme for reproducibility enhancement among a batch of MOS gas sensors

Zhang, Lei; Tian, Fengchun; Peng, Xiongwei; Yin, Xin

doi:10.1016/j.sna.2013.11.015

Cited by 40 publications

(15 citation statements)

References 23 publications

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“…There are some limitations associated with the developed unit. A drawback of the MOX-based gas sensor unit is that there is an inherent variability between sensors, as a result of manufacturing processes [51]. To minimise these effects, it may be necessary to screen a large number of the same sensors to choose those with similar characteristics (e.g., sensor resistance at room temperature).…”

Section: Discussionmentioning

confidence: 99%

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

et al. 2020

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In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is achieved using a heated sample tube, capable of monitoring sampling-related parameters, such as carbon dioxide (CO2), humidity, and temperature. A simple mobile app was developed to receive real-time data from the device, using Wi-Fi communication. The system has been tested using chemical standards and exhaled breath samples from healthy volunteers, before and after taking a peppermint capsule. Results from chemical testing indicate that we can separate chemical standards (acetone, isopropanol and 1-propanol) and different concentrations of isobutylene. The analysis of exhaled breath samples demonstrate that we can distinguish between pre- and post-consumption of peppermint capsules; area under the curve (AUC): 0.81, sensitivity: 0.83 (0.59–0.96), specificity: 0.72 (0.47–0.90), p-value: <0.001. The functionality of the developed device has been demonstrated with the testing of chemical standards and a simplified breath study using peppermint capsules. It is our intention to deploy this system in a UK hospital in an upcoming breath research study.

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Section: Discussionmentioning

confidence: 99%

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

et al. 2020

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“…The odour "fingerprint" captured by the gas sensors can then be analysed and identified with pattern classification methods, e.g., Principal Components Analysis (PCA), Cluster Analysis (CA), Support Vector Machine (SVM), and Artificial neural networks (ANNs). E-nose has been extensively applied in the areas of agriculture, medical diagnosis, environmental monitoring and protection, food safety, the military, cosmetics and pharmaceuticals [1,[4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, by using different E-nose platforms, the studies on E-nose mainly focus on two different parts: 1. The design of hardware system (such as sensor design and main control system design) [9,[11][12][13][14][15][16][17]; 2. The algorithms for Enose, such as data pre-processing methods and odour classification methods [9,[18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…The design of hardware system (such as sensor design and main control system design) [9,[11][12][13][14][15][16][17]; 2. The algorithms for Enose, such as data pre-processing methods and odour classification methods [9,[18][19][20][21][22][23][24][25]. Moreover, some researchers develop their E-nose research based on the famous commercial E-nose products (such as the fox E-nose (Alpha MOS, France), the portable Cyranose 320 (Cyrano Science, USA), Airsense PEN2 and PEN3 (Airsense Analytics GmbH, Germany) [1,[26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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A novel data pre-processing method for odour detection and identification system

Zhang

Liu

Ueland

et al. 2019

Sensors and Actuators A: Physical

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This paper presents a novel electronic nose (E-nose) data pre-processing method, based on a recently developed non-parametric kernel-based modelling (KBM) approach. The proposed method is tested by an automated odour detection and classification system, named "NOS.E", developed by the NOS.E team in University of Technology Sydney. Experimental results show that when extracting the derivative-related features from signals collected by the NOS.E, the proposed non-parametric KBM odour data pre-processing method achieves more reliable and stable pre-processing results comparing with other pre-processing methods such as wavelet package correlation filter (WPCF), mean filter (MF), polynomial curve fitting (PCF) and locally weighted regression (LWR). Based on these derivative-related features, the NOS.E can achieve a 96.23% accuracy of classification with the popular Support Vector Machine (SVM) classifier.

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“…Various chemical sensors for the detection of odorants have been reported, which are based on metal-oxide semiconductors (Zhang, et al, 2014), quartz crystal microbalances (Herrmann, et al, 2002) and ultraviolet absorption spectroscopy (Camou, et al, 2012). However, the continued active use of dogs in the detection of illegal drugs and explosives indicates that the sensitivity and selectivity of these chemical sensors are not sufficient and come short of biological olfaction (Misawa, et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Effective collection of volatile organic compounds in water using rimming flow with odorant-binding proteins

Kida

Tsukada

Tagawa

et al. 2015

Mechanical Engineering Journal

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Water solution of volatile organic compounds (VOCs) in air, especially explosive materials, is required to detect VOCs by biosensors efficiently. We developed a collecting device like olfactory mechanism of animals: thin film of water including odorant binding proteins (OBPs), which play a major role in transport of hydrophobic odor molecules to olfactory receptors. Thin liquid film was generated using rimming flow on the inner surface of a rotating glass cylinder. Two kinds of OBPs derived from Bombyx mori (Pheromone Binding Protein 1: PBP1, General Odorant Binding Protein 2: GOBP2) were used as additive in pure water. Eugenol was used to simulate an explosive substance for collection. The sample gas including dilute eugenol was collected by the rimming flow device. The concentration of eugenol in solution was measured by gas chromatography. The experimental results showed that the efficiency for the collection of eugenol increased with the revolution rate of the cylinder. Increase in the efficiency was mainly attributed to the spread of the liquid in the cylinder. Furthermore, as the revolution rate was increased, the mole fraction of eugenol near the inside wall was higher than at the axis of rotation by centrifugal force. This might enhance the dissolution of eugenol in water. The collection efficiency further increased by the adding of both proteins and PBP1 exhibited higher affinity for eugenol.

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A rapid discreteness correction scheme for reproducibility enhancement among a batch of MOS gas sensors

Cited by 40 publications

References 23 publications

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

A novel data pre-processing method for odour detection and identification system

Effective collection of volatile organic compounds in water using rimming flow with odorant-binding proteins

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