An entire feature extraction method of metal oxide gas sensors

Zhang, Shunping; Xie, Changsheng; Hu, Mulin; Li, Huayao; Bai, Zikui; Zeng, Dawen

doi:10.1016/j.snb.2008.01.015

Cited by 52 publications

(30 citation statements)

References 18 publications

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“…For example, Mielle and Marquis (2000) analysed the sensor drift for an ethanol concentration. A sensory response feedback process was discussed by Zhang et al (2008) for a few chemical volatiles, and Garcia-Gonzalez and Aparicio (2010) for volatile compounds of virgin olive oil.…”

Section: Resultsmentioning

confidence: 99%

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

Gancarz

Wawrzyniak

Gawrysiak‐Witulska

et al. 2017

International Agrophysics

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A b s t r a c t. Investigations were performed to examine the possibility of using an electronic nose to monitor development of fungal microflora during the first eighteen days of rapeseed storage. The Cyranose 320 device manufactured by Sensigent was used to analyse volatile organic compounds. Each sample of infected material was divided into three parts and the degree of spoilage was measured in three ways: analysis of colony forming units, determination of ergosterol content, and measurement of volatile organic compounds with the e-nose. Principal component analysis was performed on the generated patterns of signals and six groups of different spoilage levels were isolated. An analysis of sensorgrams for a few sensors with a strong signal for each group of rapeseed spoilage was performed. The ratio of the association time to the steady state was calculated. This ratio was different for the low level and the highest level of ergosterol and colony forming units. The results have shown that the e-nose can be a useful tool for quick estimation of the degree of rapeseed spoilage.

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Section: Resultsmentioning

confidence: 99%

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

Gancarz

Wawrzyniak

Gawrysiak‐Witulska

et al. 2017

International Agrophysics

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“…Taking the gas-sensing measurement to 50 ppm formaldehyde, ammonia, and benzene as an example, the realtime sensitivity of the gas sensor is transformed from y axis to x axis, when the first derivative of sensitivity to time is set as y axis, as shown in Figure 5. Under a determined working condition, including temperature, surface state of gas-sensing materials, and ambient gas composition, the resistance of gas sensor is fixed [17]. So, the surface state of In 2 O 3 in a determined working condition can be presented by the current of sensor.…”

Section: B Gas-sensing Propertiesmentioning

confidence: 99%

Synthesis of novel layer-packed In<inf>2</inf>O<inf>3</inf> nanostructures and their application in gas sensor for detecting indoor air contaminants

Liu

Wan

et al. 2011

2011 6th IEEE International Conference on Nano/Micro Engineered and Molecular Systems

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A novel layer-packed In 2 O 3 nanostructure and its application in gas sensor for the detection of indoor air contaminants were reported. The special In 2 O 3 nanomaterials were prepared by annealing In 2 S 3 precursors synthesized via a surfactant-assisted assembly route. The influencing factors towards the morphology of In 2 S 3 precursors were also investigated. In gas-sensing measurements, typical indoor air contaminants, including formaldehyde, ammonia, and benzene were employed as target gases. We found that the as-fabricated sensor exhibited a sensitive property to analytes, especially towards formaldehyde. Besides, by using principal component analysis (PCA) and comparison on kinetic processes of gas adsorption-desorption, the gas sensor show a significantly selective performance due to the special layer-packed structure, which enable it to be promisingly applied for environmental monitoring towards indoor air contaminants.

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“…For comparison, here we extract other 4 groups of geometric features defined by references [4], [5], [6] and [7], respectively. Each group has 5 or 4 geometric features extracted from each signal curve.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Currently, there are three main kinds of features are extracted in the signal of odor response, geometric features [4][5][6][7], frequency features [8][9], and curve features extracted by fitting the signal curve [10]. Geometric features are features like the peak value of the signal, the time from the beginning to the peak, the area under the curve, gradient, and curvatures.…”

Section: Introductionmentioning

confidence: 99%

An Effective Feature Extraction Method Used in Breath Analysis

Chen

Guo

et al. 2010

Lecture Notes in Computer Science

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It has been reported that human breath could represent some kinds of diseases. By analyzing the components of breath odor, it is easy to detect the diseases the subjects infected. The accuracy of breath analysis depends greatly on what feature are extracted from the response curve of breath analysis system. In this paper, we proposed an effective feature extraction method based on curve fitting for breath analysis, where breath odor were captured and processed by a self-designed breath analysis system. Two parametric analytic models were used to fit the ascending and descending part of the sensor signals respectively, and the set of best-fitting parameters were taken as features. This process is fast, robust, and with less fitting error than other fitting models. Experimental results showed that the features extracted by our method can significantly enhance the performance of subsequent classification algorithms.

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An entire feature extraction method of metal oxide gas sensors

Cited by 52 publications

References 18 publications

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

Synthesis of novel layer-packed In<inf>2</inf>O<inf>3</inf> nanostructures and their application in gas sensor for detecting indoor air contaminants

An Effective Feature Extraction Method Used in Breath Analysis

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