Electronic Nose Based on Temperature Modulation of MOS Sensors for Recognition of Excessive Methanol in Liquors

Liu, Huabin; Wu, Ranchao; Guo, Qi; Hua, Zhongqiu; Wu, Yi

doi:10.1021/acsomega.1c04350

Cited by 14 publications

(8 citation statements)

References 38 publications

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“…In the case of electronic noses operating in the mode of modulating the heating voltage of the MOX sensor, the shape and duration of the modulation is important. One of the most commonly used shapes is the rectangle [ 8 , 9 , 10 , 11 ]. The staricase-like profile has been used in other studies [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species

Borowik,

Dyshko,

Tarakowski

et al. 2023

Sensors

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Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor’s heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor’s response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species

Borowik,

Dyshko,

Tarakowski

et al. 2023

Sensors

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“…The sensor arrays composed of WO 3 and Pd–WO 3 further enhance the accuracy of the e-nose based on self-heating modulation. Table summarizes the recognition performance of the state-of-the-art thermal-activated and light-activated e-nose. − Apart from the simple device structure and relatively low power consumption, the recognition time of self-heating modulation is a dozen times lower than others; sufficient molecular features could be extracted and instantaneously analyzed within 0.5–1 s, which opens the opportunity for the emerging application e-nose in highly toxic gas alarming and outdoor air quality monitoring.…”

Section: Results and Discussionmentioning

confidence: 99%

Prompt Electronic Discrimination of Gas Molecules by Self-Heating Temperature Modulation

Li,

Chananonnawathorn,

Pan

et al. 2023

ACS Sens.

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Though considerable progress has been achieved on gas molecule recognition by electronic nose (e-nose) comprised of nonselective (metal oxide) semiconductor chemiresistors, extracting adequate molecular features within short time (<1 s) remains a big obstacle, which hinders the emerging e-nose applications in lethal or explosive gas warning. Herein, by virtue of the ultrafast (∼20 μs) thermal relaxation time of self-heated WO 3 -based chemiresistors fabricated via oblique angle deposition, instead of external heating, self-heating temperature modulation has been proposed to generate sufficient electrical response features. Accurate discrimination of 12 gases (including 3 xylene isomers with the same function group and molecular weight) has been readily achieved within 0.5−1 s, which is one order faster than the state-of-the-art e-noses. A smart wireless e-nose, capable of instantaneously discriminating target gas in ambient air background, has been developed, paving the way for the practical applications of e-nose in the area of homeland security and public health.

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“…Common pattern recognition methods included artificial neural network (ANN) 139 and subspace projection such as principal component analysis (PCA), 140 linear discriminant analysis (LDA), 141 fast Fourier transform (FFT), 142 discrete wavelet transform (DWT), 143 etc. In addition, the curve fitting method, 144 waveform descriptor, 145 nonlinear subspace projection (self-organizing mapping, Sammons mapping, etc. ), and clustering in feature space 146 were also employed in this field.…”

Section: Strategies To Improve the Detection Selectivitymentioning

confidence: 99%

Approaches for selectivity improvement of conductometric gas sensors: an overview

Li,

Zhao,

Wang

et al. 2024

Sens. Diagn.

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Electronic Nose Based on Temperature Modulation of MOS Sensors for Recognition of Excessive Methanol in Liquors

Cited by 14 publications

References 38 publications

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species

Prompt Electronic Discrimination of Gas Molecules by Self-Heating Temperature Modulation

Approaches for selectivity improvement of conductometric gas sensors: an overview

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