Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines

Gamboa, Juan C. Rodríguez; Silva, Adenilton J. da; Araujo, Ismael C. S.; Albarracín, E Eva Susana; Duran, Matias

doi:10.1016/j.snb.2020.128921

Cited by 60 publications

(20 citation statements)

References 15 publications

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“…As one can notice, examining Figure 1 and the description of the measurement procedure in section 2.1, such optimal time of data collection is shorter than half of the measurement time given in [11], [13]. In other research, [15] similar results have been found for measurements of other types of odors. An advantage of shortening the time of odor detection by an electronic nose is noticeable.…”

Section: Resultssupporting

confidence: 66%

“…We are interested in the analysis of the dependence of the classification accuracy on the odor measurement time. Recently Rodriguez Gamboa and coworkers [15] examined several datasets and used deep Ì ISSN: 2088-8708 learning and support vector machine models to demonstrate the potential of using only a part of electronic nose measurement data for correct odor classification. The used dataset is collected by custom-made e-nose consisting of Taguchi type MQ-series gas sensors.…”

Section: Introductionmentioning

confidence: 99%

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On data collection time by an electronic nose

Borowik

Adamowicz

Tarakowski

et al. 2021

IJECE

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<p>We use electronic nose data of odor measurements to build machine learning classiﬁcation models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classiﬁcation is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classiﬁcation performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shufﬂing cross-validation approach for determining the reported models’ average accuracy and standard deviation.</p>

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Section: Resultssupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

On data collection time by an electronic nose

Borowik

Adamowicz

Tarakowski

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…Recently, Rodriguez Gamboa and coworkers [ 63 ] reviewed studies on the possibility of using a much shorter time of data collection; however, as we verified, such an approach is not relevant for our e-nose device and/or types of odor.…”

Section: Results and Discussionmentioning

confidence: 79%

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

Borowik

Adamowicz

Tarakowski

et al. 2021

Sensors

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Compared with traditional gas chromatography–mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors’ response to the odors’ exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models’ performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.

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“…The accuracy is pretty close to the accuracy (~98%) using both RNN and CNN but higher than those obtained by other algorisms, such as the random forest (~90%), the support vector machine (~92%). 28 An accuracy of 100% was achieved by considering every sensor array as respective dataset, but this work ignored the variation between different sensor arrays 49 . Different from these works, our artificial inference system based on memristive devices is more effective for data collecting and processing in real time, which is advantageous for integrating with terminal devices in realistic scenarios.…”

Section: Resultsmentioning

confidence: 99%

An artificial olfactory inference system based on memristive devices

Wang

Huang

Wang

et al. 2021

InfoMat

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Due to the complexity of real environments, it is hard to detect toxic and harmful gases by sensors. To address such an issue, an artificial olfactory system is promoted, emulating the function of the human nose by means of gas sensors and an inference system. In this work, an artificial olfactory inference system based on memristive devices is developed to classify four gases (ethanol, methane, ethylene, and carbon monoxide) with 10 different concentrations. First, the spike trains converted from signals of the sensor array are inputted to a reservoir computing (RC) system based on volatile memristive devices, which extracts spatiotemporal features; then the features are processed by a classifier based on nonvolatile memristive devices; the output of the classifier indicates the classification result. Moreover, to reduce the device number and the power consumption, three strategies are applied to reduce the extracted features from the RC system. Eventually, the olfactory inference system successfully identifies the gases with a high accuracy of 95%.

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Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines

Cited by 60 publications

References 15 publications

On data collection time by an electronic nose

On data collection time by an electronic nose

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

An artificial olfactory inference system based on memristive devices

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