An interactive software tool for gas identification

Djelouat, Hamza; Ali, Amine Ait Si; Amira, Abbes; Bensaali, Fayçal

doi:10.1016/j.jngse.2017.08.030

Cited by 7 publications

(7 citation statements)

References 53 publications

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“…However, more resources are used. An interactive software that uses all the mentioned algorithms have been developed in [35]. More details about the use of PCA and DT can be found in [36] while the details about the use of LDA compared to PCA can be found in [37].…”

Section: Accuracy =mentioning

confidence: 99%

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

et al. 2018

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This paper presents the development of a wireless low power reconfigurable self-calibrated multi-sensing platform for gas sensing applications. The proposed electronic nose (EN) system monitors gas temperatures, concentrations and mixtures wirelessly using the radio-frequency identification (RFID) technology. The EN takes the form of a set of gas and temperature sensors as well as multiple pattern recognition algorithms implemented on the Zynq system on chip (SoC) platform. The gas and temperature sensors are integrated on a semi-passive RFID tag to reduce the consumed power. Various gas sensors are tested including an in-house fabricated 4 × 4 SnO2 based sensor and 7 commercial Figaro sensors. The Data is transmitted to the Zynq based processing unit using a RFID reader where it is processed using multiple pattern recognition algorithms for dimensionality reduction and classification. Multiple algorithms are explored for optimum performance including principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction while decision tree (DT) and k-nearest neighbors (KNN) are assessed for classification purpose. Different gases are targeted at diverse concentration including carbon monoxide (CO), ethanol (C2H6O), carbon dioxide (CO2), propane (C3H8), ammonia (N H3) and hydrogen (H2). An accuracy of 100% is achieved in many cases with an overall accuracy above 90% in most scenarios. Finally, the hardware/software heterogeneous solution to implementation PCA, LDA, DT and KNN on the Zynq SoC shows promising results in terms of resources usage, power consumption and processing time.

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Section: Accuracy =mentioning

confidence: 99%

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

et al. 2018

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“…Several analytical instrumental techniques are successfully used in the identification of gases. These include infra-red spectroscopy, , mass spectrometry, , Raman spectroscopy, photoacoustic spectroscopy, and using electronic nose systems based on different types of sensor arrays. − Nowadays, electronic nose systems are employed as the primary gas identification means, which typically involve sensor arrays, signal acquisition and processing, pattern recognition, and reference database. The common sensor types utilized in electronic noses are metal oxide semiconductors , and chemiresistive, electrochemical, gravimetric (SAW, BAW, QCM, etc.…”

Section: Introductionmentioning

confidence: 99%

Gas Identification by Simultaneous Permeation through Parallel Membranes: Proof of Concept

Marzouk

Namous

2022

Anal. Chem.

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This paper describes an experimental system for simultaneous permeation of a pressurized test gas through different gas permeable membranes and provides a proof of concept for a novel approach for gas identification/fingerprinting for potential construction of electronic noses. The design, construction, and use of a six-channel system which allows simultaneous gas permeation from a single pressurized gas compartment through six different parallel membranes are presented. The permeated gas is accumulated in confined spaces behind the respective membranes. The rate of gas pressure accumulation behind each membrane is recorded and used as a measure of the gas permeation rate through the membrane. The utilized gas permeable membranes include Teflon AF, silicone rubber, tracketch hydrophilic polycarbonate, track-etch hydrophobic polycarbonate, track-etch polyimide, nanoporous anodic aluminum oxide, zeolite ZSM-5, and zeolite NaY. An analogy between the rate of pressure accumulation of the permeating gas behind the membrane and the charging of an electric capacitor in a single series RC circuit is proposed and thoroughly validated. The simultaneous permeation rates through different membranes demonstrated a very promising potential as characteristic fingerprints for 10 test gases, that is, helium, neon, argon, hydrogen, nitrogen, carbon dioxide, methane, ethane, propane, and ethylene, which are selected as representative examples of mono-, di-, tri-, and polyatomic gases and to include some homologous series as well as to allow testing the potential of the proposed system to discriminate between closely related gases such as ethane and ethylene or carbon dioxide and propane which have almost identical molecular masses. Finally, a preliminary investigation of the possibility of applying the developed gas permeation system for semiquantitative analysis of the CO 2 −N 2 binary mixture is also presented.

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“…In 2020, Leon-Medina et al [2] developed a methodology that seeks to improve the classification accuracy with an approach based on non-linear feature extraction of signals obtained with electronic tongue type sensor array devices. This methodology is composed of several stages: (1) Data unfolding, (2) Normalization, (3) Non-linear dimensionality reduction, (4) Classification by means of a supervised machine learning model and finally a (5) Cross validation [2]. The application of the methodology in each stage includes the execution of algorithms in the software Matlab ® .…”

Section: Introductionmentioning

confidence: 99%

“…Due to the number of stages and the different configuration options of the parameters in the algorithms, the need was generated to develop a tool that would facilitate the application of this methodology, guiding the user through the different stages and making the configuration of the algorithms more user-friendly. One of the main advantages of a graphical user interface (GUI) is that it makes an implemented system easy to use, understand and evaluate [4].…”

Section: Introductionmentioning

confidence: 99%

Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Mendez-Lopez

Leon-Medina

Burgos

2021

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry

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Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

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An interactive software tool for gas identification

Cited by 7 publications

References 53 publications

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

Gas Identification by Simultaneous Permeation through Parallel Membranes: Proof of Concept

Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

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