Electronic nose system and principal component analysis technique for gases identification

Bedoui, Souhir; Faleh, Rabeb; Samet, Hekmet; Kachouri, Abdennaceur

doi:10.1109/ssd.2013.6564152

Cited by 31 publications

(12 citation statements)

References 4 publications

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“…fingerprints in order to discriminate between various gases in the air [6,7,8,9]. Nevertheless, EN system performance is prone to several issues, for instance, the gas sensor properties often change with time, which is known as the drift problem [10], this problem can occur if the gas sensors are exposed to reactive gases for a long period.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

An interactive software tool for gas identification

Djelouat

Ali

Amira

et al. 2018

Journal of Natural Gas Science and Engineering

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This paper presents the design of an interactive graphical user interface (GUI) to monitor and quantify a developed electronic nose (EN) platform for gas identification. To this end, an EN system has been implemented using a multi-sensing embedded platform comprised of a data acquisition unit, an RFID module and a signal processing unit. The gas data are collected using two different types of gas sensors, namely, seven commercial Figaro sensors and in-house fabricated 4 × 4 tin-oxide gas array sensor. The collected gas data are processed for identification by means of dimensionality reduction algorithms and classification techniques where the software implementation and the quantification of these algorithms have been carried out. Subsequently, the GUI was designed to enable several operations. The GUI allows the user to visualize the sensors responses for any selected gas at any point of the acquisition process as well as visualizing the data distribution. Beside, it provides an easy approach to evaluate the EN system performance in terms of data identification and execution time by computing the classification accuracy using a 10-fold cross validation technique. Furthermore, the GUI, which is freely distributed, grants the users the privilege to upload other types of data to enable different pattern recognition applications.

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Section: Accepted Manuscriptmentioning

confidence: 99%

An interactive software tool for gas identification

Djelouat

Ali

Amira

et al. 2018

Journal of Natural Gas Science and Engineering

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“…This powerful tool for data analysis was selected in this application since it is an effective linear unsupervised and supervised method to extract the most relevant information and project data from several sensors to a two-dimensional plane using a scores plot. Therefore, it is possible to discriminate properly a measure set, finding the directions of maximal variance [19]. PCA returns a new basis which is a linear combination of the original basis.…”

Section: Data Processingmentioning

confidence: 99%

Response Optimization of a Chemical Gas Sensor Array Using Temperature Modulation

Acevedo¹,

Benjumea²

2018

Preprint

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This paper consists in the design and implementation of a simple conditioning circuit to optimize the electronic nose performance, where a temperature modulation method was applied to the heating resistor, in order to study the sensor’s response and determine whether they are able to make the discrimination when are exposed to different Volatile Organic Compounds (VOC’s). This study was based on determining the efficiency of the gas sensors to be used in order to perform an Electronic Nose, improving the sensitivity, selectivity and repeatability of the measuring system and selecting the type of modulation (e.g. Pulse Width Modulation) for the analytes detection (i.e, Moscatel wine samples (2% of Alcohol) and Ethyl-Alcohol (70%)). The results demonstrated that using temperature modulation technique to the heater of sensors, it is possible to achieve a good discrimination of VOC's in fast and easy form, through a chemical sensors array. A discrimination model based on Principal Component Analysis (PCA) was implemented to each sensor, and data responses obtained gave a variance of 94.5% and 100% accuracy.

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“…Some linear methods such as principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SVM), etc., were used in the analysis of odor discrimination [ 16 ]. PCA is an unsupervised method ignoring discriminant information, which is a popular method for dimensionality reduction [ 17 ]. LDA is a supervised method for classification by finding decision surfaces and calculating the signed orthogonal distance of data points.…”

Section: Introductionmentioning

confidence: 99%

An Odor Labeling Convolutional Encoder–Decoder for Odor Sensing in Machine Olfaction

Wen

et al. 2021

Sensors

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Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we propose an odor labeling convolutional encoder–decoder (OLCE) for odor identification in machine olfaction. OLCE composes a convolutional neural network encoder and decoder where the encoder output is constrained to odor labels. An electronic nose was used for the data collection of gas responses followed by a normative experimental procedure. Several evaluation indexes were calculated to evaluate the algorithm effectiveness: accuracy 92.57%, precision 92.29%, recall rate 92.06%, F1-Score 91.96%, and Kappa coefficient 90.76%. We also compared the model with some algorithms used in machine olfaction. The comparison result demonstrated that OLCE had the best performance among these algorithms.

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Electronic nose system and principal component analysis technique for gases identification

Cited by 31 publications

References 4 publications

An interactive software tool for gas identification

An interactive software tool for gas identification

Response Optimization of a Chemical Gas Sensor Array Using Temperature Modulation

An Odor Labeling Convolutional Encoder–Decoder for Odor Sensing in Machine Olfaction

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