Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for ga...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-09, Vol.19 (18), p.3917
Main Authors: Fan, Shurui, Li, Zirui, Xia, Kewen, Hao, Dongxia
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Chromatography
cross-sensitivity
Datasets
Decision trees
Discriminant analysis
Eigenvalues
Food additives
Gas mixtures
gas sensor array
Gas sensors
Identification
Machine learning
Methods
Natural gas
Neural networks
Oils & fats
Optimization
particle swarm optimization
PCA
Pollutants
Principal components analysis
Qualitative analysis
Qualitative research
Quantitative analysis
random forest
Sensitivity
Sensor arrays
Sensors
Support vector machines
Wavelet transforms
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Summary:The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters and to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19183917