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Article

Classification and Identification of Industrial Gases Based on Electronic Nose Technology

1
School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
School of Electric and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
3
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 5033; https://doi.org/10.3390/s19225033
Received: 11 October 2019 / Revised: 12 November 2019 / Accepted: 15 November 2019 / Published: 18 November 2019
(This article belongs to the Section Intelligent Sensors)
Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption. View Full-Text
Keywords: electronic nose; industrial gas; classification and identification; kernel discriminant analysis electronic nose; industrial gas; classification and identification; kernel discriminant analysis
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MDPI and ACS Style

Li, H.; Luo, D.; Sun, Y.; GholamHosseini, H. Classification and Identification of Industrial Gases Based on Electronic Nose Technology. Sensors 2019, 19, 5033. https://doi.org/10.3390/s19225033

AMA Style

Li H, Luo D, Sun Y, GholamHosseini H. Classification and Identification of Industrial Gases Based on Electronic Nose Technology. Sensors. 2019; 19(22):5033. https://doi.org/10.3390/s19225033

Chicago/Turabian Style

Li, Hui, Dehan Luo, Yunlong Sun, and Hamid GholamHosseini. 2019. "Classification and Identification of Industrial Gases Based on Electronic Nose Technology" Sensors 19, no. 22: 5033. https://doi.org/10.3390/s19225033

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