Classification of chemical warfare agents using thick film gas sensor array

2005 ◽  
Vol 108 (1-2) ◽  
pp. 298-304 ◽  
Author(s):  
Nak-Jin Choi ◽  
Jun-Hyuk Kwak ◽  
Yeon-Tae Lim ◽  
Tae-Hyun Bahn ◽  
Ky-Yeol Yun ◽  
...  
2013 ◽  
Vol 13 (12) ◽  
pp. 4924-4930 ◽  
Author(s):  
Sunny ◽  
V. N. Mishra ◽  
R. Dwivedi ◽  
R. R. Das

2014 ◽  
Vol 494-495 ◽  
pp. 955-959 ◽  
Author(s):  
Wen Na Zhang ◽  
Guo Jun Qin ◽  
Niao Qing Hu

Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with three-way data array. Through the analysis and classification of three kinds of oil odor samples, the performance of PARAFAC in gas sensor array signal analysis is verified and validated.


Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 251 ◽  
Author(s):  
Grzegorz Łagód ◽  
Sylwia M. Duda ◽  
Dariusz Majerek ◽  
Adriana Szutt ◽  
Agnieszka Dołhańczuk-Śródka

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.


2015 ◽  
Vol 15 (2) ◽  
pp. 1252-1260 ◽  
Author(s):  
Sunny ◽  
Vinod Kumar ◽  
V. N. Mishra ◽  
R. Dwivedi ◽  
R. R. Das

2009 ◽  
Vol 1 (1) ◽  
pp. 232-235 ◽  
Author(s):  
D. Matatagui ◽  
J. Martí ◽  
M.J. Fernández ◽  
J.L. Fontecha ◽  
J. Gutiérrez ◽  
...  

2000 ◽  
Vol 65 (1-3) ◽  
pp. 327-330 ◽  
Author(s):  
Chul Han Kwon ◽  
Dong Hyun Yun ◽  
Hyung-Ki Hong ◽  
Seung-Ryeol Kim ◽  
Kyuchung Lee ◽  
...  

Author(s):  
M F R M Mawardzi ◽  
A Japper-Jaafar ◽  
M S Najib ◽  
S M Daud ◽  
T M Y S T Ya

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