Artificial Sensory Systems Combined with UV-Vis Spectrophotometry as a Robust Approach for VOCs Analysis of Human Urine and Exhaled Breath. (2017)
- Record Type:
- Journal Article
- Title:
- Artificial Sensory Systems Combined with UV-Vis Spectrophotometry as a Robust Approach for VOCs Analysis of Human Urine and Exhaled Breath. (2017)
- Main Title:
- Artificial Sensory Systems Combined with UV-Vis Spectrophotometry as a Robust Approach for VOCs Analysis of Human Urine and Exhaled Breath
- Authors:
- Saidi, Tarik
Moufid, Mohamed
Zaim, Omar
Bari, Nezha El
Ionescu, Radu
Bouchikhi, Benachir - Abstract:
- Abstract: The aim of this work was to investigate the ability of electronic nose (E-nose) and voltammetric electronic tongue (VE-tongue) combined with UV-Vis spectrophotometry to classify breath and urine samples according to the creatinine levels (CLs). Principal Component Analysis (PCA), Support Vector Machines (SVMs) and Partial Least Squares-Regression (PLS-R) were applied for analyzing both E-nose and VE-tongue datasets. As results, PCA on E-nose data represents certain limitations on the discrimination of breath samples depending on three CLs. While SVMs leads to an excellent classification of the studied breath samples. Otherwise, both PCA and SVMs performed on the VE-tongue data revealed that urine samples could be classified accurately according to three CLs. Moreover, PLS-R reveals a good correlation between the VE-tongue and spectroscopic Jaffe's method. In summary, we might say that the electronic sensing systems offer an efficient tools for breath and urine analysis.
- Is Part Of:
- Procedia technology. Volume 27(2016)
- Journal:
- Procedia technology
- Issue:
- Volume 27(2016)
- Issue Display:
- Volume 27, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 27
- Issue:
- 2016
- Issue Sort Value:
- 2016-0027-2016-0000
- Page Start:
- 256
- Page End:
- 257
- Publication Date:
- 2017
- Subjects:
- Breath analysis -- Urine analysis -- Creatinine content -- Electronic nose -- Voltametric electronic tongue -- Pattern recognition methods
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605 - Journal URLs:
- http://www.sciencedirect.com/science/journal/22120173 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.protcy.2017.04.110 ↗
- Languages:
- English
- ISSNs:
- 2212-0173
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library DSC - BLDSS-3PM
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- 2868.xml