Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis.

detection electronic electronic nose gas sensors metal oxide semiconductor (MOS) sensors multivariate analysis principal components analysis

Journal

Micromachines
ISSN: 2072-666X
Titre abrégé: Micromachines (Basel)
Pays: Switzerland
ID NLM: 101640903

Informations de publication

Date de publication:
18 Jun 2020
Historique:
received: 16 05 2020
revised: 16 06 2020
accepted: 16 06 2020
entrez: 24 6 2020
pubmed: 24 6 2020
medline: 24 6 2020
Statut: epublish

Résumé

Gas sensors are critical components when adhering to health safety and environmental policies in various manufacturing industries, such as the petroleum and oil industry; scent and makeup production; food and beverage manufacturing; chemical engineering; pollution monitoring. In recent times, gas sensors have been introduced to medical diagnostics, bioprocesses, and plant disease diagnosis processes. There could be an adverse impact on human health due to the mixture of various gases (e.g., acetone (A), ethanol (E), propane (P)) that vent out from industrial areas. Therefore, it is important to accurately detect and differentiate such gases. Towards this goal, this paper presents a novel electronic nose (e-nose) detection method to classify various explosive gases. To detect explosive gases, metal oxide semiconductor (MOS) sensors are used as reliable tools to detect such volatile gases. The data received from MOS sensors are processed through a multivariate analysis technique to classify different categories of gases. Multivariate analysis was done using three variants-differential, relative, and fractional analyses-in principal components analysis (PCA). The MOS sensors also have three different designs: loading design, notch design, and Bi design. The proposed MOS sensor-based e-nose accurately detects and classifies three different gases, which indicates the reliability and practicality of the developed system. The developed system enables discrimination of these gases from the mixture. Based on the results from the proposed system, authorities can take preventive measures to deal with these gases to avoid their potential adverse impacts on employee health.

Identifiants

pubmed: 32570813
pii: mi11060597
doi: 10.3390/mi11060597
pmc: PMC7345365
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Deanship of Scientific Research, Najran University
ID : NU/ESCI/16/046

Références

Chem Cent J. 2017 Mar 24;11(1):27
pubmed: 29086811
Sensors (Basel). 2018 Jan 08;18(1):
pubmed: 29316723
Sensors (Basel). 2018 Jul 16;18(7):
pubmed: 30012960
Sensors (Basel). 2012 Oct 25;12(11):14363-81
pubmed: 23202165
Sensors (Basel). 2017 Aug 31;17(9):
pubmed: 28858220
IEEE J Biomed Health Inform. 2019 Jan;23(1):197-207
pubmed: 29994291
Sensors (Basel). 2009;9(7):5099-148
pubmed: 22346690
Int J Environ Res Public Health. 2020 Feb 08;17(3):
pubmed: 32046302
Diagnostics (Basel). 2018 Jan 31;8(1):
pubmed: 29385067
Arch Med Res. 2018 Jan;49(1):74-76
pubmed: 29678351
Talanta. 2015 Nov 1;144:329-40
pubmed: 26452830
Sensors (Basel). 2019 Jan 08;19(1):
pubmed: 30626158

Auteurs

Saifur Rahman (S)

Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia.

Abdullah S Alwadie (AS)

Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia.

Muhammed Irfan (M)

Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia.

Rabia Nawaz (R)

Department of Physics, COMSATS University, Park Road, Chak Shahzad Islamabad 45550, Pakistan.

Mohsin Raza (M)

Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.

Ehtasham Javed (E)

Helsinki Institute for Life Sciences, Neuroscience Center, University of Helsinki, 00014 Helsinki, Finland.

Muhammad Awais (M)

Energy and Environment Institute, Faculty of Science and Engineering, University of Hull, Hull 7RX, UK.

Classifications MeSH