Evidence map›Paper›PMID 32570813›Full record

ArticleMicromachines2020

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

Saifur Rahman, Abdullah S Alwadie, Muhammed Irfan, Rabia Nawaz, Mohsin Raza, Ehtasham Javed, Muhammad Awais

Abstract read
In one paragraph

Article in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Saifur RahmanElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia.
Abdullah S AlwadieElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia.ORCID 0000-0003-0929-8221
Muhammed IrfanElectrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia.ORCID 0000-0003-4161-6875
Rabia NawazDepartment of Physics, COMSATS University, Park Road, Chak Shahzad Islamabad 45550, Pakistan.
Mohsin RazaDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.ORCID 0000-0002-7351-9749
Ehtasham JavedHelsinki Institute for Life Sciences, Neuroscience Center, University of Helsinki, 00014 Helsinki, Finland.
Muhammad AwaisEnergy and Environment Institute, Faculty of Science and Engineering, University of Hull, Hull 7RX, UK.ORCID 0000-0001-6421-9245

Funding

Deanship of Scientific Research, Najran University NU/ESCI/16/046
6 · The paper itself

Abstract

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.

Indexed as

detectionelectronicelectronic nosegas sensorsmetal oxide semiconductor (MOS) sensorsmultivariate analysisprincipal components analysis

Identifiers

PMID32570813
PMCPMC7345365

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.