Toxic gases detection using PARAFAC and PCA to protect the environment

Major goal of this research paper is to monitor theenvironment but also identify butane, ethane, propane as well as acetoneand other organic gases in solid waste. Sensors employed to identify toxic gases from organic waste are shown here. The health of environment globally greatly depends on a green...

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Vydáno v:Materials today : proceedings
Hlavní autoři: Rai, Pratiksha, Hasan Saeed, Syed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2023
Témata:
Discrimination
Electronic nose device
Multi sensor array
Odour
PARAFAC
Quantification
Discrimination
Quantification
Multi sensor array
Electronic nose device
PARAFAC
Odour
ISSN:2214-7853, 2214-7853
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Shrnutí:Major goal of this research paper is to monitor theenvironment but also identify butane, ethane, propane as well as acetoneand other organic gases in solid waste. Sensors employed to identify toxic gases from organic waste are shown here. The health of environment globally greatly depends on a green as well as pollution-free environment. Atmosphere around us is being impacted by several types of pollution. In thisreview paper primarily addresses tracking of organic gases found in the environment, a highly sensitive subject that has a negative effect on public health also upsets the biological equilibrium ofplanet.Electronic nose is used in a variety of scientific study domains as well as industrial activities, environmental factors as well as pollutants, quality of air on space stations &shuttles, medicine bodily function, food manufacturing, but alsotoxicology in the military. In this paper we are using 4sensors in placed of 6, and findings were presented as score, varianceas well asloading plots with cross validation. Consequently, our goal is to create a sensor based arraysystem that can detect the most polluting gases while also being highly responsive, precise, affordable, and energy-efficient. Furthermore, for detection of gases we use parallel factor analysis and compare it with the principal component analysis (PCA) with cross- validation.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2023.03.360