Multi-channel piezoelectric quartz crystal sensor with principal component analysis and back-propagation neural network for organic pollutants from petrochemical plants

Chiao-Chen Chen, Jeng Shong Shih

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A multi-channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home-made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4-tert-Butylcalix[6]arene, Cholesteryl chloroformate, C60-Polyphenyl acetylene (C60-PPA) and Ag(I)/cryptand-2,2/ Ethylene diamine/NH3/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back-propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi-channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068-1.127 mg/L for these organic pollutants. The multi-channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1-9.6%. Furthermore, this multi-channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.

Original languageEnglish
Pages (from-to)979-993
Number of pages15
JournalJournal of the Chinese Chemical Society
Volume55
Issue number5
DOIs
Publication statusPublished - 2008 Jan 1

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Petrochemical plants
Quartz
Organic pollutants
Backpropagation
Principal component analysis
Neural networks
Crystals
Chemical sensors
Sensors
Coatings
Adsorbents
Acetylene
Styrene
Diamines
Sensor arrays
Chloroform
Benzene
Polyvinyl Chloride
Linear regression
Regression analysis

All Science Journal Classification (ASJC) codes

  • Chemistry(all)

Cite this

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title = "Multi-channel piezoelectric quartz crystal sensor with principal component analysis and back-propagation neural network for organic pollutants from petrochemical plants",
abstract = "A multi-channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home-made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4-tert-Butylcalix[6]arene, Cholesteryl chloroformate, C60-Polyphenyl acetylene (C60-PPA) and Ag(I)/cryptand-2,2/ Ethylene diamine/NH3/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back-propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi-channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068-1.127 mg/L for these organic pollutants. The multi-channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1-9.6{\%}. Furthermore, this multi-channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.",
author = "Chiao-Chen Chen and Shih, {Jeng Shong}",
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N2 - A multi-channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home-made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4-tert-Butylcalix[6]arene, Cholesteryl chloroformate, C60-Polyphenyl acetylene (C60-PPA) and Ag(I)/cryptand-2,2/ Ethylene diamine/NH3/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back-propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi-channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068-1.127 mg/L for these organic pollutants. The multi-channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1-9.6%. Furthermore, this multi-channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.

AB - A multi-channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home-made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4-tert-Butylcalix[6]arene, Cholesteryl chloroformate, C60-Polyphenyl acetylene (C60-PPA) and Ag(I)/cryptand-2,2/ Ethylene diamine/NH3/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back-propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi-channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068-1.127 mg/L for these organic pollutants. The multi-channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1-9.6%. Furthermore, this multi-channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.

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