Estimation of chemical and physical characteristics of analyte vapors through analysis of the response data of arrays of polymer-carbon black composite vapor detectors

Brian C. Sisk, Nathan S Lewis

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Analysis of the signals produced by a collection of organic polymer-carbon black composite vapor detectors has been performed to assess the ability to estimate various chemical and physical properties of analyte vapors based on information contained in the response patterns of the detector array. A diverse array of composite chemiresistive vapor detectors was exposed to a series of 75 test analytes that had been selected from among five different chemical classes: alcohols, halogenated hydrocarbons, aromatics, unsubstituted hydrocarbons, and esters. The algorithmic task of interest was to use the resulting array of response data to assign one of the five chemical class labels to a test analyte, despite having left that analyte out of the model used to generate the class labels. Algorithms evaluated for this purpose included principal components analysis (PCA) and k-nearest neighbor (k-NN) analysis employing either Euclidean or Mahalanobis distance calculations. Each data cluster that was produced by replicate exposures to an individual analyte was well resolved from all of the other 74 analyte clusters. Furthermore, with the exception of the halide cluster, the analyte response clusters could be robustly grouped into supersets such that each of the five individual chemical classes was well-separated from every other class of analytes in principal component space. Accordingly, using either of the k-nearest neighbor algorithms, in excess of 85% of the non-halide test analyte exposures were correctly assigned to their chemical classes, and halides were only routinely confused with aromatics or esters but not with alcohols or hydrocarbons. The detector array response data also was found to contain semi-quantitative information regarding physicochemical properties of the members of the test analyte series, such as the degree of unsaturation of the carbon chain, the dipole moment, the molecular weight, the number of halogen atoms, and type of aromatic ring in the test analytes. The performance in these types of tasks is relevant for applications of a semi-selective array of vapor detectors in situations when no prior knowledge of the analyte identity is available and when there is no assurance that the test analyte will have been contained in the training set database produced by a compiling a library of responses from the detector array.

Original languageEnglish
Pages (from-to)268-282
Number of pages15
JournalSensors and Actuators, B: Chemical
Volume96
Issue number1-2
DOIs
Publication statusPublished - Nov 15 2003

Fingerprint

Soot
Carbon black
Polymers
Vapors
vapors
Detectors
composite materials
carbon
detectors
Composite materials
polymers
hydrocarbons
halides
Labels
esters
Esters
alcohols
Alcohols
Halogenated Hydrocarbons
Aromatic Hydrocarbons

Keywords

  • Conducting polymer vapor sensors
  • Sensor arrays
  • Solvent properties

ASJC Scopus subject areas

  • Analytical Chemistry
  • Electrochemistry
  • Electrical and Electronic Engineering

Cite this

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title = "Estimation of chemical and physical characteristics of analyte vapors through analysis of the response data of arrays of polymer-carbon black composite vapor detectors",
abstract = "Analysis of the signals produced by a collection of organic polymer-carbon black composite vapor detectors has been performed to assess the ability to estimate various chemical and physical properties of analyte vapors based on information contained in the response patterns of the detector array. A diverse array of composite chemiresistive vapor detectors was exposed to a series of 75 test analytes that had been selected from among five different chemical classes: alcohols, halogenated hydrocarbons, aromatics, unsubstituted hydrocarbons, and esters. The algorithmic task of interest was to use the resulting array of response data to assign one of the five chemical class labels to a test analyte, despite having left that analyte out of the model used to generate the class labels. Algorithms evaluated for this purpose included principal components analysis (PCA) and k-nearest neighbor (k-NN) analysis employing either Euclidean or Mahalanobis distance calculations. Each data cluster that was produced by replicate exposures to an individual analyte was well resolved from all of the other 74 analyte clusters. Furthermore, with the exception of the halide cluster, the analyte response clusters could be robustly grouped into supersets such that each of the five individual chemical classes was well-separated from every other class of analytes in principal component space. Accordingly, using either of the k-nearest neighbor algorithms, in excess of 85{\%} of the non-halide test analyte exposures were correctly assigned to their chemical classes, and halides were only routinely confused with aromatics or esters but not with alcohols or hydrocarbons. The detector array response data also was found to contain semi-quantitative information regarding physicochemical properties of the members of the test analyte series, such as the degree of unsaturation of the carbon chain, the dipole moment, the molecular weight, the number of halogen atoms, and type of aromatic ring in the test analytes. The performance in these types of tasks is relevant for applications of a semi-selective array of vapor detectors in situations when no prior knowledge of the analyte identity is available and when there is no assurance that the test analyte will have been contained in the training set database produced by a compiling a library of responses from the detector array.",
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N2 - Analysis of the signals produced by a collection of organic polymer-carbon black composite vapor detectors has been performed to assess the ability to estimate various chemical and physical properties of analyte vapors based on information contained in the response patterns of the detector array. A diverse array of composite chemiresistive vapor detectors was exposed to a series of 75 test analytes that had been selected from among five different chemical classes: alcohols, halogenated hydrocarbons, aromatics, unsubstituted hydrocarbons, and esters. The algorithmic task of interest was to use the resulting array of response data to assign one of the five chemical class labels to a test analyte, despite having left that analyte out of the model used to generate the class labels. Algorithms evaluated for this purpose included principal components analysis (PCA) and k-nearest neighbor (k-NN) analysis employing either Euclidean or Mahalanobis distance calculations. Each data cluster that was produced by replicate exposures to an individual analyte was well resolved from all of the other 74 analyte clusters. Furthermore, with the exception of the halide cluster, the analyte response clusters could be robustly grouped into supersets such that each of the five individual chemical classes was well-separated from every other class of analytes in principal component space. Accordingly, using either of the k-nearest neighbor algorithms, in excess of 85% of the non-halide test analyte exposures were correctly assigned to their chemical classes, and halides were only routinely confused with aromatics or esters but not with alcohols or hydrocarbons. The detector array response data also was found to contain semi-quantitative information regarding physicochemical properties of the members of the test analyte series, such as the degree of unsaturation of the carbon chain, the dipole moment, the molecular weight, the number of halogen atoms, and type of aromatic ring in the test analytes. The performance in these types of tasks is relevant for applications of a semi-selective array of vapor detectors in situations when no prior knowledge of the analyte identity is available and when there is no assurance that the test analyte will have been contained in the training set database produced by a compiling a library of responses from the detector array.

AB - Analysis of the signals produced by a collection of organic polymer-carbon black composite vapor detectors has been performed to assess the ability to estimate various chemical and physical properties of analyte vapors based on information contained in the response patterns of the detector array. A diverse array of composite chemiresistive vapor detectors was exposed to a series of 75 test analytes that had been selected from among five different chemical classes: alcohols, halogenated hydrocarbons, aromatics, unsubstituted hydrocarbons, and esters. The algorithmic task of interest was to use the resulting array of response data to assign one of the five chemical class labels to a test analyte, despite having left that analyte out of the model used to generate the class labels. Algorithms evaluated for this purpose included principal components analysis (PCA) and k-nearest neighbor (k-NN) analysis employing either Euclidean or Mahalanobis distance calculations. Each data cluster that was produced by replicate exposures to an individual analyte was well resolved from all of the other 74 analyte clusters. Furthermore, with the exception of the halide cluster, the analyte response clusters could be robustly grouped into supersets such that each of the five individual chemical classes was well-separated from every other class of analytes in principal component space. Accordingly, using either of the k-nearest neighbor algorithms, in excess of 85% of the non-halide test analyte exposures were correctly assigned to their chemical classes, and halides were only routinely confused with aromatics or esters but not with alcohols or hydrocarbons. The detector array response data also was found to contain semi-quantitative information regarding physicochemical properties of the members of the test analyte series, such as the degree of unsaturation of the carbon chain, the dipole moment, the molecular weight, the number of halogen atoms, and type of aromatic ring in the test analytes. The performance in these types of tasks is relevant for applications of a semi-selective array of vapor detectors in situations when no prior knowledge of the analyte identity is available and when there is no assurance that the test analyte will have been contained in the training set database produced by a compiling a library of responses from the detector array.

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