Classification performance of carbon black-polymer composite vapor detector arrays as a function of array size and detector composition

Michael C. Burl, Brian C. Sisk, Thomas P. Vaid, Nathan S. Lewis

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

The vapor classification performance of arrays of conducting polymer composite vapor detectors has been evaluated as a function of the number and type of detectors in an array. Quantitative performance comparisons were facilitated by challenging a collection of detector arrays with vapor discrimination tasks that were sufficiently difficult that at least some of the arrays did not exhibit perfect classification ability for all of the tasks of interest. Specific discrimination tasks involved differentiating between low concentration (<1% of the vapor pressure) exposures to 1-propanol versus 2-propanol, low concentration exposures to n-hexane versus n-heptane, and differentiating between compositionally similar mixtures of closely related analytes, such as 9.37 ppm m-xylene with 10.2 ppm p-xylene versus 7.67 ppm m-xylene with 12.4 ppm p-xylene. A decision boundary was developed using a cross-validated Fisher linear discriminant algorithm on a training set of analyte presentations and the resulting chemometric model was then used to classify a subsequent collection of test analyte presentations to the array being evaluated. In other cases, classification performance was evaluated using the Fisher linear discriminant and a leave-one-out (LOO) cross-validation procedure. For nearly all of the discrimination tasks investigated in this work, classification performance either increased or did not significantly decrease as the number of chemically different detectors in the array increased. Any given subset of the full array of detectors, selected because it yielded the best classification performance at a given array size for one particular task, was invariably outperformed by a different subset of detectors, and by the entire array of 20 chemically diverse detectors when used in at least one other vapor discrimination task. Arrays of detectors were nevertheless identified that yielded robust discrimination performance between compositionally close mixtures of 1-propanol and 2-propanol, n-hexane and n-heptane, and m-xylene and p-xylene, attesting to the excellent analyte classification performance that can be obtained through the use of such semi-selective vapor detector arrays.

Original languageEnglish
Pages (from-to)130-149
Number of pages20
JournalSensors and Actuators, B: Chemical
Volume87
Issue number1
DOIs
Publication statusPublished - Nov 15 2002

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Keywords

  • Array size
  • Sensor arrays
  • Vapor detection

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering
  • Materials Chemistry

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