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 journalConference articlepeer-review


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. 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, 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 meta-xylene and para-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)520-531
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue numberII
Publication statusPublished - 2002
EventProceedings of the 2002 Detection and Remediation Technologies for Mines and Minelike Targets IV - Orlando, FL, United States
Duration: Apr 1 2002Apr 5 2002


  • Detector array size
  • Vapor detectors

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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