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Classification Performance of Carbon Black-polymer Composite Vapor Detector Arrays as a Function of Array Size and Detector Composition by Michael C Burl; Brian C Sisk; Thomas P Vaid; Nathan S Lewis is a Engineering article available to read on EtoBox.

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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 mxylene 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

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Author

Michael C Burl; Brian C Sisk; Thomas P Vaid; Nathan S Lewis

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0925-4005)

Published

2002

Language

EN

Field

Engineering (Physical Sciences)