Comparison of analytical methods and calibration methods for correction of detector response drift in arrays of carbon black-polymer composite vapor detectors

Brian C. Sisk, Nathan S Lewis

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

The responses of 15 carbon black-polymer composite chemiresistors have been analyzed during exposure to eight different analytes (n-hexane, tetrahydrofuran, ethanol, ethyl acetate, cyclohexane, n-heptane, n-octane, and isooctane) in random order at low concentration (0.5% of the vapor pressure of analyte at room temperature) over 4 months (8000 total analyte exposures) of data collection. Data were collected for periods during which the array was continuously exposed periodically to analytes and after long periods during which no analyte exposures had been performed. All but the most difficult separation tasks (for example, discrimination between low concentrations of straight-chain hydrocarbons) could be performed robustly over the entire 4 month time period based only on the use of a decision boundary formulated from an initial training set of 200 exposures, indicating the sensor drift had minimal effect on system performance in such classification tasks. For the remaining classification tasks, modeling the dynamics of sensor drift either through a linear regression or Fourier transform decomposition of the individual relative differential resistance responses versus time of each sensor yielded little improvement in classification performance, indicating that external events were largely responsible for changes in sensor response versus time. Six analytes that were not treated as unknowns for a binary separation task were individually treated as calibrants whose response was intermittantly used to renormalize the response of the sensor array. A simple linear sensor-by-sensor calibration scheme proved effective at restoring the classification performance of difficult binary separation tasks to the performance that was observed in the initial training set period. Calibrants that were mutually similar to the analytes being differentiated tended to be more effective than calibrants that were very chemically different from the analytes of interest. Evaluation of various calibration protocols indicated that an optimal tradeoff existed between the number of calibration exposures and the frequency of calibration periods. Condition-based calibration, in which calibration was only performed when the classification model exhibited a decline in classification performance below a predetermined threshold value, was observed to be superior to a time-based calibration approach or to interval-based, cyclic calibration protocols for this set of analytes exposed under the chosen analysis conditions.

Original languageEnglish
Pages (from-to)249-268
Number of pages20
JournalSensors and Actuators, B: Chemical
Volume104
Issue number2
DOIs
Publication statusPublished - Jan 24 2005

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Soot
Carbon black
Polymers
Vapors
Calibration
vapors
Detectors
composite materials
carbon
sensors
detectors
Composite materials
polymers
Sensors
low concentrations
education
octanes
tradeoffs
heptanes
tetrahydrofuran

Keywords

  • Calibration
  • Carbon black-polymer composite vapor sensors
  • Drift
  • Sensor arrays

ASJC Scopus subject areas

  • Analytical Chemistry
  • Electrochemistry
  • Electrical and Electronic Engineering

Cite this

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title = "Comparison of analytical methods and calibration methods for correction of detector response drift in arrays of carbon black-polymer composite vapor detectors",
abstract = "The responses of 15 carbon black-polymer composite chemiresistors have been analyzed during exposure to eight different analytes (n-hexane, tetrahydrofuran, ethanol, ethyl acetate, cyclohexane, n-heptane, n-octane, and isooctane) in random order at low concentration (0.5{\%} of the vapor pressure of analyte at room temperature) over 4 months (8000 total analyte exposures) of data collection. Data were collected for periods during which the array was continuously exposed periodically to analytes and after long periods during which no analyte exposures had been performed. All but the most difficult separation tasks (for example, discrimination between low concentrations of straight-chain hydrocarbons) could be performed robustly over the entire 4 month time period based only on the use of a decision boundary formulated from an initial training set of 200 exposures, indicating the sensor drift had minimal effect on system performance in such classification tasks. For the remaining classification tasks, modeling the dynamics of sensor drift either through a linear regression or Fourier transform decomposition of the individual relative differential resistance responses versus time of each sensor yielded little improvement in classification performance, indicating that external events were largely responsible for changes in sensor response versus time. Six analytes that were not treated as unknowns for a binary separation task were individually treated as calibrants whose response was intermittantly used to renormalize the response of the sensor array. A simple linear sensor-by-sensor calibration scheme proved effective at restoring the classification performance of difficult binary separation tasks to the performance that was observed in the initial training set period. Calibrants that were mutually similar to the analytes being differentiated tended to be more effective than calibrants that were very chemically different from the analytes of interest. Evaluation of various calibration protocols indicated that an optimal tradeoff existed between the number of calibration exposures and the frequency of calibration periods. Condition-based calibration, in which calibration was only performed when the classification model exhibited a decline in classification performance below a predetermined threshold value, was observed to be superior to a time-based calibration approach or to interval-based, cyclic calibration protocols for this set of analytes exposed under the chosen analysis conditions.",
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AB - The responses of 15 carbon black-polymer composite chemiresistors have been analyzed during exposure to eight different analytes (n-hexane, tetrahydrofuran, ethanol, ethyl acetate, cyclohexane, n-heptane, n-octane, and isooctane) in random order at low concentration (0.5% of the vapor pressure of analyte at room temperature) over 4 months (8000 total analyte exposures) of data collection. Data were collected for periods during which the array was continuously exposed periodically to analytes and after long periods during which no analyte exposures had been performed. All but the most difficult separation tasks (for example, discrimination between low concentrations of straight-chain hydrocarbons) could be performed robustly over the entire 4 month time period based only on the use of a decision boundary formulated from an initial training set of 200 exposures, indicating the sensor drift had minimal effect on system performance in such classification tasks. For the remaining classification tasks, modeling the dynamics of sensor drift either through a linear regression or Fourier transform decomposition of the individual relative differential resistance responses versus time of each sensor yielded little improvement in classification performance, indicating that external events were largely responsible for changes in sensor response versus time. Six analytes that were not treated as unknowns for a binary separation task were individually treated as calibrants whose response was intermittantly used to renormalize the response of the sensor array. A simple linear sensor-by-sensor calibration scheme proved effective at restoring the classification performance of difficult binary separation tasks to the performance that was observed in the initial training set period. Calibrants that were mutually similar to the analytes being differentiated tended to be more effective than calibrants that were very chemically different from the analytes of interest. Evaluation of various calibration protocols indicated that an optimal tradeoff existed between the number of calibration exposures and the frequency of calibration periods. Condition-based calibration, in which calibration was only performed when the classification model exhibited a decline in classification performance below a predetermined threshold value, was observed to be superior to a time-based calibration approach or to interval-based, cyclic calibration protocols for this set of analytes exposed under the chosen analysis conditions.

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