Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array

https://doi.org/10.1016/S0925-4005(97)80272-9Get rights and content

Abstract

Quantitative analysis of gases, by means of semiconductor sensor arrays and pattern-recognition techniques such as artificial neural networks, has been the goal of a great deal of work over the last few years. However, the lack of selectivity, repeatability and drifts of the sensors, have limited the applications of these systems to qualitative or semi-quantitative gas analysis. While the steady-state response of the sensors is usually the signal to be processed in such analysis systems, our method consists of processing both, transient and steady-state information. The sensor transient behaviour is characterised through the measure of its conductance rise time (Tr), when there is a step change in the gas concentration. Tr is characteristic of each gas/sensor pair, concentration-independent and shows higher repeatability than the steady state measurements. An array of four thick-film tin oxide gas sensors and pattern-recognition techniques are used to discriminate and quantify among ethanol, toluene and o-xylene [concentration range: 25, 50 and 100 ppm]. A principal component analysis is carried out to show qualitatively that selectivity improves when the sensor behaviour is dynamically characterised. The steady-state and transient conductance of the array components are processed with artificial neural networks. In a first stage, a feed-forward back-propagation-trained ANN discriminates among the studied compounds. Afterwards, three separate ANN (one for each vapour) are used to quantify the previously identified compound. Processing data from the dynamic characterisation of the sensor array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state signals are used up to 100%.

References (27)

  • J.W. Cooper

    Spectroscopic Techniques for Organic Chemists

    (1980)
  • P.T. Moseley et al.

    Techniques and Mechanisms in Gas Sensing

    (1991)
  • Cited by (170)

    • Acceleration and drift reduction of MOX gas sensors using active sigma-delta controls based on dielectric excitation

      2022, Sensors and Actuators B: Chemical
      Citation Excerpt :

      Despite the advantages, these sensors may present a lack of selectivity, large response times and long-term drifts [8]. Dynamic models have been proposed to describe the transient response using nonlinear methods [9,10]. Some of these works use neural networks [11,12], PCA separation [13,14], reservoir computing [15], support vector machines [16] or instabilities corrections [17].

    • Nanotechnology-based E-nose for smart manufacturing

      2021, Nanosensors for Smart Manufacturing
    View all citing articles on Scopus
    View full text