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Quantitative Identification of Volatile Organics by SAW Sensor Transients ‒ Comparative Performance Analysis of Fuzzy Inference and Partial-Least-Square-Regression Methods

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Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 247))

Abstract

We present a comparative performance analysis between three methods (fuzzy c-means and fuzzy subtractive clustering based fuzzy inference systems and partial-least-square regression) for simultaneous determination of vapor identity and concentration in gas sensing applications. Taking poly-isobutylene coated surface acoustic wave sensor transients for measurements we analyzed simulated data for seven volatile organic compounds by applying these methods as a function of polymer film thickness. The sensor transients were represented by discrete wavelet approximation coefficients. It is found that PLS regression performs most optimally for both discrimination between vapor identities and simultaneous estimation of their concentration.

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Authors and Affiliations

  1. Department of Physics, College of Engineering, Teerthanker Mahaveer University, Moradabad, 244001, India

    Prashant Singh

  2. Sensors and Signal Processing Laboratory, Department of Physics, Faculty of Science, Banaras Hindu University, Varanasi, 221005, India

    R. D. S. Yadava

Authors
  1. Prashant Singh
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  2. R. D. S. Yadava
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Correspondence to Prashant Singh .

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Editors and Affiliations

  1. Dept. of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K Udgata

  3. Bhubaneswar Engineering College, Bhubaneswar, India

    Bhabendra Narayan Biswal

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Singh, P., Yadava, R.D.S. (2014). Quantitative Identification of Volatile Organics by SAW Sensor Transients ‒ Comparative Performance Analysis of Fuzzy Inference and Partial-Least-Square-Regression Methods. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-02931-3_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02930-6

  • Online ISBN: 978-3-319-02931-3

  • eBook Packages: EngineeringEngineering (R0)

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