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

Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

SAW sensor transients quantitative recognition discrete wavelet decomposition fuzzy inference system partial-least-square regression 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Physics, College of EngineeringTeerthanker Mahaveer UniversityMoradabadIndia
  2. 2.Sensors and Signal Processing Laboratory, Department of Physics, Faculty of ScienceBanaras Hindu UniversityVaranasiIndia

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