Data–driven modeling of nano-nose gas sensor arrays

Tommy Sonne Alstrøm, Jan Larsen, Claus Højgård Nielsen, Niels Bent Larsen

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Abstract

We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state-of-the-art machine learning methods and the Bayesian learning paradigm.
Original languageEnglish
Title of host publicationSPIE : Defense, Security and Sensing
Volume7697
Publication date2010
Publication statusPublished - 2010
EventSPIE : Defense, Security and Sensing -
Duration: 1 Jan 2010 → …
Conference number: 7697-30

Conference

ConferenceSPIE : Defense, Security and Sensing
Number7697-30
Period01/01/2010 → …

Keywords

  • Principal Component Analysis (PCA)
  • Non–negative Matrix Factorization (NMF)
  • Polymer Coated Quartz Crystal Microbalance Sensor (QCM)
  • Principal Component Regression (PCR)
  • aussian Process Regression (GPR)
  • Artificial Neural Network (ANN)

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