Data-driven modeling of nano-nose gas sensor arrays

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

    Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

    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 publicationSignal Processing, Sensor Fusion, and Target Recognition Xix
    Place of PublicationBellingham
    PublisherSPIE - International Society for Optical Engineering
    Publication date2010
    Publication statusPublished - 2010
    SeriesProceedings of SPIE, the International Society for Optical Engineering
    Volume7697
    ISSN0277-786X

    Keywords

    • Concentration Level Estimation
    • Gaussian Process Regression (GPR)
    • Classification
    • Principal Component Analysis (PCA)
    • Polymer Coated Quartz Crystal Microbalance Sensor (QCM)
    • Non-negative Matrix Factorization (NMF)
    • Principal Component Regression (PCR)
    • Artificial Neural Network (ANN)

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