Data–driven modeling of nano-nose gas sensor arrays

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

AbstractWe 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.
KeywordsPolymer Coated Quartz Crystal Microbalance Sensor (QCM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), Non–negative Matrix Factorization (NMF), Principal Component Analysis (PCA), Principal Component Regression (PCR)
TypeConference paper [With referee]
ConferenceSignal Processing, Sensor Fusion, and Target Recognition XIX
EditorsIvan Kadar
Year2010    Month April    Vol. 7697    No. 1    pp. 76970U
PublisherThe International Society for Optical Engineering
NoteCopyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited
Electronic version(s)[pdf]
Publication linkhttp://dx.doi.org/10.1117/12.850314
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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