@inbook{3ea44818e1a347b39040a96907a60c5e,
title = "Data-driven modeling of nano-nose gas sensor arrays",
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.",
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)",
author = "Alstr{\o}m, {Tommy Sonne} and Jan Larsen and Nielsen, {Claus H{\o}jg{\aa}rd} and Larsen, {Niels Bent}",
year = "2010",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE - International Society for Optical Engineering",
booktitle = "Signal Processing, Sensor Fusion, and Target Recognition Xix",
}