title: Chemical gas sensor array dataset.
abstract: To address drift in chemical sensing, an extensive dataset was collected over a period of three years. An array of 16 metal-oxide gas sensors was exposed to six different volatile organic compounds at different concentration levels under tightly-controlled operating conditions. Moreover, the generated dataset is suitable to tackle a variety of challenges in chemical sensing such as sensor drift, sensor failure or system calibration. The data is related to "Chemical gas sensor drift compensation using classifier ensembles", by Vergara et al. [1], and "On the calibration of sensor arrays for pattern recognition using the minimal number of experiments", by Rodriguez-Lujan et al. [2] The dataset can be accessed publicly at the UCI repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations.
authors.affiliation: BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA.
authors.forename: Jordi
authors.initials: J
authors.lastname: Fonollosa
authors.affiliation: BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA.
authors.forename: Irene
authors.initials: I
authors.lastname: Rodríguez-Luján
authors.affiliation: BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA.
authors.forename: Ramón
authors.initials: R
authors.lastname: Huerta
keywords: Chemical sensing; Chemometrics; Electronic nose; Machine learning; Machine olfaction