title: Data set from chemical sensor array exposed to turbulent gas mixtures.
abstract: A chemical detection platform composed of 8 chemo-resistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided. The experimental setup was designed to test gas sensors in realistic environments. Traditionally, chemical detection systems based on chemo-resistive sensors include a gas chamber to control the sample air flow and minimize turbulence. Instead, we utilized a wind tunnel with two independent gas sources that generate two gas plumes. The plumes get naturally mixed along a turbulent flow and reproduce the gas concentration fluctuations observed in natural environments. Hence, the gas sensors can capture the spatio-temporal information contained in the gas plumes. The sensor array was exposed to binary mixtures of ethylene with either methane or carbon monoxide. Volatiles were released at four different rates to induce different concentration levels in the vicinity of the sensor array. Each configuration was repeated 6 times, for a total of 180 measurements. The data is related to "Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry", by Fonollosa et al. [1]. The dataset can be accessed publicly at the UCI repository upon citation of [1]: http://archive.ics.uci.edu/ml/datasets/Gas+senso+rarray+exposed+to+turbulent+gas+mixtures.
authors.affiliation: University of California, San Diego, USA.
authors.forename: Jordi
authors.initials: J
authors.lastname: Fonollosa
authors.affiliation: University of California, San Diego, USA.
authors.forename: Irene
authors.initials: I
authors.lastname: Rodríguez-Luján
authors.affiliation: University of California, San Diego, USA.
authors.forename: Marco
authors.initials: M
authors.lastname: Trincavelli
authors.affiliation: University of California, San Diego, USA.
authors.forename: Ramón
authors.initials: R
authors.lastname: Huerta
keywords: Chemical Sensing; Chemometrics; Electronic nose; Machine learning; Machine olfaction