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Sensors 2014, 14(9), 17786-17806;

An Efficient Approach for Preprocessing Data from a Large-Scale Chemical Sensor Array

National Research Council of Italy, Institute of Optics, via della Libertà 3 Arnesano (Lecce), 73010, Italy
School of Chemical Engineering and Analytical Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
Author to whom correspondence should be addressed.
Received: 8 July 2014 / Revised: 8 September 2014 / Accepted: 15 September 2014 / Published: 24 September 2014
(This article belongs to the Section Chemical Sensors)
Full-Text   |   PDF [567 KB, uploaded 24 September 2014]


In this paper, an artificial olfactory system (Electronic Nose) that mimics thebiological olfactory system is introduced. The device consists of a Large-Scale ChemicalSensor Array (16; 384 sensors, made of 24 different kinds of conducting polymer materials)that supplies data to software modules, which perform advanced data processing. Inparticular, the paper concentrates on the software components consisting, at first, of acrucial step that normalizes the heterogeneous sensor data and reduces their inherent noise.Cleaned data are then supplied as input to a data reduction procedure that extracts the mostinformative and discriminant directions in order to get an efficient representation in a lowerdimensional space where it is possible to more easily find a robust mapping between theobserved outputs and the characteristics of the odors in input to the device. Experimentalqualitative proofs of the validity of the procedure are given by analyzing data acquired fortwo different pure analytes and their binary mixtures. Moreover, a classification task isperformed in order to explore the possibility of automatically recognizing pure compoundsand to predict binary mixture concentrations. View Full-Text
Keywords: large scale chemical sensor; data reduction; EigenOdour; pattern recognition large scale chemical sensor; data reduction; EigenOdour; pattern recognition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Leo, M.; Distante, C.; Bernabei, M.; Persaud, K. An Efficient Approach for Preprocessing Data from a Large-Scale Chemical Sensor Array. Sensors 2014, 14, 17786-17806.

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