Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions

Intensive research and fast developments in electronic nose (EN) technologies provide the users with a wide spectrum of sensors and systems for their applications. This paper presents some of the results obtained with four different ENs on a series of collaborative tests carried out on six standard fruit samples, pure liquids and mixtures. These experiments, part of the EU ASTEQ concerted action, were designed for inter-comparison of the system's performances. Various feature extraction techniques are considered along with inter-comparison of the individual results obtained with radial basis function (RBF) and probabilistic neural networks (PNN). A low-level data fusion technique is used to merge the various datasets together, considering all extracted parameters in order to increase the amount of information available for classification. We achieve 86.7% correct classification with the fusion system, which outperforms the results obtained with individual ENs. With this fusion array, a problem of dimensionality occurs and it is possible to find an optimal array configuration of reduced dimensionality considering a subset of parameters. We report on various parameter selection methods: principal component analysis (PCA) as a mathematical transformation and two types of genetic algorithms (GAs) optimisation as search methods. Various subsets of parameters are selected and all techniques return improved classification rates, 80% with PICA, 96.7% with 6-integer gene GAs and 93.3% with 72-binary gene GAs. In order to overcome cost and technology limitations, optimisation techniques can be used to create application specific arrays selecting the best sensors or the correct parameters. (C) 2002 Elsevier Science B.V. All rights reserved.