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

Abstract

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 PCA, 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.