A hierarchical classification scheme for an Electronic Nose

A procedure for the classification of data from an Electronic Nose (EN) is proposed, which is beneficial in the case in which the number of classes is big and/or the classes are not nicely clustered (for instance, as seen in a PCA score plot). The procedure consists of separating the original classification problem in successive, less demanding sub-classification tasks. The advantages, which are due to the greater flexibility, include the following: smaller processing times, enhanced performances and better interpretation of the results. Each classification step uses PCA and Multilayer Perceptrons (MLP) in cascade and, for comparison, Simca. The method has been tested on a data set formed by 242 measurements of 14 olive oil types performed with a commercial EN that was equipped with 12 MOS sensors. (C) 2000 Elsevier Science S.A. All rights reserved.