Classification of the strain and growth phase of cyanobacteria in potable water using an electronic nose system

Abstract

An electronic nose comprising an array of six commercial odour sensors has been used to monitor not only different strains, but also the growth phase, of cyanobacteria which is normally called blue green algal. A series of experiments were carried out to analyse the nature of two closely related strains of cyanobacteria, Microcystis aeruginosa PCC 7806 that produces a toxin and PCC 7941 that does not. The authors have constructed a measurement system for the testing of the cyanobacteria in water over a period of up to 40 days. After some pre-processing to remove the variation associated with running the electronic nose in ambient air, the two different strains, and their growth phase, with principal component, were classified analysis, multilayer perceptron (MLP), learning vector quantisation (LVQ), and fuzzy ARTMAP. The optimal MLP network was found to classify correctly 97.1% of unknown non-toxic and 100% of unkhown toxic cyanobacteria. The optimal LVQ and fuzzy ARTMAP algorithms were able to classify 100% of both strains of cyanobacteria. The accuracy of MLP, LVQ and fuzzy ARTMAP algorithms with the four different growth phases of toxic cyanobacteria was 92.3%, 95.1% and 92.3%, respectively.