An electronic nose system for monitoring the quality of potable water

Abstract

A measurement system has been developed for the testing of cyanobacteria in water, and it consists of three main stages: the odour sampling system, an electronic nose (e-nose) and a CellFacts instrument that analyses liquid samples. The e-nose system, which employs an array of six commercial odour sensors, has been used to monitor not only different strains but also the growth phase of cyanobacteria (i.e. blue-green algae) in water over a 40-day period. Principal components analysis (PCA), multi-layer perceptron (MLP), learning vector quantisation (LVQ) and Fuzzy ARTMAP were used to analyse the response of the sensors. The optimal MLP network was found to classify correctly 97.1% of the unknown nontoxic and 100% of the unknown toxic cyanobacteria. The optimal LVQ and Fuzzy ARTMAP algorithms were able to classify 100% of both strains of cyanobacteria samples. The accuracy of MLP, LVQ and Fuzzy ARTMAP in terms of predicting four different growth phases of toxic cyanobacteria was 92.3%, 95.1% and 92.3%, respectively. These results show the potential application of neural network based e-noses to test the quality of potable water as an alternative to instruments, such as liquid chromatography or optical microscopy.