Hyun Woo Shin

An electronic nose system to diagnose illness

Recently, medical diagnostics has emerged to be a promising application area for electronic noses (e-nose). In this paper, we review work carried out at Warwick University on the use of an e-nose to diagnose illness. Specifically, we have applied an e-nose to the identification of pathogens from cultures and diagnosing illness from breath samples. These initial results suggest that an e-nose will be able to assist in the diagnosis of diseases in the near future.

An electronic nose system for monitoring the quality of potable water

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.

Gas Identification Using Oxide Semiconductor Gas Sensor Array and Neural-network Pattern Recognition

In order to identify CH/sub 3/SH, (CH/sub 3/)/sub 3/, C/sub 2/H/sub 5/OH and CO gases in the concentration range of 0.1 to 100 ppm, a gas recognition system using a gas sensor array and neural network pattern recognition has been fabricated. The sensor array consists of such thin film oxide semiconductor sensing materials as 1 wt.% Pd-doped SnO/sub 2/, 6 wt.% AI/sub 2/O/sub 3/-doped ZnO, WO/sub 3/ and ZnO. The principal component analysis and the neural-network pattern recognition analysis were used for the discrimination of gas species and concentrations. Good separation among gases and concentrations was obtained using the principal component analysis. The recognition probability of the neural-network was 100% for each five-trial of twelve gas samples.