Abstract
We have fabricated an electronic nose system using a thin film oxide semiconductor micro gas sensor array which shows only 65 mW of power consumption at an operating temperature of 300°C. Principal component analysis and neural network pattern recognition analysis were used to identify 12 gas samples (CH 3SH, (CH 3) 3N, C 2H 5OH and CO gases in the concentration range of 0.1–100 ppm) or six flavor samples (carrot, green onion, woman's perfume (eau de cologne), man's perfume (eau de toilette), 25% liquor (Korean soju) and 40% liquor (whisky)). Good separation among the gases with different concentrations or flavor samples was obtained using the principal component analysis. The recognition probability of the neural network was 100% for each of the 5 trials of 12 gas samples and 93% for each of 10 trials of 6 flavor samples.
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