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Several indoor chemical contaminants such as CO and NO 2 are highly toxic. Inhalation of CO or NO 2 as low as ppm level may cause respiratory distress or failure. Therefore, detection of indoor air is very important in the industrial, medical, and environmental applications. In this paper, a new electronic nose (E-nose) architecture has been proposed for the real-time quantification and qualification of indoor air contaminations. The metal oxide TGS gas sensors were used as the sensing part. The principal component analysis (PCA) method and a set of mathematical model were employed in data analysis. By combining with the proposed mathematical model, this E-nose can estimate the amount of CO gas contaminations in air at ppm levels. Moreover, the PCA results can clearly show a classification between two different rooms.
Wongchoosuk et al. (Tue,) studied this question.