A high precise E-nose for daily indoor air quality monitoring in living environment

Abstract E-nose, whose major components include a sensor array and a pattern recognition algorithm, is considered to be a potential way to balance the trade-off between cost and accuracy for daily indoor air quality monitoring in living environment. In this paper, we presented a high precise E-nose for such application. QS-01 from FIS, TGS2600 and TGS2602 from FIGARO, temperature and humidity sensor SHT10 are selected to compose the sensor array. Back Propagation (BP) nueral network, the typical machine learning algorithm is used to be the pattern recognition algorithm of the E-nose. The performance comparison between the proposed E-nose and other E-nose solutions shows the improvement.

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