Abstract

Abstract One of the most complicated components of electronic olfaction process is odour handling and delivery system capable of enabling the associated sensors to perform with acceptable sensitivity. For smell monitoring of black tea, an array of metal oxide semiconductor (MOS) sensors has been used for assessment of volatiles in the experimental set-up. In the presence of detectable vapor, the conductivity of the sensor increases depending on the concentration of odour molecules in the vapor. But, the MOS sensors are highly sensitive to moisture and water vapor. Presence of water vapor in the headspace of any sample, therefore, produces strong sensor outputs, which are essentially noise. Such overriding effect of noise caused by water vapor plays catastrophic role in terms of efficient pattern recognition by parametric and non-parametric methods. This paper presents the details of a novel sampling system based on illumination-controlled heating together with physical raking of the tea samples developed for enhancement of sensitivity of MOS sensor array. This increase in sensor outputs enhances the precision of the measurement system significantly. The efficacy of the system has been validated by comparison of performance of the system in terms of correlating electronic nose data with tea tasters’ scores using probabilistic neural network (PNN).

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