Computational-based volatile organic compounds discrimination: An experimental low-cost setup

Abstract

In this work, an array of low-cost cross-sensitive sensors is used for discriminating the best candidate within a set of volatile organic compounds (VOCs). The challenge of our experimental setting is to deal with the problems of low selectivity, especially in normal operating conditions, so that ambiguous sensor responses (i.e. referable to more than one VOC) can be given, at least, a qualitative interpretation. In order to carry out the signal disambiguation task, a computational technique employing simple classifying rules and fuzzy descriptions has been engineered. The basic idea is that, if the same gas is actually measured by two or more sensors, then the estimated concentrations will show a low variance, with an accuracy related to the number of concordant sensors. Experiments show that, despite the cheapness of the setup and the coarse-grained nature of the provided response, encouraging results can be obtained and prospective work can follow.