Sobre a capacidade de representação de um nariz eletrônico composto por dois sensores do tipo óxido metálico

Abstract

The use of electronic noses for the analysis of odor signals has been increasing over the last decades. From applications in food discrimination and quality control to medical applications, many studies have used these devices and studied the characteristics associated with the sensors used in their construction. Metal oxide (MOX) sensors are one of the most widely used, but they still have disadvantages that continue to be studied and compensated for in some way, such as their low selectivity. In this context, this work aimed to propose a quantifiable way, based on the knowledge of information theory, to compare the capacity of spaces generated by simultaneous responses of MOX sensors to represent, with an arbitrary low classification error, different combinations of gas mixtures present in an environment with an arbitrarily low error. The estimation of this proposed quantity is demonstrated for simulated data from an array of two different MOX sensors based on a commercial sensor operating at two different temperature modulation levels, in which a representation capacity of a few dozen classes for the signal space was observed, when measurements were analyzed in a steady state without drift effect, under moderate noise levels. It is believed that this proposed quantity may be useful to further understand and compare these devices that constitute electronic noses to establish theoretical limits regarding their use in classifiers.