Electronic nose algorithm design using classical system identification for odour intensity detection

Abstract

The two elements considered crucial for constructing an efficient environmental odour intensity monitoring systems are sensors and algorithms typically addressed to as electronic nose sensor (e-nose). Due to operational complexity of biochemical sensors developed in human bodies algorithms based on computational methods of artificial intelligence are typically considered superior to classical model based approaches in development of e-nose algorithms. However, in this work authors proposed an approach to derive an algorithm for e-nose using a classical approach kept in model identification framework. The benefits of the proposed solution, apart of the structural correctness of the derived algorithm model, include improved generalisation capabilities in case of low training data volume is available. To that goal the algorithm structure is derived based on available knowledge on human senses reaction to odorants. Due to the algorithm structure a random search algorithm with heuristics (evolutionary algorithm) is used to search for the required parameters of the electronic nose e-nose to be able to explain the laboratory experiment data. The evolutionary algorithm is kept in a multi-objective optimisation framework. As such, two heuristic decision making mechanisms have been proposed to select parameters for the algorithm under development. A comparison of the resulting algorithm with the one developed based on artificial neural networks (ANNs) is provided.