The human-based multisensor fusion method for artificial nose and tongue sensor data

Presently, an increased interest is apparent for the development of integrated human-like smell and taste sensing capabilities, e.g. for chemical, paper pulp, food, and medicine applications. This paper will present an original sensor fusion method based on human expert opinions about smell and taste and measurement data from artificial nose and taste sensors. The “electronic nose” consists of an array of gas sensors with different selectivity patterns, signal handling, and a sensor signal pattern recognition and decision strategy. The “electronic tongue”, which was developed for the taste analysis of liquids is based on pulse voltammetry. Measurement data from the artificial smell and taste sensors are used to produce sensor-specific opinions about these two human-like sensing modalities. This is achieved by a team of artificial neural networks and conventional signal handling which approximates a Bayesian decision strategy for classifying the sensor information. Further, a fusion algorithm based on the maximum likelihood principle provides a combination of the smell and, respectively, taste opinions, into an overall integrated opinion similar to human beings. The proposed integrated smell- and taste-sensing method is then illustrated by an application of real world measurements in the food industry