On mappings between electronic noses

We consider the task of finding a mapping between two eNoses that employ two different sensor technologies, quartz microbalance and conducting polymers. Such a mapping is a model that predicts the response of one eNose based on the response of the other. eNose mappings are important for odor communication and synthesis, as well as for eNose data integration. We investigated a number of methods for performing this task, including principal components regression, partial least squares, neural networks and tessellation-based linear interpolation. Our measure of success is the percentage of predictions that are correctly classifiable. Using two different techniques for splitting our data set, we achieved success rates of 67% and 100%.