Non-Linear Source Separation Under the Langmuir Model for Chemical Sensors
Pierre Maho, Simon Barthelmé, Pierre Comon · 2018
Electronic nose is a promising bio-inspired instrument for the detection of Volatil Organic Compounds (VOCs), meaning a compound containing carbon which easily evaporates. One of the most important parts of these devices is a set of non-specific chemical sensors, which will interact with the VOC and output valuable information for its identification. The nonspecificity of these chemical sensors ensures the universality of the instrument. The main task achieved by this instrument is the detection of individual VOC. However, in many real-life applications, mixtures of VOCs are observed. The recovery of the mixture composition, meaning the individual signatures and their relative contribution, is a challenging task which can be studied in a Blind Source Separation framework. In this paper, we propose a non-linear mixture model for a particular type of chemical sensors. This model is based on the Langmuir isotherm for a multi-component gas. We study the joint identifiability of signatures and concentrations, and propose a necessary identification condition. Finally, we propose an algorithm for the blind estimation of the parameters and assess its performance through simulations.