Un réseau de senseurs pour identifier des mélanges complexes de COVs dans l'haleine humaine

Lung cancer is one of the deadliest form of cancer in Europe, being the first and second cause of cancer death respectively for men and women. This high death toll has to be blamed on the lack of obvious symptoms in the early stages of the illness. Current diagnostic methods tend make the screening costly and difficult to organise at a large scale. Asymptomatic subjects and people in remote areas are rarely tested overall, leading to late discovery of the cancer and poor survival chances. There is therefore a need for a diagnostic method that could be used remotely while being simple enough to be used with little prior formation. Gas sensor arrays have properties fitting for the task. This thesis aims at creating and testing a sensor array in order to build a benchmark on which one can compare the discriminative power of different arrays. Several tasks will be performed simultaneously: The first is the establishment of a standardized test method of the metrological characteristics of commercial thick film sensors as well as experimental ones, and their qualities within a sensor network. The second is the integration of experimental sensors into a prototype gas sensor array consistent with the final purpose of the device. The third is the validation of the test method with the prototype electronic nose, which requires the reproducible synthesis of reference gas mixtures. It is also planned to use real breath from healthy persons and cancer patients as validation of the benchmark’s conclusions. The last task is about the processing and analysis of data and the identification and classification of samples in order to obtain a measurement of the array’s discriminatory power. This thesis is part of the PATHACOV research project, funded by Interreg France-Wallonie-Vlaanderen.Pathaco