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Contribution au développement d'un dispositif robuste de détection-diffusion d'huiles essentielles à concentration contrôlée

Abstract : control This work contributes to the design of a gas diffusion-sensing system controlling in real time the essential oil concentration in a confined atmosphere. The objective is to create reproducible exposure conditions of olfactory stimuli on living beings to test their neurosensory impacts. The main constraint is to measure with good accuracy and rapidity the odor concentration of a global atmosphere. We decided to use a gaseous detection device (electronic nose) based on commercial resistive metal oxide sensors coupled to a prior learning at fixed concentrations of pine essential oil. Experimental equipment was first developed in order to study, characterize and especially optimize the device performances to be achieved. Initially, the study of time gas sensor responses was used to optimize working measurement conditions: cycle of 75s gas exposure phase, followed by 350s pure air regeneration phase. First results allowed the classification of our sensors in terms of rapidity, sensitivity and drift levels. A systematic characterization measurement was made under various concentration variations: increasing, decreasing or random ones taking account of all possible forms of response drifts. To reduce errors due to the drifts, an original pretreatment was initiated by normalizing each sensor response value in relation with its corresponding conductance value at the end of regeneration phase. Two normalized features and also the maximum value of the derivative curve were defined for each time sensor response. The analysis by ACP and AFD classification methods of the database created using these three features show the difficulty in differentiating high concentrations, even by eliminating the two least efficient sensors. So, a completely new approach was proposed by combining the orthogonal signal correction technique (OSC) allowing to remove irrelevant information, and the Partial Linear Square regression method PLS, adapted in case of multi-collinearity and a large number of parameters. Using these two methods yields a much better discrimination of the high concentrations, maintaining the concentration prediction accuracy with a maximum stability of the regression model. Finally, the concentration prediction has been optimized by substituting representative parameters with the full response signal, the calculation time remaining low. A very good assessment of the gas concentration in all the used range was obtained. So we have developed a robust and accurate model for the calibration of our system thanks to a combination of original processing and analysis methods, allowing to achieve a reliable detection-diffusion prototype
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Submitted on : Thursday, March 29, 2018 - 1:33:48 PM
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  • HAL Id : tel-01752183, version 1



Mohamed Diaa Ahmadou. Contribution au développement d'un dispositif robuste de détection-diffusion d'huiles essentielles à concentration contrôlée. Autre. Université de Lorraine, 2015. Français. ⟨NNT : 2015LORR0187⟩. ⟨tel-01752183⟩



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