Desarrollo de diferentes métodos de selección de variables para sistemas multisensoriales

Identificador:  TDX:256

Handle: https://hdl.handle.net/20.500.11797/TDX256

Autors:  Gualdron Guerrero, Oscar Eduardo

Resum:
The electronic noses systems are instruments that have been developed to emulate olfactory biologic systems. These systems are known as electronic noses (EN).<br/>Nowadays, researchers and engineers working in this area are trying to optimize these systems considering different directions, such as: development of new gas sensors (with better discrimination and greater sensitivity), adaptation of analytical techniques such as mass spectrometry (MS) in substitution of chemical sensors matrix and extraction of new parameters of the sensors responses (pre-processing) or even development of sophisticated techniques for the data processing.<br/>One of the main disadvantages that have artificial olfactory systems is high dimensionality of sets to analyze. The main objective of this thesis have been study and development of new variable selection methods with the purpose of reducing dimensionality of data and thus to be able to optimize recognition processes in electronic olfactory systems based on gas sensors or mass spectrometry.<br/>These methods have been used with four datasets which belong to real applications.<br/>They allowed us to verify and to compare different implemented methods. These four datasets have been used in three studies whose conclusions are reviewed as follows.<br/>The first study has demonstrated that different methods (either deterministic or stochastic) can be coupled to a fuzzy ARTMAP or a PNN classifier and be used for variable selection in gas analysis problems by multisensor systems. The methods were applied to simultaneously identify and quantify three volatile organic compounds and their binary mixtures by building neural classification models.<br/>The second study, proposes a new strategy for feature selection in dataset of system olfactory based on mass spectrometry (MS). This strategy has been introduced and its good performance demonstrated using different MS e-nose databases. The strategy has been applied initially to a database consisting of synthetic mixtures of volatile compounds. This simple database has been used to show that the feature selection process is able to identify a minimal set of fragments that enables the correct discrimination between mixtures using a simple fuzzy ARTMAP classifier.<br/>Furthermore, given the simple nature of the problem envisaged, it was possible to show that the fragments selected 'made sense' were characteristic ionisation fragments of the species present in the mixtures which were discriminated. Once demonstrated the correct operation of this strategy, this methodology was applied to other two data sets (olive oil, Iberian ham).<br/>In the third study of this thesis has been introduced a new method of variable selection based on sequential backward selection. The method is specifically designed to work with Support vector machines (SVM) either for classification or regression. The usefulness of the method has been assessed using two multisensor system databases (measurements of vapour simples and vapour mixtures performed using an array of metal oxide gas sensors and measurement of Iberian ham).<br/>For different databases studied, dramatic decrease in dimensionality of model and an increase in classification performance is result of using variable selection. The methods introduced here are useful not only to solve MS-based electronic nose problems, but are of interest for any electronic nose application suffering from highdimensionality problems, no matter which sensing technology is used.