Thesis (M.S.)--Oregon State University, 1992. / Typescript (photocopy). Includes bibliographical references (leaves 152-157). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 235-242). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 235-242). Also available on the Internet.

Thesis (M.S.)--University of Wisconsin--Madison, 1970. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

The need for sensitive and selective detectors in supercritical fluid chromatography (SFC) is particularly evident since SFC can be used to analyze classes of compounds that are not readily amenable to either gas chromatography (GC) or liquid chromatography (LC). These compounds include species that are nonvolatile or thermally labile and , in addition, contain no chromophore that can be used for spectrophoto detection. The objective of this research is therefore to interface selective chemilumninescent detectors with SFC in the sensitive detection of nitrogen- and/or sulfur containing compounds. The chemiluminecent nitrogen detector (CLND), a gas-phase detector which is specific for nitrogen-containing compounds, was first evaluated as a detector for use with capillary SFC. The potential use of the CLND for food flavor and petroleum samples was demonstrated. In addition to equimolar nitrogen response, the CLND showed good sensitivity and large linear dynamic range. Minimum detectable quantity (MDQ) was 60 pg of nitrogen with a linear range of over 3 orders of magnitude. Nitrogen to carbon selectivity of 105 was obtained. Capillary SFC with simultaneous flame ionization and chemiluminescent detection was also demonstrated. The second portion of the research investigated the CLND for packed column SFC with methanol modified CO2. The only modification made in the CLND for packed column SFC is the pyrolysis furnace. The CLND and UV were used to interface with SFC via a post-column split. Methanol-modified CO2 was also demonstrated to be compatible with the CLND even with a high mobile phase flow rate. The use of pressure and modifier programs appears to be feasible as is evidenced by the baseline studies which have been performed, as well as by the applications demonstrated. The last portion of the research focused on the evaluation of a new generation sulfur chemiluminescent detector (SCLD), which is also a gas-phase detector, with packed column SFC using both pure and methanol modified CO2. The minimum detectable quantities were determined to be 2.6 pg or 14 pg sulfur for mobile phase employing pure CO2 or 8% methanol modified CO2 respectively. The evaluation study also showed excellent selectivity and linearity, as well as day-to-day repeatability. The capabilities of the SFC-SCLD system for sulfur speciation and detection of thermally labile pesticides and polar sulfonamides, as well as petrochemical samples were presented. / Ph. D.

Parmi les dimensions sensorielles engagées dans la perception de la flaveur, la composante odorante est déterminante car elle porte le plus souvent l’identité et la typicité d’un aliment. L’analyse chimique de la composante odorante repose sur une stratégie séparative qui permet d’identifier les différents odorants présents dans l’aliment. Cependant, la perception des odorants en mélange induit des interactions au niveau perceptif qui ne sont pas prises en compte dans les techniques séparatives. Les mécanismes sous-jacents aux interactions perceptives sont mal connus, ce qui limite les possibilités de prédiction de l’odeur d’un aliment sur la base de sa composition chimique. En réponse à cette problématique deux approches émergent de la revue de la littérature. La première est basée sur la prédiction d’odeur d’après la structure moléculaire des odorants. Cependant, les études concernent des odorants seuls et non leurs mélanges. La seconde repose sur la recombinaison d’odorants en mélange après l’étape d’analyse séparative, mais le choix des odorants à associer est essentiellement empirique. Ainsi, deux questions se posent : Comment prédire l'odeur de mélanges de molécules d’après la structure moléculaire des odorants? Comment améliorer l'analyse de la flaveur dans le but de prédire l'odeur d’aliments complexes composés de plusieurs dizaine d’odorants en mélanges? Ces deux questions ont été abordées dans cette thèse dont les travaux sont décrits dans ce manuscrit selon deux axes principaux.Le premier axe décrit l'utilisation et le développement d’un modèle basé sur le concept des distances angulaires calculées à partir de la structure moléculaire des odorants avec pour objectif de prédire la similarité perceptive de mélanges plus ou moins complexes d’odorants. Les résultats soulignent l'importance de prendre en compte la dimension d'intensité des odorants afin d’améliorer la qualité de la prédiction. Des perspectives d’amélioration du modèle sont dégagées pour permettre de dépasser la dimension de similarité et prédire des dimensions qualitatives de l’odeur.Le deuxième axe présente une démarche originale d’intégration de connaissances liées à l’expertise dans la procédure d'analyse de la flaveur. Ainsi, trois types de données hétérogènes sont agrégés dans un modèle mathématique global : des données chimiques, des données sensorielles et des connaissances d’experts aromaticiens. L'expertise est intégrée à travers la création d'une ontologie qui est ensuite associée à une approche de logique floue optimisée par algorithme évolutionnaire. Le modèle développé permet de prédire le profil odorant de seize vins rouges sur la base de leur composition en odorants. Au final, l’ensemble des travaux menés dans cette thèse apporte des résultats originaux permettant une meilleure compréhension de la construction des odeurs des aliments et permet d’élaborer des hypothèses quant aux relations sous-jacentes de l'espace perceptif des odeurs en mélanges complexes. / Among the sensory dimensions involved in food flavor, the odor component is critical because it often determines the identity and the typicality of the food. Chemical flavor analysis provides a list of the odorants contained in a food product but is not sufficient to predict the odor resulting from their mixture. Indeed, odor perception relies on the processing by the olfactory system of many odorants embedded in complex mixtures and several perceptual interactions can occur. Thus, the prediction of the perceptual outcome of a complex odor mixture remains challenging and two main approaches emerge from the literature review. On the one hand, predictive approaches based on the molecular structure of odorants have been proposed but have been limited to single odorants only. On the other hand, methodologies relying on recombination strategies after the chemical analyses of flavor have been successfully applied to identify those odorants that are key to the food odor. However, the choices of odorants to be recombined are mostly based on empirical approaches. Thus, two questions arise: How can we predict the odor quality of a mixture on the basis of the molecular structure of its odorants? How can we improve food flavor analysis in order to predict the odor of a food containing several tens of odorants? These two questions are at the basis of the thesis and of this manuscript which is divided in two main axes.The first axis describes the development of a model based on the concept of angle distances computed from the molecular structure of odorants in order to predict the odor similarity between mixtures. The results highlight the importance of taking into account the odor intensity dimension to reach a good prediction level. Moreover, several perspectives are proposed to extend the model prediction beyond the similarity dimension and to predict more qualitative dimensions of odors.The second axis presents an innovative strategy which allows integrating experts’ knowledge in the flavor analysis procedure. Three different types of heterogeneous data are embedded in a mathematical model: chemical data, sensory data and knowledge from expert flavorists. Experts’ knowledge is integrated owing to the development of an ontology, which is further used to define fuzzy rules optimized by evolutionary algorithms. The final output of the model is the prediction of red wines’ odor profile on the basis of their odorants’ composition. Overall, the thesis work brings original results allowing a better understanding of food odor construction and gives insights on the underlying relationships within the odor perceptual space for complex mixtures.