More than 350,000 anterior cruciate ligament (ACL) injuries occur every year in the United States. A torn ACL is typically replaced with an allograft or autograft tendon (patellar, quadriceps or hamstring), with the choice of tissue generally dictated by surgeon preference. Despite the number of ACL reconstructions performed every year, the process of ligamentization, transformation of a tendon graft to a healthy functional ligament, is poorly understood. Previous research studies have relied on mechanical, biochemical and histological studies. However, these methods are destructive. Clinically, magnetic resonance imaging (MRI) is the most common method of graft evaluation, but it lacks adequate resolution and molecular specificity. There is a need for objective methodology to study the ligament repair process that would ideally be non- or minimally invasive. Development of such a method could lead to a better understanding of the effects of therapeutic interventions and rehabilitation protocols in animal models of ligamentization, and ultimately, in clinical studies. Fourier transform infrared (FT-IR) spectroscopy is a technique sensitive to molecular structure and composition in tissues. FT-IR fiber optic probes combined with arthroscopy could prove to be an important tool where minimally invasive tissue assessment is required, such as assessment of graft composition during the ligamentization process. Spectroscopic methods have been used to differentiate normal and diseased connective tissues, but have not been applied to investigate ligamentization, or to investigate differences in tendons and ligaments. In the proposed studies, we hypothesize that infrared spectroscopy can provide molecular information about the compositional differences between tendons and ligaments, which can serve as a foundation to non-destructively monitor the tissue transformation that occurs during ligamentization. / Bioengineering

In the past 30 years zero-valent iron (ZVI) has become an increasingly popular reducing agent technology for remediating environmental contaminants prone to chemical degradation. Azo dyes and chlorinated organic compounds (COCs) are two classes of such contaminants, both of which include toxic compounds with known carcinogenic potential. ZVI has been successfully applied to the surfaces of permeable reactive barriers, as well as grown into nanoscale particles (nZVI) and applied in-situ to chemically reduce these contaminants into more environmentally benign compounds. However, the reactivity of ZVI and nZVI in these technologies is limited by their finite supply of electrons for facilitating chemical reduction, and the tendency of nZVI particles to homo-aggregate in solution and form colloids with reduced surface area to volume ratio, and thus reduced reactivity. The goal of this project was to combine reactive nanoparticle and membrane technologies to create an electro-catalytic permeable reactive barrier that overcomes the weaknesses of nZVI for the enhanced electrochemical filtration of azo dyes in solution. Specifically, nZVI was successfully grown and stabilized in a network of functionalized carbon nanotubes (CNTs) and deposited into an electrically conductive thin film on the surface of a polymeric microfiltration support membrane. Under a cathodic applied voltage, this thin film facilitated the direct reduction of the methyl orange (MO) azo dye in solution, and regenerated nZVI reactivity for enhanced electro-catalytic operation. The electro-catalytic performance of these nZVI-CNT membrane surface composites to remove MO was validated, modelled, and optimized in a batch system, as well as tested in a dead-end continuous flow cell system. In the batch experiments, systems with nZVI and a -2 V applied potential demonstrated synergistic enhancement of MO removal, which indicated the regeneration of nZVI reactivity and allowed for the complete removal of 0.25 mM MO batches within 2-3 hours. Partial least squares regression (PLSR) modelling was used to determine the impact of each experimental parameter in the batch system and provided the means for an optimization leading to maximized MO removal. Finally, tests in a continuous system yielded rates of MO removal 1.6 times greater than those of the batch system in a single pass, and demonstrated ~87% molar removal of MO at fluxes of approximately 422 lmh. The work herein lays the foundation for a promising technology that, if further developed, could be applied to remediate azo dyes and COCs in textile industry effluents and groundwater sites respectively. / Thesis / Master of Applied Science (MASc)

[ES] De acuerdo con las regulaciones europeas y muchos estudios científicos, es necesario monitorear y analizar las condiciones microclimáticas en museos o edificios, para preservar las obras de arte en ellos. Con el objetivo de ofrecer herramientas para el monitoreo de las condiciones climáticas en este tipo de edificios, en esta tesis doctoral se propone una nueva metodología estadística para clasificar series temporales de parámetros climáticos como la temperatura y humedad relativa. La metodología consiste en aplicar un método de clasificación usando variables que se computan a partir de las series de tiempos. Los dos primeros métodos de clasificación son versiones conocidas de métodos sparse PLS que no se habían aplicado a datos correlacionados en el tiempo. El tercer método es una nueva propuesta que usa dos algoritmos conocidos. Los métodos de clasificación se basan en diferentes versiones de un método sparse de análisis discriminante de mínimos cuadra- dos parciales PLS (sPLS-DA, SPLSDA y sPLS) y análisis discriminante lineal (LDA). Las variables que los métodos de clasificación usan como input, corresponden a parámetros estimados a partir de distintos modelos, métodos y funciones del área de las series de tiempo, por ejemplo, modelo ARIMA estacional, modelo ARIMA- TGARCH estacional, método estacional Holt-Winters, función de densidad espectral, función de autocorrelación (ACF), función de autocorrelación parcial (PACF), rango móvil (MR), entre otras funciones. También fueron utilizadas algunas variables que se utilizan en el campo de la astronomía para clasificar estrellas. En los casos que a priori no hubo información de los clusters de las series de tiempos, las dos primeras componentes de un análisis de componentes principales (PCA) fueron utilizadas por el algoritmo k- means para identificar posibles clusters de las series de tiempo. Adicionalmente, los resultados del método sPLS-DA fueron comparados con los del algoritmo random forest. Tres bases de datos de series de tiempos de humedad relativa o de temperatura fueron analizadas. Los clusters de las series de tiempos se analizaron de acuerdo a diferentes zonas o diferentes niveles de alturas donde fueron instalados sensores para el monitoreo de las condiciones climáticas en los 3 edificios.El algoritmo random forest y las diferentes versiones del método sparse PLS fueron útiles para identificar las variables más importantes en la clasificación de las series de tiempos. Los resultados de sPLS-DA y random forest fueron muy similares cuando se usaron como variables de entrada las calculadas a partir del método Holt-Winters o a partir de funciones aplicadas a las series de tiempo. Aunque los resultados del método random forest fueron levemente mejores que los encontrados por sPLS-DA en cuanto a las tasas de error de clasificación, los resultados de sPLS- DA fueron más fáciles de interpretar. Cuando las diferentes versiones del método sparse PLS utilizaron variables resultantes del método Holt-Winters, los clusters de las series de tiempo fueron mejor discriminados. Entre las diferentes versiones del método sparse PLS, la versión sPLS con LDA obtuvo la mejor discriminación de las series de tiempo, con un menor valor de la tasa de error de clasificación, y utilizando el menor o segundo menor número de variables.En esta tesis doctoral se propone usar una versión sparse de PLS (sPLS-DA, o sPLS con LDA) con variables calculadas a partir de series de tiempo para la clasificación de éstas. Al aplicar la metodología a las distintas bases de datos estudiadas, se encontraron modelos parsimoniosos, con pocas variables, y se obtuvo una discriminación satisfactoria de los diferentes clusters de las series de tiempo con fácil interpretación. La metodología propuesta puede ser útil para caracterizar las distintas zonas o alturas en museos o edificios históricos de acuerdo con sus condiciones climáticas, con el objetivo de prevenir problemas de conservación con las obras de arte. / [CA] D'acord amb les regulacions europees i molts estudis científics, és necessari monitorar i analitzar les condiciones microclimàtiques en museus i en edificis similars, per a preservar les obres d'art que s'exposen en ells. Amb l'objectiu d'oferir eines per al monitoratge de les condicions climàtiques en aquesta mena d'edificis, en aquesta tesi es proposa una nova metodologia estadística per a classificar series temporals de paràmetres climàtics com la temperatura i humitat relativa.La metodologia consisteix a aplicar un mètode de classificació usant variables que es computen a partir de les sèries de temps. Els dos primers mètodes de classificació són versions conegudes de mètodes sparse PLS que no s'havien aplicat adades correlacionades en el temps. El tercer mètode és una nova proposta que usados algorismes coneguts. Els mètodes de classificació es basen en diferents versions d'un mètode sparse d'anàlisi discriminant de mínims quadrats parcials PLS (sPLS-DA, SPLSDA i sPLS) i anàlisi discriminant lineal (LDA). Les variables queels mètodes de classificació usen com a input, corresponen a paràmetres estimats a partir de diferents models, mètodes i funcions de l'àrea de les sèries de temps, per exemple, model ARIMA estacional, model ARIMA-TGARCH estacional, mètode estacional Holt-Winters, funció de densitat espectral, funció d'autocorrelació (ACF), funció d'autocorrelació parcial (PACF), rang mòbil (MR), entre altres funcions. També van ser utilitzades algunes variables que s'utilitzen en el camp de l'astronomia per a classificar estreles. En els casos que a priori no va haver-hi información dels clústers de les sèries de temps, les dues primeres components d'una anàlisi de components principals (PCA) van ser utilitzades per l'algorisme k-means per a identificar possibles clústers de les sèries de temps. Addicionalment, els resultats del mètode sPLS-DA van ser comparats amb els de l'algorisme random forest.Tres bases de dades de sèries de temps d'humitat relativa o de temperatura varen ser analitzades. Els clústers de les sèries de temps es van analitzar d'acord a diferents zones o diferents nivells d'altures on van ser instal·lats sensors per al monitoratge de les condicions climàtiques en els edificis.L'algorisme random forest i les diferents versions del mètode sparse PLS van ser útils per a identificar les variables més importants en la classificació de les series de temps. Els resultats de sPLS-DA i random forest van ser molt similars quan es van usar com a variables d'entrada les calculades a partir del mètode Holt-winters o a partir de funcions aplicades a les sèries de temps. Encara que els resultats del mètode random forest van ser lleument millors que els trobats per sPLS-DA quant a les taxes d'error de classificació, els resultats de sPLS-DA van ser més fàcils d'interpretar.Quan les diferents versions del mètode sparse PLS van utilitzar variables resultants del mètode Holt-Winters, els clústers de les sèries de temps van ser més ben discriminats. Entre les diferents versions del mètode sparse PLS, la versió sPLS amb LDA va obtindre la millor discriminació de les sèries de temps, amb un menor valor de la taxa d'error de classificació, i utilitzant el menor o segon menor nombre de variables.En aquesta tesi proposem usar una versió sparse de PLS (sPLS-DA, o sPLS amb LDA) amb variables calculades a partir de sèries de temps per a classificar series de temps. En aplicar la metodologia a les diferents bases de dades estudiades, es van trobar models parsimoniosos, amb poques variables, i varem obtindre una discriminació satisfactòria dels diferents clústers de les sèries de temps amb fácil interpretació. La metodologia proposada pot ser útil per a caracteritzar les diferents zones o altures en museus o edificis similars d'acord amb les seues condicions climàtiques, amb l'objectiu de previndre problemes amb les obres d'art. / [EN] According to different European Standards and several studies, it is necessary to monitor and analyze the microclimatic conditions in museums and similar buildings, with the goal of preserving artworks. With the aim of offering tools to monitor the climatic conditions, a new statistical methodology for classifying time series of different climatic parameters, such as relative humidity and temperature, is pro- posed in this dissertation.The methodology consists of applying a classification method using variables that are computed from time series. The two first classification methods are ver- sions of known sparse methods which have not been applied to time dependent data. The third method is a new proposal that uses two known algorithms. These classification methods are based on different versions of sparse partial least squares discriminant analysis PLS (sPLS-DA, SPLSDA, and sPLS) and Linear Discriminant Analysis (LDA). The variables that are computed from time series, correspond to parameter estimates from functions, methods, or models commonly found in the area of time series, e.g., seasonal ARIMA model, seasonal ARIMA-TGARCH model, seasonal Holt-Winters method, spectral density function, autocorrelation function (ACF), partial autocorrelation function (PACF), moving range (MR), among others functions. Also, some variables employed in the field of astronomy (for classifying stars) were proposed.The methodology proposed consists of two parts. Firstly, different variables are computed applying the methods, models or functions mentioned above, to time series. Next, once the variables are calculated, they are used as input for a classification method like sPLS-DA, SPLSDA, or SPLS with LDA (new proposal). When there was no information about the clusters of the different time series, the first two components from principal component analysis (PCA) were used as input for k-means method for identifying possible clusters of time series. In addition, results from random forest algorithm were compared with results from sPLS-DA.This study analyzed three sets of time series of relative humidity or temperate, recorded in different buildings (Valencia's Cathedral, the archaeological site of L'Almoina, and the baroque church of Saint Thomas and Saint Philip Neri) in Valencia, Spain. The clusters of the time series were analyzed according to different zones or different levels of the sensor heights, for monitoring the climatic conditions in these buildings.Random forest algorithm and different versions of sparse PLS helped identifying the main variables for classifying the time series. When comparing the results from sPLS-DA and random forest, they were very similar for variables from seasonal Holt-Winters method and functions which were applied to the time series. The results from sPLS-DA were easier to interpret than results from random forest. When the different versions of sparse PLS used variables from seasonal Holt- Winters method as input, the clusters of the time series were identified effectively.The variables from seasonal Holt-Winters helped to obtain the best, or the second best results, according to the classification error rate. Among the different versions of sparse PLS proposed, sPLS with LDA helped to classify time series using a fewer number of variables with the lowest classification error rate.We propose using a version of sparse PLS (sPLS-DA, or sPLS with LDA) with variables computed from time series for classifying time series. For the different data sets studied, the methodology helped to produce parsimonious models with few variables, it achieved satisfactory discrimination of the different clusters of the time series which are easily interpreted. This methodology can be useful for characterizing and monitoring micro-climatic conditions in museums, or similar buildings, for preventing problems with artwork. / I gratefully acknowledge the financial support of Pontificia Universidad Javeriana Cali – PUJ and Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior – ICETEX who awarded me the scholarships ’Convenio de Capacitación para Docentes O. J. 086/17’ and ’Programa Crédito Pasaporte a la Ciencia ID 3595089 foco-reto salud’ respectively. The scholarships were essential for obtaining the Ph.D. Also, I gratefully acknowledge the financial support of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 814624. / Ramírez Buelvas, SM. (2022). A Statistical Methodology for Classifying Time Series in the Context of Climatic Data [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181123

This project utilised a novel solvent-free continuous cocrystallisation (SFCC) method to manufacture pharmaceutical co-crystals. The objectives were to optimize the process towards achieving high co-crystal yields and to understand the behaviour of co-crystals under different conditions. Particular attention was paid to the development of near infrared (NIR) spectroscopy as a process analytical technology (PAT). Twin screw, hot melt extrusion was the base technique of the SFCC process. Changing parameters such as temperature, screw speed and screw geometry was important for improving the co-crystal yield. The level of mixing and shear was directly influenced by the screw geometry, whilst the screw speed was an important parameter for controlling the residence time of the material during hot melt extrusion. Ibuprofen – nicotinamide 1:1 cocrystals and carbamazepine – nicotinamide 1:1 co-crystals were successfully manufactured using the SFCC method. Characterisation techniques were important for this project, and NIR spectroscopy proved to be a convenient, accurate analytical technique for identifying the formation of co-crystals along the extruder barrel. Separate thermal and model shear deformation studies were also carried out to determine the effect of temperature and shear on co-crystal formation for several different pharmaceutical co-crystal pairs. Finally, NIR spectroscopy was used to create two partial least squares regression models, for predicting the 1:1 co-crystal yield of ibuprofen – nicotinamide and carbamazepine – nicotinamide, when in a powder mixture with the respective pure API. It is believed that the prediction models created in this project can be used to facilitate future in-line PAT studies of pharmaceutical co-crystals during different manufacturing processes. / Engineering and Physical Sciences Research Council (EPSRC)

Les tourbières ombrotrophes, ou bogs sont particulièrement vulnérables à l’augmentation de la déposition atmosphérique d’azote. Cet apport d’un nutriment normalement limitant altère la capacité des tourbières à accumuler le carbone (C), en plus de mener à des changements de leur composition végétale. L’imagerie spectrale est une approche prometteuse puisqu’elle rend possible la détection des espèces végétales et de certaines caractéristiques chimiques des plantes, à distance. Toutefois, l’ampleur des différences spectrales intra- et interespèces n’est pas encore connue. Nous avons évalué la façon dont la chimie, la structure et la signature spectrale des feuilles changent chez Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum et Eriophorum vaginatum, dans trois tourbières du sud du Québec et de l’Ontario, incluant une tourbière où se déroule une expérience de fertilisation à long terme. Nous avons mesuré des changements dans les traits fonctionnels dus aux différences dans la quantité d’azote disponible dans les sites. Toutefois, la déposition atmosphérique d’azote a eu relativement peu d’effet sur les spectres foliaires ; les variations spectrales les plus importantes étaient entre les espèces. En fait, nous avons trouvé que les quatre espèces ont un spectre caractéristique, une signature spectrale permettant leur identification au moyen d’analyses discriminantes des moindres carrés partiels (PLSDA). De plus, nous avons réussi à prédire plusieurs traits fonctionnels (l’azote, le carbone ; et la proportion d’eau et de matière sèche) avec moins de 10 % d’erreur grâce à des régressions des moindres carrés partiels (PLSR) des données spectrales. Notre étude fournit de nouvelles preuves que les variations intraspécifiques, causées en partie par des variations environnementales considérables, sont perceptibles dans les spectres foliaires. Toutefois, les variations intraspécifiques n’affectent pas l’identification des espèces ou la prédiction des traits. Nous démontrons que les spectres foliaires comprennent des informations sur les espèces et leurs traits fonctionnels, confirmant le potentiel de la spectroscopie pour le suivi des tourbières. / Abstract Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18 years N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.

La littérature sur le stress au travail considère souvent que les personnes dépourvues de soutien social tendent à avoir un taux de stress plus élevé. Si cette vision est confirmée empiriquement, elle a toutefois une portée limitée : elle ne tient pas toujours compte de l’inégalité d’accès au soutien, inégalité qui affecte la perception de ce soutien. Pourquoi certains salariés ont plus de facilité à accéder au soutien social ? Qu’est-ce qui fait que l’aide est plus disponible et plus variée pour une personne plutôt que pour une autre ? Ces interrogations nous amènent à situer le soutien social perçu, et plus précisément le soutien des collègues perçu, dans un modèle théorique plus large nourri par la théorie des réseaux sociaux. A l’aide d’un modèle explicatif, l’objectif de notre recherche est d’étudier l’impact du positionnement de l’individu dans le réseau social sur le stress au travail perçu. Les hypothèses de recherche ont été testées en utilisant les techniques de régression en moindres carrés partiels pour estimer les équations structurelles. A partir de données de type « réseau complet » collectées auprès d’une entreprise de services de taille moyenne (N=343), nous avons montré que la force des liens favorise l’accès au soutien des collègues et, par conséquent, réduit le stress professionnel. Les résultats indiquent que le soutien des collègues est médiateur total dans cette relation, et que le lien direct entre la force des liens et le stress perçu n’est pas établi. De plus, nous avons confirmé l’ambivalence des bridging ties (liens vers des personnes de départements différents) : ils influencent négativement la perception du soutien social (qui réduit le stress), mais ont aussi un effet négatif direct sur le stress au travail. En soulignant le rôle des relations informelles comme antécédent au soutien social, nous avons contribué à fournir un outil analytique susceptible d’être mis en œuvre dans la sphère managériale. / The literature on workplace stress often considers that people who lack social support tend to have higher levels of perceived stress. This is empirically confirmed, but it is not always taken into account and offers limited scope. Indeed, why do some employees have more access to social support? What renders support more available and more varied from one person to the other? These questions allow us to situate perceived social support, and more accurately the perceived support of colleagues, in a larger theoretical model, enhanced by social network theory. Through an explanatory model, the objective of this research is to explore the role of the positioning of the individual in the social network on perceived workplace stress. Based on a “complete network” data in a medium-sized IT services company, we used partial least squares to test our hypothesis (N = 343). The strength of ties affects stress through social support, such that people with stronger ties perceive more support and ultimately exhibit less stress. However, the direct link between strength and stress is not established. Bridging ties (supportive ties to other departments) negatively influence social support (a situation which increases stress) but also have a direct negative effect on stress. By stressing the role of social relationships as an antecedent of social support and stress, our results offer new potential managerial actions within organizations

A associação dos métodos cinéticos de análises e dos sistemas de análises em fluxo foi demonstrada em relação à determinação espectrofotométrica de ferro e vanádio em ligas Fe-V O método se baseia na influência de Fe2+ e VO2+ na taxa de oxidação de iodeto por dicromato sob condições ácidas; por esta razão o emprego do redutor de Jones foi necessário. Um sistema de análises por injeção em fluxo (FIA) e um sistema multi-impulsão foram dimensionados e avaliados. Em ambos os sistemas, a solução da amostra era inserida no fluxo transportador / reagente iodeto, e a solução de dicromato era adicionada por confluência. Sucessivas medidas eram realizadas durante a passagem da zona de amostra processada pelo detector, cada uma relacionada a uma diferente condição para o desenvolvimento da reação. O tratamento dos dados envolveu calibração multivariada, particularmente o algorítmo PLS. O sistema FIA se mostrou pouco adequado para as determinações multi-paramétricas, uma vez que os elementos de fluído resultantes da natureza de escoamento laminar não continham informações cinéticas suficientes para compor as etapas de modelagem. Por outro lado, MPFS mostrou que a natureza do fluxo pulsado resulta em melhorias nas figuras de mérito devido ao movimento caótico dos elementos de fluído. O sistema proposto é simples e robusto, capaz de analisar 50 amostras por hora, significando em um consumo de 48 mg KI por determinação. A duas primeiras variáveis latentes contém ca 94 % da informação analítica, mostrando que a dimensionalidade dupla intrínsica ao conjunto de dados. Os resultados se apresentaram concordantes com aqueles obtidos por espectrometria de emissão optica com plasma induzido em argônio. / Differential kinetic analysis can be implemented in a flow system analyser, and this was demonstrated in designing an improved spectrophotometric catalytic determination of iron and vanadium in Fe-V alloys. The method relied on the influence of Fe2+ and VO2+ on the rate of the iodide oxidation by Cr2O7 under acidic conditions; therefore the Jones reductor was needed. To this end, a flow injection system (FIA) and a multi-pumping flow system (MPFS) were dimensioned and evaluated. In both systems, the alloy solution was inserted into an acidic KI solution that acted also as carrier stream, and a dichromate solution was added by confluence. Successive measurements were performed during sample passage through the detector, each one related to a different yet reproducible condition for reaction development. Data treatment involved multivariate calibration by the PLS algorithm. The FIA system was less recommended for multi-parametric determination, as the laminar flow regimen could not provide suitable kinetic information. On the other hand, a MPFS demonstrated that pulsed flow led to enhance figures of merit due to chaotic movement of its fluid elements. The proposed MPFS system is very simple and rugged, allowing 50 samples to be run per hour, meaning 48 mg KI per determination. The first two latent variables carry ca 94 % of the analytical information, pointing out that the intrinsic dimensionality of the data set is two. Results are in agreement with inductively coupled argon plasma – optical emission spectrometry.

Thesis submitted in fulfilment of the requirements for the degree Master of Technology Chemistry in the Faculty of (Science) Supervisor: Professor T.N. van der Walt Bellville campus Date submitted: October 2011 / It is important to quantify polymeric components in a coating because they greatly influence the performance of a coating. The difficulty associated with analysis of polymers by Fourier transform infrared (FTIR) analysis’s is that colinearities arise from similar or overlapping spectral features. A quantitative FTIR method with attenuated total reflectance coupled to multivariate/ chemometric analysis is presented. It allows for simultaneous quantification of 3 polymeric components; a rheology modifier, organic opacifier and styrene acrylic binder, with no prior extraction or separation from the paint. The factor based methods partial least squares (PLS) and principle component regression (PCR) permit colinearities by decomposing the spectral data into smaller matrices with principle scores and loading vectors. For model building spectral information from calibrators and validation samples at different analysis regions were incorporated. PCR and PLS were used to inspect the variation within the sample set. The PLS algorithms were found to predict the polymeric components the best. The concentrations of the polymeric components in a coating were predicted with the calibration model. Three PLS models each with different analysis regions yielded a coefficient of correlation R2 close to 1 for each of the components. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) was less than 5%. The best out-put was obtained where spectral features of water was included (Trial 3). The prediction residual values for the three models ranged from 2 to -2 and 10 to -10. The method allows paint samples to be analysed in pure form and opens many opportunities for other coating components to be analysed in the same way.

Análise cinética diferencial explora diferenças em taxas reacionais entre os analitos e um sistema reacional comum; etapas de separação prévia dos analitos podem então ser prescindidas. Sistemas de análise por injeção em fluxo (FIA) se afiguram como uma ferramenta importante para métodos envolvendo essa estratégia, pois permitem um controle preciso da dispersão de reagentes / amostras e da temporização. O objetivo deste trabalho foi então explorar estes dois aspectos favoráveis visando a determinação simultânea de cobre e de níquel, a partir de suas reações com o reagente cromogênico 5-Br-PADAP. Três alíquotas de amostra eram simultaneamente inseridas, por meio de um injetor proporcional, no fluxo transportador reagente (5-Br-PADAP 75 mg L-1 + sistema tampão 0,5 mol L-1 em ácido acético / acetato, pH 4,7) de um sistema FIA em linha única. Durante o transporte em direção ao detector, as zonas estabelecidas se coalesciam, originando uma zona complexa que era monitorada a 562 nm. Os valores locais máximos e mínimos da função concentração / tempo obtida eram considerados para calibração multivariada utilizando a ferramenta quimiométrica PLS-2 (partial least squares - 2). A concentração do reagente, a capacidade tampão, a temperatura, a vazão, os comprimentos do percurso analítico e das alças de amostragem, bem como a distância inicial entre as zonas de amostra estabelecidas foram avaliados para construção dos modelos matemáticos. Estes foram criados a partir de 24 soluções-padrão mistas de Cu2+ e Ni2+ (0,00-1,60 mg L-1 em HNO3 a 0,1% v/v). Duas variáveis latentes foram suficientes para capturar > 98 % das variâncias inerentes ao conjunto de dados e erros médios das previsões (RMSEP) foram estimados em 0,025 e 0,071 mg L-1 para Cu e Ni, salientando a boa precisão do modelo de calibração. O sistema proposto apresenta boas figuras de mérito: fisicamente estável, quando mantido em operação por quatro horas ininterruptas, consumo de 314 \'mü\'g 5-Br-PADAP por amostra, frequência analítica de 33 amostras por hora (165 dados, 66 determinações) e erros nas leituras em sinais de absorbância tipicamente < 5%. Entretanto, verificou-se a inexatidão das previsões efetuadas pelo modelo proposto, quando comparadas aos resultados obtidos por ICP OES. A partir deste fato, tornam-se necessários maiores estudos referentes a este tipo de matriz, bem como de técnicas de mascaramento dos possíveis interferentes presentes / Differential kinetic analysis exploits the differences in reaction rates between the analytes and a common reactant system; prior steps of analyte separation can then be waived. Flow-injection systems (FIA) are considered as an important tool for methods involving such a strategy because they allow precise control of sample / reagent dispersion and timing. The aim of this work was then to exploit these two favorable aspects for the simultaneous determination of copper and nickel using the 5-Br-PADAP chromogenic reagent. Three sample aliquots were simultaneously inserted by means of a proportional injector into reagent carrier stream (75 mg L-1 5-Br-PADAP + 0.5 mol L-1 acetic acid / acetate, pH 4.7) of a single-line FIA system. During transport towards detection, the established zones coalesce themselves, resulting in a complex zone that was monitored at 562 nm. The local maximum and minimum values of the concentration / time obtained function were considered for multivariate calibration using the PLS-2 (partial least squares - 2) chemometric tool. The reagent concentration, buffering capacity, temperature, flow rate and lengths of the analytical path, sampling loops and initial distance between plugs were established and evaluated for the construction of mathematical models. To this end, 24 Cu2+ and Ni2+ (0.00 - 1.60 mg L-1, also 0.1% v/v HNO3) mixed standard solutions were used. Two latent variables were enough to capture > 98% of the variance inherent in the data set and average prediction errors (RMSEP) were estimated as 0.025 and 0.071 mg L-1 for Cu and Ni, emphasizing the good precision the calibration model. The proposed system presents good figures of merit: physical stability when kept in operation for four uninterrupted hours, consumption of 314 \'mü\'g 5-Br-PADAP per sample, sample throughput of 33 h-1 (165 data, 66 determinations) and error readings in absorbance signals typically <5%. However, inaccuracy of the predictions made by the proposed model when compared to results obtained by ICP OES was noted. Thus, further studies involving this type of matrix, as well as masking techniques of potential interferences present, are recommended

En Analyse Sensorielle, les préférences des consommateurs sont généralement modélisées en fonction de données sensorielles par les méthodes de cartographie des préférences. L'objectif de cette thèse est de modéliser les préférences des consommateurs en intégrant, en plus des données sensorielles, de nouvelles variables relatives à leur perception des produits. Nous appellerons ces variables les dimensions subjectives. Elles recouvrent des dimensions pragmatiques liées à l'utilisation du produit et des dimensions plus symboliques telles que l'esthétisme, la modernité, l'originalité…Les problématiques relatives aux dimensions subjectives ont d'abord été étudiées lors d'une étude exploratoire sur des tasses à café. L'ensemble du travail a ensuite été réalisé sur 11 sièges de voitures. Dans un premier temps, des entretiens qualitatifs ont été réalisés auprès de 16 consommateurs d'une part et de 2 designers d'autre part. Ces entretiens ont permis d'identifier les dimensions subjectives caractéristiques des sièges. Une évaluation quantitative des dimensions subjectives et des préférences a ensuite été réalisée par 110 consommateurs. Enfin, les sièges ont été caractérisés sensoriellement par des experts. Les préférences des consommateurs ont été modélisées en fonction des données sensorielles et des dimensions subjectives par des modèles d'équations structurelles à variables latentes, plus précisément par Partial Least Square Path Modeling. Quatre modèles, fondés sur les groupes de préférences, ont été mis en place. Selon le groupe étudié, la contribution des deux jeux de données diffère et quatre profils de clients sont identifiés. D'un point de vue méthodologique, ce travail fournit des éléments de réponse sur l'intérêt des dimensions subjectives pour la modélisation des préférences. L'ensemble de la démarche est en cours d'application sur un produit alimentaire : le chocolat. / In Sensory Science, preference mapping is used to explain consumers' preferences with sensory data. This PhD aims to integrate not only sensory data but also new variables that are related to consumers' perception of the product in the modelling of consumers' preferences. These variables are labelled as subjective dimensions. They address the pragmatic dimensions that cover the context of use of the products and more symbolic dimensions, such as aesthetics, modernity, originality…An exploratory study based on coffee cups was a first mean to approach the issues related to subjective dimensions. Then, all the work was done on a study of 11 car seats. The first step consisted in qualitative interviews of 16 consumers and of 2 designers. These interviews allowed identifying the subjective dimensions that characterize car seats. 110 consumers then performed a quantitative evaluation of their preferences and subjective dimensions. Finally, the seats were characterized by experts with sensory descriptors. The consumers' preferences were modelled according to both sensory data and subjective dimensions, using structural equations: the Partial Least Square Path Modeling. Four models based on preferences clustering were established. The contribution of two kinds of data differed according to the considered cluster, which led to the identification of four customer profiles. From a methodological point of view, this work provides first elements about the benefit of subjective dimensions in preference modelling. The methodology is being implemented on a food product: chocolate.