Trots att tjära har använts av människan under en väldigt lång tid har tjärframställning ägnats förvånansvärt lite uppmärksamhet inom arkeologin. Trots att det i Sverige finns tusentals tjärframställningsplatser registrerade har endast ett fåtal av dessa grävts ut. Denna uppsats syftar till att addera kunskap om historisk tjärframställning och om spår av denna kan identifieras med hjälp av Miljö- och geoarkeologiska metoder. Genom att analysera jordprover från en historisk tjärdal demonstrerar denna uppsats hur fosfatanalys (CitP), magnetisk suceptibilitet (MS), röntgenfluorescens (XRF) och nära infraröd reflektansspektroskopi (NIRS) kan vara viktiga verktyg för arkeologisk forskning om historisk tjärframställning.

Mikroplastik (1–1000 µm) kommt praktisch überall in der Umwelt vor, aber immer noch ist die Iden-tifizierung und Quantifizierung eine anspruchsvolle und zeitintensive Aufgabe. Erste analytisch Metho¬den beginnen sich zu etablieren, jedoch sind die benötigten Instrumente komplex und der Probendurchsatz für Routineuntersuchungen in den meisten Fällen noch zu gering. Diese Arbeit widmet sich zunächst dem Potenzial der Nahinfrarot (NIR)-Spektroskopie diese Lücke zu schließen. Exemplarisch wird ein günstiges Verfahren mit großem Probendurchsatz zur Bestimmung von Mikro¬plastik-Gesamtgehalten der verbreiteten Verpackungskunststoffe Polyethylen (PE), Polystyrol (PS) und Polypropylen (PP) in Böden und Kompost entwickelt. Neben der Untersuchung von Mikroplastik-Gesamtgehalten einer Probe ist auch die Charakterisierung individueller Partikel von großer Bedeutung. Die bildgebende Fourier-Transform-Infrarot (FTIR)-Mikrospektroskopie ist hierfür sehr gut geeignet. Allerdings ist es eine Herausforderung Mikroplastik in den aus mehreren Million Spektren bestehenden hyperspektralen Bildern zu identifizieren. Eine schnelle und zuverlässige Mikroplastikerkennung wird hier durch eine explorative Analyse und automatisierte Klassifizierung der Spektren erreicht. Zusammenfassend zeigt diese Arbeit, dass die optische Spektroskopie im mittleren und nahen Infrarot über ihre bisherige Anwendung hinaus ein großes Potenzial besitzen, die Mikroplastik-Analytik kostengünstiger, einfacher und schneller zu gestalten. / Microplastics (1-1000 µm) are ubiquitous in the environment, but their identification and quantification is still a challenging and time-consuming task. The first established methods require complex instruments and the sample throughput is still too low for routine analysis in most cases. This work first addresses the potential of near-infrared (NIR) spectroscopy to fill this gap. A low-cost method with large sample throughput is developed for the determination of total microplastic contents of the common packaging plastics polyethylene (PE), polystyrene (PS) and polypropylene (PP) in soils and compost. In addition to the investigation of total microplastic levels in a sample, the characterization of individual particles is also of great importance. Fourier transform infrared (FTIR) imaging microspectroscopy is well suited for this purpose. However, it is challenging to identify microplastics in hyperspectral images consisting of several million spectra. Fast and reliable microplastic detection is achieved by exploratory analysis and automated classification of the spectra. In summary, this work shows that mid- and near-infrared optical spectroscopy have great potential beyond their current application to make microplastics analysis cheaper, easier, and faster.

Reflective roof coatings keep the roof cooler by minimizing solar absorption and maximizing thermal emission. Keeping the surface of the roof cooler allows less heat to be conducted into the interior of the building which reduces the cooling load in air-conditioned buildings and improve comfort conditions in non-air conditioned buildings. A number of cool white materials, compatible with most roofing products, are available on the market. To appeal to homeowners, special cool “color” products have been developed to match the dark colors of conventional residential roofs but are highly reflective in the invisible near-infrared (NIR) spectrum. Although many studies highlight the benefits of cool white coatings on roof membranes of low-slope roofs, knowledge of NIR reflective coatings on asphalt shingles of steep slope roofs remains limited. The intent of this exploratory study is to present a process that can be used to evaluate the perceived and actual benefits of NIR coatings field-applied to asphalt shingles on single-family houses. The proposed process can be applied to a large sample of homes and occupants in a future study. A questionnaire was designed to attempt to evaluate occupants’ perceived benefits in regards to their indoor environment and occupant satisfaction following applications of NIR coatings. Along with subjective data collection, a field-experiment was developed to objectively compare the thermal performance of an NIR reflective field-coated asphalt shingle roof system with that of a conventional asphalt shingle roof system. Questionnaire results indicated that occupants did not perceive any significant changes to their indoor environment but were satisfied overall with the application and appearance of the roof coating. Additionally, 50% of occupants stated that their monthly energy costs somewhat decreased after the application. Interestingly, 63% of respondents experienced some form of roof leak following the coating application. Among those who experienced roof leaks, 100% of the roofs were 10 years or older. Field results showed that the coated roof surface was 2 to 5℉ cooler than the uncoated roof surface at midafternoon. Statistical testing for correlation between coated roof surface temperature and external conditions revealed that relative humidity was negatively correlated with coated roof temperature, while solar altitude angle was positively correlated with coated roof temperature. Multiple linear regression analysis was used to develop a model for predicting the surface temperature of the coated asphalt shingle roofs from the ambient temperature, sky conditions, dew point temperature, relative humidity, solar altitude and azimuth angle.

En 1992, le cas de Barr Laboratories vs. US Food and Drug Administration (FDA) est le sujet de l'heure dans l'industrie pharmaceutique. La FDA poursuit le manufacturier contractuel de produit pharmaceutique pour ses mauvaises pratiques de validation de procédés et de tests de routine. Le mélange des matières premières est particulièrement visé. Les procédures de validation y sont en effet négligées et les contrôles et évaluations de la qualité du produit fini sont alors restreints aux tests de quelques comprimés sur un lot parfois de plusieurs millions de comprimés. Un besoin est ainsi identifié : mettre en place un nouveau système d'évaluation de la qualité ou de contrôle de la production plus représentatif. C'est ainsi qu'en 2004 est lancée l'initiative des technologies d'analyse des procédés, ou"Process Analytical Technologies" ou PAT. Cette initiative, chapeautée par la FDA, prend rapidement de l'ampleur bien que restant à ce jour une recommandation plus qu'une loi. Dans la même veine, le projet de Recherche et Développement Coopératif (RDC) entre l'Université de Sherbrooke et Wyeth Pharmaceutiques sur l'étude des phénomènes granulaires d'écoulement et de ségrégation identifie ce même besoin important pour la continuation des travaux. C'est ainsi que naquit ce présent projet de maîtrise. Afin de faire suite aux recommandations de la FDA et afin de continuer les études sur les systèmes particulaires, un outil de suivi de la qualité en ligne doit être développé pour les mélangeurs utilisés dans l'industrie pharmaceutique. Cet outil devra éventuellement être viable pour la production de l'entreprise et servira de critère de relâche des produits finis. La technologie de la spectroscopie en proche infrarouge (NIR) a rapidement été identifiée pour ce besoin. Cette technologie possède les avantages nécessaires afin d'être installée sur le mélangeur et suivre la qualité de mélange en temps réel. Les travaux ici présentés ont été effectués entre janvier 2006 et septembre 2008 sur une véritable formulation pharmaceutique et ont été appliqués à l'échelle laboratoire tout comme à l'échelle de production conventionnelle. Les travaux ont permis d'identifier trois techniques d'analyse statistique viables pour le suivi de l'homogénéité de mélange en ligne : l'analyse de la variance, l'analyse de la distance et l'analyse quantitative. Ces trois techniques possèdent toutes leurs avantages et désavantages et le choix d'une de ces techniques dépend de la formule visée et du besoin du client. De plus, grâce à l'application d'un Design d'Expérience (DoE), les paramètres critiques ayant une influence sur le spectre NIR ont été évalués. La distribution de tailles de particules est un de ces facteurs ayant une influence importante. Son influence a été évaluée grâce à l'application du modèle de Kubelka-Munk. Les recommandations de ces travaux ont été transmises à l'entreprise Wyeth Pharmaceutiquess à Montréal et seront appliquées pour la future implantation de la technologie NIR pour le suivi de l'homogénéité de mélange lors des procédés de mélanges granulaires pharmaceutiques.

Le contrôle de la qualité est une étape critique lors de la fabrication de produits pharmaceutiques. Pour accomplir cette tâche, différentes méthodes de laboratoire peuvent être utilisées, mais celles-ci sont habituellement coûteuses et exigent beaucoup de temps. Une alternative suggérée par l'agence réglementaire Food and Drug Administration est l'utilisation d'outils permettant l'analyse en continu, dont entre autres les outils spectroscopiques comme le proche infrarouge par exemple. Une des applications de cette technologie est le suivi de l'humidité d'une poudre pharmaceutique lors de son séchage dans un lit fluidisé. Une sonde à réflectance, qui est attachée au spectromètre, est insérée dans le bol du lit fluidisé et des spectres sont acquis tout au long du procédé. L'interprétation des spectres obtenus nécessite toutefois l'application de l'analyse multivariée et l'utilisation de plusieurs tests statistiques. Une courbe de calibration peut ainsi être construite et être utilisée au lieu des tests réalisés au laboratoire. Néanmoins, les paramètres critiques du spectromètre peuvent influencer la performance de la courbe de calibration bâtie. Par ailleurs, les articles publiés sur le sujet d'intérêt mentionnent que l'emplacement de la sonde, c'est-à-dire son angle et sa hauteur dans le bol, est critique pour l'obtention d'une acquisition spectrale de qualité. Or, aucun article n'a été publié sur la détermination de cet emplacement optimal et aussi sur le type d'embout à utiliser pour s'assurer d'une bonne lecture spectrale. Tout d'abord, des courbes de calibration ont été construites pour deux produits différents, soient un supplément vitaminé et un anti-inflammatoire. La méthodologie utilisée pour ces modélisations chimiométriques était innovatrice, car seulement des échantillons provenant de l'usine étaient utilisés et les spectres acquis avec le spectromètre étaient répliqués pour chaque échantillon collecté. Par la suite, un embout permettant une acquisition spectrale de qualité a été conçu et le nombre moyen de scans/spectre a été déterminé. En ayant fixé ces deux paramètres, la performance de la courbe de calibration construite pour le supplément vitaminé a été évaluée dans le lit fluidisé dans un environnement de production. Enfin, des plans d'expériences réalisés dans le lit fluidisé au laboratoire avec les deux produits ont permis d'évaluer l'effet des paramètres critiques du spectromètre sur les performances des courbes de calibration. Les tests statistiques effectués sur les deux courbes de calibration montrent de bonnes corrélations entre les prédictions par le spectromètre et les valeurs de référence. Par ailleurs, les essais réalisés dans un environnement de production ont démontré qu'un embout comportant une cavité permettant à la poudre de se stabiliser ainsi qu'un faible nombre de scans/spectre assurent une acquisition spectrale de qualité. Uniquement la courbe de calibration construite pour le supplément vitaminé a été évaluée dans un environnement de production ; le modèle chimiométrique réussit à bien prédire la fin de séchage. Enfin, les conclusions obtenues suite aux plans d'expériences réalisés dans le lit fluidisé au laboratoire ne peuvent pas être généralisées à différents produits. Il a aussi été démontré qu'il est difficile d'acquérir des spectres en mode dynamique et d'interpréter les résultats associés aux réplicas d'essais. Des travaux futurs sont suggérés pour investiguer certaines questions suscitées par cette recherche.

Thesis (PhD)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Concrete evidence of dryland salinity was observed in the Berg River catchment in the Western Cape Province of South Africa. Soil salinization is a global land degradation hazard that negatively affects the productivity of soils. Timely and accurate detection of soil salinity is crucial for soil salinity monitoring and mitigation. It would be restrictive in terms of costs to use traditional wet chemistry methods to detect and monitor soil salinity in the entire Berg River catchment. The goal of this study was to investigate less tedious, accurate and cost effective techniques for better monitoring. Firstly, hyperspectral remote sensing (HRS) techniques that can best predict electrical conductivity (EC) in the soil using individual bands, a unique normalized difference soil salinity index (NDSI), partial least squares regression (PLSR) and bagging PLSR were investigated. Spectral reflectance of dry soil samples was measured using an analytical spectral device FieldSpec spectrometer in a darkroom. Soil salinity predictive models were computed using a training dataset (n = 63). An independent validation dataset (n = 32) was used to validate the models. Also, field-based regression predictive models for EC, pH, soluble Ca, Mg, Na, Cl and SO4 were developed using soil samples (n = 23) collected in the Sandspruit catchment. These soil samples were not ground or sieved and the spectra were measured using the sun as a source of energy to emulate field conditions. Secondly, the value of NIR spectroscopy for the prediction of EC, pH, soluble Ca, Mg, Na, Cl, and SO4 was evaluated using 49 soil samples. Spectral reflectance of dry soil samples was measured using the Bruker multipurpose analyser spectrometer. “Leave one out” cross validation (LOOCV) was used to calibrate PLSR predictive models for EC, pH, soluble Ca, Mg, Na, Cl, and SO4. The models were validated using R2, root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD) and the ratio of prediction to interquartile distance (RPIQ). Thirdly, owing to the suitability of land components to map soil properties, the value of digital elevation models (DEMs) to delineate accurate land components was investigated. Land components extracted from the second version of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM (GEOEYE DEM) derived from GeoEye stereo-images were compared. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The land components were visually inspected and quantitatively analysed using the slope gradient standard deviation measure and the mean slope gradient local variance ratio for accuracy. Fourthly, the spatial accuracy of hydrological parameters (streamlines and catchment boundaries) delineated from the 5-m resolution SUDEM (L1 and L2), the 30-m ASTER GDEM2 and the 90-m SRTM was evaluated. Reference catchment boundary and streamlines were generated from the 1.5-m GEOEYE DEM. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module for ArcGIS. Visual inspection, correctness index, a new Euclidean distance index and figure of merit index were used to validate the results. Finally, the value of terrain attributes to model soil salinity based on the EC of the soil and groundwater was investigated. Soil salinity regression predictive models were developed using CurveExpert software. In addition, stepwise multiple linear regression soil salinity predictive models based on annual evapotranspiration, the aridity index and terrain attributes were developed using Statgraphics software. The models were validated using R2, standard error and correlation coefficients. The models were also independently validated using groundwater hydro-census data covering the Sandspruit catchment. This study found that good predictions of soil salinity based on bagging PLSR using first derivative reflectance (R2 = 0.85), PLSR using untransformed reflectance (R2 = 0.70), a unique NDSI (R2 = 0.65) and the untransformed individual band at 2257 nm (R2 = 0.60) predictive models were achieved. Furthermore, it was established that reliable predictions of EC, pH, soluble Ca, Mg, Na, Cl and SO4 in the field are possible using first derivative reflectance. The R2 for EC, pH, soluble Ca, Mg, Na, Cl and SO4 predictive models are 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 and 0.58 respectively. Regarding NIR spectroscopy, validation R2 for all the PLSR predictive models ranged from 0.62 to 0.87. RPD values were greater than 1.5 for all the models and RMSECV ranged from 0.22 to 0.51. This study affirmed that NIR spectroscopy has the potential to be used as a quick, reliable and less expensive method for evaluating salt-affected soils. As regards hydrological parameters, the study concluded that valuable hydrological parameters can be derived from DEMs. A new Euclidean distance ratio was proved to be a reliable tool to compare raster data sets. Regarding land components, it was concluded that higher resolution DEMs are required for delineating meaningful land components. It seems probable that land components may improve salinity modelling using hydrological modelling and that they can be integrated with other data sets to map soil salinity more accurately at catchment level. In the case of terrain attributes, the study established that promising soil salinity predictions could be made based on slope, elevation, evapotranspiration and terrain wetness index (TWI). Stepwise multiple linear regressions soil salinity predictive model based on elevation, evapotranspiration and TWI yielded slightly more accurate prediction of soil salinity. Overall, the study showed that it is possible to enhance soil salinity monitoring using HRS, NIR spectroscopy, land components, hydrological parameters and terrain attributes. / AFRIKAANSE OPSOMMING: Konkrete bewyse van droëland sout is waargeneem in die Bergrivier opvanggebied in die Wes- Kaap van Suid-Afrika. Verbrakking van grond is 'n wêreldwye probleem wat ‘n negatiewe invloed op die produktiwiteit van grond kan hê. Tydige en akkurate herkenning van verandering in grond soutgehalte is ‘n noodsaaklike aksie vir voorkoming. Dit sou beperkend wees in terme van koste om konvensionele nat chemiese metodes te gebruik vir die opsporing en monitering daarvan in die hele Bergrivier opvanggebied. Die doel van hierdie studie was om ondersoek in te stel na minder tydsame, akkurate en koste-effektiewe tegnieke vir beter monitering. Eerstens, is hiperspektrale afstandswaarnemings (HRS) tegnieke wat die beste in staat is elektriese geleidingsvermoë (EG) in die grond te kan voorspel deur gebruik te maak van individuele bande, 'n unieke genormaliseerde grond soutindeks verskil (NDSI), parsiële kleinste kwadratiese regressie (PLSR) en afwyking in PLSR, is ondersoek. Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van 'n spektrale analitiese toestel: FieldSpec spektrometer in 'n donkerkamer. Voorspellings modelle vir grond soutgehalte is bereken met behulp van 'n toets datastel (n = 63). 'n onafhanklike validasie datastel (n = 32) is gebruik om die modelle te evalueer. Daarbenewens is veld-gebaseerde regressie voorspellings modelle vir EG, pH oplosbare Ca, Mg, Na, Cl and SO4 ontwikkel deur gebruik te maak van grondmonsters (n = 23) versamel in the Sandpruit opvangsgebied. Hierdie grondmonsters is nie gemaal of gesif nie en die spectra is gemeet deur gebruik te maak van die son as ‘n bron van energie om veld toestande na te boots. Tweedens, is die waarde van NIR spektroskopie vir die voorspelling van die EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 met behulp van 49 grondmonsters geëvalueer. Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van die Bruker NIR veeldoelige analiseerder . Kruisvalidering (LOOCV) is gebruik om PLSR voorspellings modelle vir EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 te kalibreer. Hierdie modelle is gevalideer: R2, wortel-gemiddelde-kwadraat fout kruisvalidering (RMSECV), verhouding van voorspellings afwyking (RPD) en die verhouding van die voorspelling se inter-kwartiel afstand (RPIQ). Derdens is land komponente gekarteer vanweë die nut daat van tov grondeienskappe, en die waarde van DEMs is ondersoek om akkurate land komponente af te baken. Land komponente uit die tweede weergawe van die 30 m gevorderde ruimte termiese emissie en refleksie radio globale DEM (ASTER GDEM2), die 90-m ruimtetuig radar topografie sending DEM (SRTM DEM), twee weergawes van die 5 m Universiteit van Stellenbosch DEMs (SUDEM L1 en L2) en 'n 5 m DEM (GEOEYE DEM) afgelei van GeoEye stereo-beelde, is vergelyk. Land komponente is afgebaken met behulp van helling, gradiënt en aspek afgeleides van elke DEM. Die land komponente is visueel geïnspekteer en kwantitatief ontleed met behulp van die helling gradiënt standaardafwyking te meet en die gemiddelde helling-gradiënt-plaaslike variansie verhouding vir akkuraatheid. Vierdens, is die ruimtelike akkuraatheid van hidrologiese parameters (stroomlyn en opvanggebied grense) geëvalueer soos afgelei vanaf die 5 m resolusie SUDEM (L1 en L2), die 30 m ASTER GDEM2 en die 90 m SRTM . Die verwysings opvanggebied grens en stroomlyn is gegenereer vanaf die 1,5-m GEOEYE DEM. Opvanggebied grense en stroomlyn uit die DEMs is bepaal deur gebruik te maak van die Arc Hydro module in ArcGIS. Visuele inspeksie, korrektheid indeks, 'n nuwe Euklidiese afstand indeks en die indikasie-van-meriete indeks is gebruik om die resultate te valideer. Laastens is die waarde van die terrein eienskappe om grond southalte te modeleer ondersoek, gebaseer op die EG van die grond en grondwater. Grond soutgehalte regressie voorspellings modelle is ontwikkel met behulp van CurveExpert sagteware. Verder, stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings modelle gebaseer op jaarlikse evapotranspirasie, die dorheids indeks en terrein eienskappe is ontwikkel met behulp van Statgraphics sagteware. Die modelle is gevalideer deur gebruik te maak van R2, standaardfout en korrelasiekoëffisiënte. Die modelle is ook onafhanklik bekragtig deur die gebruik van grondwater hidro-sensus-data wat die Sandspruit opvanggebied insluit. Hierdie studie het bevind dat 'n goeie voorspelling van grond soutgehalte gebaseer op uitsak PLSR met behulp van eerste orde afgeleide reflektansie (R2 = 0,85), PLSR deur gebruik te maak van ongetransformeerde reflektansie (R2 = 0,70), 'n unieke NDSI (R2 = 0,65) en die ongetransformeerde individuele band op 2257 nm (R2 = 0,60) voorspellings modelle verkry is. Verder is vasgestel dat betroubare voorspellings van die EG, pH, oplosbare Ca, Mg, Na, Cl en SO4 in die veld moontlik is met behulp van eerste afgeleide reflektansie. Die R2 van EG, pH, oplosbare Ca, Mg, Na, Cl en SO4 is 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 en 0.58 onderskeidelik. Ten opsigte van NIR spektroskopie het die validasie van R2 vir al die PLSR voorspellings modelle gewissel tussen 0,62-0,87. Die RPD waardes was groter as 1,5 vir al die modelle en RMSECV het gewissel tussen 0,22-0,51. Hierdie studie het bevestig dat NIR spektroskopie die potensiaal het om gebruik te word as 'n vinnige, betroubare en goedkoper metode vir die analise van soutgeaffekteerde gronde. T.o.v. hidrologiese parameters, het die studie tot die gevolgtrekking gekom dat waardevolle hidrologiese parameters afgelei kan word uit DEMs. 'n nuwe Euklidiese afstand verhouding is bevestig as 'n betroubare hulpmiddel om raster datastelle te vergelyk. Ten opsigte van grond komponente, is daar tot die gevolgtrekking gekom dat hoër resolusie DEMs nodig is vir die bepaling van sinvolle land komponente. Dit lyk waarskynlik dat die land komponent soutgehalte modellering hidrologiese modellering verbeter en dat hulle geïntegreer kan word met ander datastelle vir meer akkurate kaarte op opvangsgebied skaal. In die geval van die terrein eienskappe het, die studie vasgestel dat belowende grond soutgehalte voorspellings gemaak kan word gebaseer op helling, elevasie, evapotranspirasie en terrein natheid indeks (TWI). 'n stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings model wat gebaseer is op elevasie, evapotranspirasie en TWI het effens meer akkurate voorspellings van die grond soutgehalte gelewer. In geheel gesien, het die studie getoon dat dit moontlik is om grond soutgehalte monitering te verbeter met behulp van HRS, NIR spektroskopie, land komponente, hidrologiese parameters en terrein eienskappe. / The Agricultural Research Council (ARC), Water Research Commission and the National Research Foundation for funding.

Computer technologies have revolutionised the processing of information and the search for knowledge. With the ever increasing computational power, it is becoming possible to tackle new data analysis applications as diverse as mining the Internet resources, analysing drugs effects on the organism or assisting wardens with autonomous video detection techniques. Fundamentally, the principle of any data analysis task is to fit a model which encodes well the dependencies (or patterns) present in the data. However, the difficulty is precisely to define such proper model when data are noisy, dependencies are highly stochastic and there is no simple physical rule to represent them. The aim of this work is to discuss the principles, the advantages and weaknesses of the probabilistic modelling framework for data analysis. The main idea of the framework is to model dispersion of data as well as uncertainty about the model itself by probability distributions. Three data analysis tasks are presented and for each of them the discussion is based on experimental results from real datasets. The first task considers the problem of linear subspaces identification. We show how one can replace a Gaussian noise model by a Student-t noise to make the identification more robust to atypical samples and still keep the learning procedure simple. The second task is about regression applied more specifically to near-infrared spectroscopy datasets. We show how spectra should be pre-processed before entering the regression model. We then analyse the validity of the Bayesian model selection principle for this application (and in particular within the Gaussian Process formulation) and compare this principle to the resampling selection scheme. The final task considered is Collaborative Filtering which is related to applications such as recommendation for e-commerce and text mining. This task is illustrative of the way how intuitive considerations can guide the design of the model and the choice of the probability distributions appearing in it. We compare the intuitive approach with a simpler matrix factorisation approach.

A novel series of near-infrared fluorescent contrast agents was developed and characterized. Their physicochemical and optical properties were measured. By altering functional groups of cyanine fluorophores, the selective targeting of endocrine glands, exocrine glands, cartilage and bone using NIR fluorescence to visualize the targeted tissue has been reported. These agents have high specificity for tissue targeting inherent to the chemical structure of the fluorophore. After a single low-dose intravenous injection these agents have high specificity for tissue targeting inherent to the chemical structure of the fluorophore. The results lay the foundation for future improvements in optical imaging in endocrine surgery, tissue engineering, joint surgery, and cartilage-specific drug development.

Soil compaction is a widely spread problem in agricultural soils that has negative agronomic and environmental impacts. The former may lead to poor crop growth and yield, whereas the latter may lead to poor hydraulic properties of soils, and high risk to flooding, soil erosion and degradation. Therefore, the elimination of soil compaction must be done on regular bases. One of the main parameters to quantify soil compaction is soil bulk density (BD). Mapping of within field variation in soil BD will be a main requirement for within field management of soil compaction. The aim of this research was to develop a new approach for the measurement of soil BD as an indicator of soil compaction. The research relies on the fusion of data from visible and near infrared spectroscopy (vis-NIRS), to measure soil gravimetric moisture content (ω), with frequency domain reflectometry (FDR) data to measure soil volumetric moisture content (θv). The values of the estimated ω and θv, for the same undisturbed soil samples were collected from selected locations, textures, soil moisture contents and land use systems to derive soil BD. A total of 1013 samples were collected from 32 sites in the England and Wales. Two calibration techniques for vis-NIRS were evaluated, namely, partial least squares regression (PLSR) and artificial neural networks (ANN). ThetaProbe calibration was performed using the general formula (GF), soil specific calibration (SSC), the output voltage (OV) and artificial neural networks (ANN). ANN analyses for both ω and θv properties were based either on a single input variable or multiple input variables (data fusion). Effects of texture, moisture content, and land use on the prediction accuracy on ω, θv and BD were evaluated to arrive at the best experimental conditions for the measurement of BD with the proposed new system. A prototype was developed and tested under laboratory conditions and implemented in-situ for mapping of ω, θv and BD. When using the entire dataset (general data set), results proved that high measurement accuracy can be obtained for ω and θv with PLSR and the best performing traditional calibration method of the ThetaProbe with R2 values of 0.91 and 0.97, and root mean square error of prediction (RMSEp) of 0.027 g g-1 and 0.019 cm3 cm-3, respectively. However, the ANN – data fusion method resulted in improved accuracy (R2 = 0.98 and RMSEp = 0.014 g g-1 and 0.015 cm3 cm-3, respectively). This data fusion approach gave the best accuracy for BD assessment when only vis-NIRS spectra and ThetaProbe V were used as an input data (R2 = 0.81 and RMSEp = 0.095 g cm-3). The moisture level (L) impact on BD prediction revealed that the accuracy improved with soil moisture increasing, with RMSEp values of 0.081, 0.068 and 0.061 g cm-3, for average ω of 0.11, 0.20 and 0.28 g g-1, respectively. The influence of soil texture was discussed in relation with the clay content in %. It was found that clay positively affected vis-NIRS accuracy for ω measurement and no obvious impact on the dielectric sensor readings was observed, hence, no clear influence of the soil textures on the accuracy of BD prediction. But, RMSEp values of BD assessment ranged from 0.046 to 0.115 g cm-3. The land use effect of BD prediction showed measurement of grassland soils are more accurate compared to arable land soils, with RMSEp values of 0.083 and 0.097 g cm-3, respectively. The prototype measuring system showed moderate accuracy during the laboratory test and encouraging precision of measuring soil BD in the field test, with RMSEp of 0.077 and 0.104 g cm-3 of measurement for arable land and grassland soils, respectively. Further development of the prototype measuring system expected to improve prediction accuracy of soil BD. It can be concluded that BD can be measured accurately by combining the vis-NIRS and FDR techniques based on an ANN-data fusion approach.

The purpose of this thesis work was to investigate possible in-, on- or at-line methods to determine the degree of oxidation in dialdehyde cellulose. Several technologies were reviewed which led to a feasibility study into a possible on-line or at-line method using near infrared spectroscopy for determining the degree of oxidation in wet dialdehyde cellulose. A calibration model was built using the near infrared spectra of 19 samples created from kraft pulp with a degree of oxidation between 0 and 52.1 %. The obtained model uses five significant principal components and has a goodness of fit (R2) of 0.998 and a goodness ofprediction (Q2) of 0.991. The first principal component describes the degree of oxidation and the second the water content. A validation set of six samples was used to test the model and the predicted values resulted in a root mean square error of prediction of 0.85 in comparison with the reference method which had a pooled standard deviation of 0.69.