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.

Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed Molecular Factor Computing (MFC) based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument.

Carbon nanotubes (CNTs) have special characteristics that made them good agents especially for biomedical applications. In this study, Fe3O4 nanoparticles were incorporated onto the surface of CNTs followed by polyethylene glycol (PEG) grafting forming CNTs-Fe3O4-PEG hybrids. The PEGylation improves their biocompatibility, water solubility, and increases blood circulation. CNTs-Fe3O4-PEG was used as T2-contrat agent for magnetic resonance imaging (MRI). In addition, doxorubicin (DOX) was loaded onto CNTs-Fe3O4-PEG. The release of DOX from DOX-loaded CNTs-Fe3O4-PEG was tested under different pH conditions (7.4, 6.3 and 5.2). The release increased at acidic pH compared to neutral pH, which shows the sensitivity of the system to pH change. Triggering the release by Near Infra-Red (NIR) irradiation was furthermore investigated. The release increased after irradiation with NIR compared to control sample. These result prove that the designed system can be triggered by an internal stimuli (pH) and external stimuli (NIR irradiation) making it a promising candidate to be used for biomedical applications.

O presente trabalho apresenta proposições para seleção de variáveis em avaliações sensoriais descritivas e de espectro infravermelho que contribuam com a indústria de alimentos e química através da utilização de métodos de análise multivariada. Desta forma, os objetivos desta tese são: (i) Estudar as principais técnicas de análise multivariada de dados, como são comumente organizadas e como podem contribuir no processo de seleção de variáveis; (ii) Identificar e estruturar técnicas de análise multivariada de dados de forma a construir um método que reduza o número de variáveis necessárias para fins de caracterização, classificação e predição dos produtos; (iii) Reduzir a lista de variáveis/atributos, selecionando aqueles relevantes e não redundantes, reduzindo o tempo de execução e a fadiga imposta aos membros de um painel em avaliações sensoriais; (iv) Validar o método proposto utilizando dados reais; e (v) Comparar diferentes abordagens de análise sensorial voltadas ao desenvolvimento de novos produtos. Os métodos desenvolvidos foram avaliados através da aplicação de estudos de caso, em exemplos com dados reais. Os métodos sugeridos variam com as características dos dados analisados, dados altamente multicolineares ou não e, com e sem variável dependente (variável de resposta). Os métodos apresentam bom desempenho, conduzindo a uma redução significativa no número de variáveis e apresentando índices de adequação de ajuste dos modelos ou acurácia satisfatórios quando comparados aos obtidos mediante retenção da totalidade das variáveis ou comparados a outros métodos dispostos na literatura. Conclui-se que os métodos propostos são adequados para a seleção de variáveis sensoriais e de espectro infravermelho. / This dissertation presents propositions for variable selection in data from descriptive sensory evaluations and near-infrared (NIR) spectrum analyses, based on multivariate analysis methods. There are five objectives here: (i) review the main multivariate analysis techniques, their relationships and potential use in variable selection procedures; (ii) propose a variable selection method based on the techniques in (i) that allows product prediction, classification, and description; (iii) reduce the list of variables/attributes to be analyzed in sensory panels identifying those relevant and non-redundant, such that the time to collect panel data and the fatigue imposed on panelists is minimized; (iv) validate methodological propositions using real life data; and (v) compare different sensory analysis approaches used in new product development. Proposed methods were evaluated through case studies, and vary according to characteristics in the datasets analyzed (data with different degrees of multicollinearity, presenting or not dependent variables). All methods presented good performance leading to significant reduction in the number of variables in the datasets, and leading to models with better adequacy of fit. We conclude that the methods are suitable for datasets from descriptive sensory evaluations and NIR analyses.

The sensitivity of biological studies performed between 190 and 650 nm is greatly reduced due to the autofluorescence of biomolecules and impurities in this region. Therefore, the enhanced signal-to-noise ratios encountered at longer wavelengths makes biological analysis within the near infrared (NIR) region from 650 nm to 1100 nm far more advantageous. This dissertation describes the noncovalent binding interactions of near-infrared (NIR) carbocyanine dyes with human serum albumin (HSA) and poly-L-lysine (PLL) using UV-Vis/NIR absorption spectroscopy, emission spectroscopy, circular dichroism (CD), and fluorescence detected circular dichroism (FDCD). The optical spectroscopy methods used in this work are described in detail in Chapter 1. The various applications of NIR dyes in protein analysis are introduced in Chapter 2. In general, the sensitivity of cyanines to the polarity of their local environment makes them quite suitable for protein labeling schemes. In aqueous media, cyanines have a high propensity for self-association. Yet in the hydrophobic binding sites of globular proteins, these aggregates often dissipate. Absorption and emission spectroscopy can be utilized to observe the differential spectral properties of monomer, intra-molecular and intermolecular aggregates. In Chapter 3, the photophysical properties of bis(cyanine) NIR dyes containing di-, tri-, and tetraethylene glycol linkers were each examined in the presence of HSA are discussed. Variations in chain length as well as probe flexibility were demonstrated through distinct differences in absorption and emission spectra. The observed changes in the spectral properties of the NIR dyes in the presence and absence of HSA were correlated to the physical parameters of the probes' local environment (i.e., protein binding sites and self-association). All three bis-cyanines examined exhibited enhanced fluorescence in the presence of HSA. The bis-cyanine dye containing the tri(ethylene glycol) spacer allowed for a complete overlap of the benzene rings, to form π-π interactions which were observed as intra-molecular H-aggregate bands. The dye exhibited no fluorescence in buffer, owing to the H-aggregation observed in the absorption data. In the presence of HSA, the intra-molecular dimers were disrupted and fluorescence was then detected. The "cut-on" fluorescence displayed by the dye in the presence of HSA made it ideal for noncovalent labeling applications. The utility of several NIR dyes for use as secondary structural probes was investigated in Chapter 4. NIR dyes were screened thoroughly using UV-Vis/NIR absorption spectroscopy dyes with spectral properties which were sensitive to protein secondary structure models of such as PLL in basic solution. Two NIR dyes were found to be quite sensitive to the structural features of uncharged α- and β-PLL. The chiral discrimination of these probes for basic protein secondary structures was also evaluated through CD measurements within the NIR probes' absorption bands.

O presente trabalho apresenta proposições para seleção de variáveis em avaliações sensoriais descritivas e de espectro infravermelho que contribuam com a indústria de alimentos e química através da utilização de métodos de análise multivariada. Desta forma, os objetivos desta tese são: (i) Estudar as principais técnicas de análise multivariada de dados, como são comumente organizadas e como podem contribuir no processo de seleção de variáveis; (ii) Identificar e estruturar técnicas de análise multivariada de dados de forma a construir um método que reduza o número de variáveis necessárias para fins de caracterização, classificação e predição dos produtos; (iii) Reduzir a lista de variáveis/atributos, selecionando aqueles relevantes e não redundantes, reduzindo o tempo de execução e a fadiga imposta aos membros de um painel em avaliações sensoriais; (iv) Validar o método proposto utilizando dados reais; e (v) Comparar diferentes abordagens de análise sensorial voltadas ao desenvolvimento de novos produtos. Os métodos desenvolvidos foram avaliados através da aplicação de estudos de caso, em exemplos com dados reais. Os métodos sugeridos variam com as características dos dados analisados, dados altamente multicolineares ou não e, com e sem variável dependente (variável de resposta). Os métodos apresentam bom desempenho, conduzindo a uma redução significativa no número de variáveis e apresentando índices de adequação de ajuste dos modelos ou acurácia satisfatórios quando comparados aos obtidos mediante retenção da totalidade das variáveis ou comparados a outros métodos dispostos na literatura. Conclui-se que os métodos propostos são adequados para a seleção de variáveis sensoriais e de espectro infravermelho. / This dissertation presents propositions for variable selection in data from descriptive sensory evaluations and near-infrared (NIR) spectrum analyses, based on multivariate analysis methods. There are five objectives here: (i) review the main multivariate analysis techniques, their relationships and potential use in variable selection procedures; (ii) propose a variable selection method based on the techniques in (i) that allows product prediction, classification, and description; (iii) reduce the list of variables/attributes to be analyzed in sensory panels identifying those relevant and non-redundant, such that the time to collect panel data and the fatigue imposed on panelists is minimized; (iv) validate methodological propositions using real life data; and (v) compare different sensory analysis approaches used in new product development. Proposed methods were evaluated through case studies, and vary according to characteristics in the datasets analyzed (data with different degrees of multicollinearity, presenting or not dependent variables). All methods presented good performance leading to significant reduction in the number of variables in the datasets, and leading to models with better adequacy of fit. We conclude that the methods are suitable for datasets from descriptive sensory evaluations and NIR analyses.

O presente trabalho apresenta proposições para seleção de variáveis em avaliações sensoriais descritivas e de espectro infravermelho que contribuam com a indústria de alimentos e química através da utilização de métodos de análise multivariada. Desta forma, os objetivos desta tese são: (i) Estudar as principais técnicas de análise multivariada de dados, como são comumente organizadas e como podem contribuir no processo de seleção de variáveis; (ii) Identificar e estruturar técnicas de análise multivariada de dados de forma a construir um método que reduza o número de variáveis necessárias para fins de caracterização, classificação e predição dos produtos; (iii) Reduzir a lista de variáveis/atributos, selecionando aqueles relevantes e não redundantes, reduzindo o tempo de execução e a fadiga imposta aos membros de um painel em avaliações sensoriais; (iv) Validar o método proposto utilizando dados reais; e (v) Comparar diferentes abordagens de análise sensorial voltadas ao desenvolvimento de novos produtos. Os métodos desenvolvidos foram avaliados através da aplicação de estudos de caso, em exemplos com dados reais. Os métodos sugeridos variam com as características dos dados analisados, dados altamente multicolineares ou não e, com e sem variável dependente (variável de resposta). Os métodos apresentam bom desempenho, conduzindo a uma redução significativa no número de variáveis e apresentando índices de adequação de ajuste dos modelos ou acurácia satisfatórios quando comparados aos obtidos mediante retenção da totalidade das variáveis ou comparados a outros métodos dispostos na literatura. Conclui-se que os métodos propostos são adequados para a seleção de variáveis sensoriais e de espectro infravermelho. / This dissertation presents propositions for variable selection in data from descriptive sensory evaluations and near-infrared (NIR) spectrum analyses, based on multivariate analysis methods. There are five objectives here: (i) review the main multivariate analysis techniques, their relationships and potential use in variable selection procedures; (ii) propose a variable selection method based on the techniques in (i) that allows product prediction, classification, and description; (iii) reduce the list of variables/attributes to be analyzed in sensory panels identifying those relevant and non-redundant, such that the time to collect panel data and the fatigue imposed on panelists is minimized; (iv) validate methodological propositions using real life data; and (v) compare different sensory analysis approaches used in new product development. Proposed methods were evaluated through case studies, and vary according to characteristics in the datasets analyzed (data with different degrees of multicollinearity, presenting or not dependent variables). All methods presented good performance leading to significant reduction in the number of variables in the datasets, and leading to models with better adequacy of fit. We conclude that the methods are suitable for datasets from descriptive sensory evaluations and NIR analyses.

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.

Made available in DSpace on 2014-12-17T15:41:56Z (GMT). No. of bitstreams: 1 AlessandraMC_DISSERT.pdf: 3533058 bytes, checksum: de92ff683335d9aa6c119013acb5b5d0 (MD5) Previous issue date: 2011-08-15 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / In this work calibration models were constructed to determine the content of total lipids and moisture in powdered milk samples. For this, used the near-infrared spectroscopy by diffuse reflectance, combined with multivariate calibration. Initially, the spectral data were submitted to correction of multiplicative light scattering (MSC) and Savitzsky-Golay smoothing. Then, the samples were divided into subgroups by application of hierarchical clustering analysis of the classes (HCA) and Ward Linkage criterion. Thus, it became possible to build regression models by partial least squares (PLS) that allowed the calibration and prediction of the content total lipid and moisture, based on the values obtained by the reference methods of Soxhlet and 105 ? C, respectively . Therefore, conclude that the NIR had a good performance for the quantification of samples of powdered milk, mainly by minimizing the analysis time, not destruction of the samples and not waste. Prediction models for determination of total lipids correlated (R) of 0.9955, RMSEP of 0.8952, therefore the average error between the Soxhlet and NIR was ? 0.70%, while the model prediction to content moisture correlated (R) of 0.9184, RMSEP, 0.3778 and error of ? 0.76% / Neste trabalho foram constru?dos modelos de calibra??o para determinar os teores de lip?dios totais e umidade em amostras de leite em p?. Para isso, utilizou-se a espectroscopia no infravermelho pr?ximo por reflect?ncia difusa, aliado ? calibra??o multivariada. Inicialmente, os dados espectrais foram submetidos ? corre??o multiplicativa do espalhamento da luz (MSC) e alisamento de Savitzsky-Golay. Em seguida, as amostras foram divididas em subgrupos por aplica??o da an?lise por agrupamento hier?rquico das classes (HCA) e crit?rio de Ward Linkage. Desta forma, tornou-se poss?vel construir modelos de regress?o por m?nimos quadrados parciais (PLS) que permitiu a calibra??o e previs?o dos teores de lip?dios e umidade, com base nos valores obtidos por m?todos de refer?ncia de Soxhlet e secagem a 105 ? C, respectivamente. Portanto, conclui-se que o NIR apresentou um bom desempenho para quantifica??o de amostras de leite em p?, principalmente pela minimiza??o do tempo das an?lises, n?o destrui??o das amostras e n?o gera??o de res?duos. Os modelos de previs?o para determina??o de lip?dios totais apresentaram correla??o (R) de 0,9955, RMSEP de 0,8952, por conseguinte, o erro m?dio entre o Soxhlet e o NIR foi ? 0,70%, enquanto o modelo de previs?o para teor de umidade apresentou correla??o (R) de 0,9184, RMSEP, 0,3778 e erro de ? 0,76%

In der vorliegenden Arbeit werden verschiedene Spektrometer für die Analyse von Gasen bzw. Gasgemischen vorgestellt und deren Design, Aufbau, Charakterisierung und Optimierung beschrieben. Das Resultat der Optimierung und Weiterentwicklungen ist ein spektral breitbandiges Cavity-Ring-Down-Spektrometer (CRD-Spektrometer). Ausgangspunkt der hier vorgestellten Arbeit ist ein Spektrometer auf Basis klassischer Absorptionsspektroskopie in einer Multireflexionszelle. Für dieses Spektrometer wurde als Strahlquelle ein Superkontinuumlaser verwendet. Der Vorteil dieses Spektrometers liegt in seiner Kompaktheit. Mit diesem Spektrometer wurden Absorptionsspektren von mehreren Reingasen und einem Gasgemisch über einen Wellenlängenbereich von 1500 nm – 1700 nm aufgenommen. Der qualitative Vergleich mit zu erwartenden Spektren, welche auf der HITRAN-Datenbank basieren, zeigte eine gute Übereinstimmung. Die quantitative Interpretierbarkeit der Daten war jedoch stark eingeschränkt aufgrund des hohen zufälligen und systematischen Fehlers der Messungen. Als Konsequenz aus der als nicht zufriedenstellend bewerteten quantitativen Interpretierbarkeit der Daten wurde eine alternative Messmethode gesucht, welche eine höhere Sensitivität und Genauigkeit ermöglicht. Die Wahl fiel auf die Cavity-Ring-Down-Spektroskopie, eine resonatorgestützte Variante der Absorptionsspektroskopie. Wesentliche Vorteile dieser Technik sind a) die Unabhängigkeit von Leistungsschwankungen der Strahlquelle, b) ein effektiver Absorptionsweg von bis zu mehreren Kilometern, welcher sich unmittelbar auf die Sensitivität der Messungen auswirkt, c) die Ermittlung absoluter Absorberkonzentrationen, ohne die Notwendigkeit einer Kalibrierung oder den Vergleich mit einer Referenzzelle und d) die Vernachlässigbarkeit von Absorptionen außerhalb des Resonators. Als notwendiger Zwischenschritt auf dem Weg zu einem breitbandigen CRD-Spektrometer wurde zunächst ein monochromatisches CRD-Spektrometer designt, aufgebaut und charakterisiert. Für die effektive Einkopplung von Strahlungsenergie in einen Resonator ist die Anpassung der Strahlparameter an die Mode des Resonators notwendig. Voraussetzung dieser Anpassung ist die Kenntnis der Strahlparameter, welche experimentell ermittelt wurden. Im Laufe des Aufbaus des Spektrometers ergab sich, dass trotz der Modenanpassung die Einkopplung der Strahlungsenergie in den Resonator gestört wurde. Daraufhin wurden systematisch mögliche Ursachen dieser Störung untersucht und das Spektrometer optimiert. Mit diesem optimierten Spektrometer wurden Spektren gemessen, welche sowohl qualitativ als auch quantitativ gut mit den zu erwartenden Spektren übereinstimmen. Als Nachweisgrenze dieses Spektrometers wurde ein Wert für den Absorptionskoeffizienten alpha von 10^-8 cm-1 bestimmt. Mit dem monochromatischen CRD-Spektrometer war es zudem möglich, isotopenspezifische Messungen durchzuführen. Für das breitbandige Spektrometer wurde als Strahlquelle eine ASE-Diode (amplified spontaneous emission) verwendet. Dabei handelt es sich um eine inkohärente Strahlquelle. Mittels Messungen nach dem Prinzip der Cavity-Enhanced-Absorptionsspektroskopie wurde die generelle Funktionalität des resonatorgestützten Spektrometers überprüft. Anschließend wurden die wellenlängenabhängigen Abklingsignale des leeren und des mit einem CO2-Luft-Gemisch gefüllten Resonators gemessen und ebenfalls mit den zu erwartenden Spektren verglichen. Qualitativ stimmen die experimentellen Spektren gut mit den zu erwartenden Spektren überein. Für die quantitative Interpretation der Daten wurde ein spezieller Algorithmus entwickelt, der die spektrale Auflösung des Systems berücksichtigt. Mit dem vorgestellten Spektrometer ist so die qualitative und quantitative Interpretation der Spektren möglich. Die Nachweisgrenze des breitbandigen Cavity-Ring-Down-Spektrometers wurde zu einem Wert von alpha = 8x10^-7 cm-1 bestimmt. Der systematischen und der zufällige Fehler der Messungen lagen bei Werten von ca. 1%. Bei diesem Spektrometer handelt es sich um einen Prototyp. Mittels Optimierung des Systems lassen sich sowohl der Wert der Nachweisgrenze als auch die Fehler der Messungen verbessern. / This thesis presents the design, set-up, characterisation and optimization of various spectrometers to be used for the analysis of gases and gas mixtures. The result of this optimization and its further development is a spectrally broadband cavity ring-down spectrometer (CRD spectrometer), which uses an incoherent light source that emits in the near-infrared. The starting point of the development was a spectrometer which is based on classic absorption spectroscopy inside a multipass cell. This spectrometer uses a supercontinuum laser as light source. The advantage of this spectrometer is its compactness. With this spectrometer, the spectra of various gases and a gas mixture were detected in the spectral range of 1500 nm to 1700 nm. The experimentally derived spectra are in good qualitative accordance to expected spectra based on the HITRAN database. Nevertheless, the qualitative interpretation of the data reveals significant systematic and random errors. As a consequence, a different spectroscopic approach was chosen. The method of choice was cavity ring-down spectroscopy. The advantages of this technique are a) the independence from power fluctuations of the light source, b) an effective absorption path length of up to several kilometres, c) absolute measurement of absorber concentration and d) independence of absorption outside of the cavity. As an important intermediate step on the way to the broadband CRD spectrometer, a monochromatic CRD spectrometer was designed, set up and characterised. To effectively couple light into the cavity, the beam parameters have to be matched to the cavity’s mode. Prerequisite of this mode matching is the knowledge of the beam parameters, which were determined experimentally. Despite this mode matching, the coupling of the light into the cavity turned out to be instable. The cause of that disturbance was systematically investigated, which let to an optimization of the system. The spectra measured with this optimized system were in very good qualitative and quantitative agreement with the expected spectra. The limit of detection of this spectrometer was determined to an absorption coefficient alpha of 10^-8 cm-1. Furthermore, isotope-selective measurements were performed. The light source of the broadband CRD spectrometer is an amplified spontaneous emission diode, which is an incoherent light source. The general functionality of the spectrometer was first tested by means of CEAS measurements (cavity enhanced absorption spectroscopy). Afterwards, the wavelength dependent ring-down signals of the empty cavity and the cavity filled with a CO2 air mixture were detected. The qualitative comparison with the expected data shows very good agreement. For the quantitative interpretation of the experimental data, a special algorithm was developed. Thereby the data measured with the presented spectrometer can be interpreted both qualitatively and quantitatively. The limit of detection of the broadband CRD spectrometer was determined to a value of alpha = 8x10^-7 cm-1. The systematic and the random error are in the range of 1 %. The presented spectrometer is a prototype. Therefore the systematic and random error will be improved by further optimization of the spectrometer.