Le but de ce travail est de quantifier le potentiel et les capacités de la technologie térahertz à contrôler des colis afin de détecter les menaces telles que les armes et les explosifs, sans avoir besoin d'ouvrir le colis.Dans cette étude, nous présentons la spectroscopie térahertz résolue en temps et l'imagerie multi-spectrale pour la détection des explosifs. Deux types d’explosifs, ainsi que leurs mélanges binaires sont analysés. En raison de la complexité de l'extraction des informations face à tels échantillons, trois outils de chimiométrie sont utilisés: l’analyse en composantes principales (ACP), l'analyse des moindres carrés partiels (PLS) et l'analyse des moindres carrés partiels discriminante (PLS-DA). Les méthodes sont appliquées sur des données spectrales térahertz et sur des images spectrales pour : (i) décrire un ensemble de données inconnues et identifier des similitudes entre les échantillons par l'ACP ; (ii) créer des classes, ensuite classer les échantillons inconnus par PLS-DA ; (iii) créer un modèle capable de prédire les concentrations d’un explosif, à l'état pur ou dans des mélanges, par PLS.Dans la deuxième partie de ce travail, nous présentons l'imagerie par les ondes millimétriques pour la détection d'armes dans les colis. Trois techniques d'imagerie différentes sont étudiées : l'imagerie passive, l’imagerie active par des ondes continues (CW) et l’imagerie active par modulation de fréquence (FMCW). Les performances, les avantages et les limitations de chacune de ces techniques, pour l’inspection de colis, sont présentés. En outre, la technique de reconstruction tomographique est appliquée à chacune de ces trois techniques, pour visualiser en 3D et inspecter les colis en volume. Dans cet ordre, un algorithme de tomographie spécial est développé en prenant en considération la propagation gaussienne de l'onde. / The aim of this work is to demonstrate the potential and capabilities of terahertz technology for parcels screening and inspection to detect threats such as weapons and explosives, without the need to open the parcel.In this study, we first present terahertz time-domain spectroscopy and spectral imaging for explosives detection. Two types of explosives as well as their binary mixture is analyzed. Due to the complexity of extracting information when facing such mixtures of samples, three chemometric tools are used: principal component analysis (PCA), partial least square analysis (PLS) and partial least squares-discriminant analysis (PLS-DA). The analyses are applied to terahertz spectral data and to spectral-images in order to: (i) describe a set of unknown data and identify similarities between samples by PCA; (ii) create a classification model and predict the belonging of unknown samples to each of the classes, by PLS-DA; (iii) create a model able to quantify and predict the explosive concentrations in a pure state or in mixtures, by PLS.The second part of this work focuses on millimeter wave imaging for weapon detection in parcels. Three different imaging techniques are studied: passive imaging, continuous wave (CW) active imaging and frequency modulated continuous wave (FMCW) active imaging. The performances, the advantages and the limitations of each of the three techniques, for parcel inspection, are exhibited. Moreover, computed tomography is applied to each of the three techniques to visualize data in 3D and inspect parcels in volume. Thus, a special tomography algorithm is developed by taking in consideration the Gaussian propagation of the wave.

O biodiesel vem se consolidando como combustível alternativo devido às suas vantagens e importância econômica. Conforme seu percentual no diesel comercial cresce, o monitoramento da qualidade se torna cada vez mais importante para uma comercialização segura. No entanto, a estabilidade oxidativa do biodiesel é inferior à do diesel fóssil, assim antioxidantes sintéticos, tais como BHA, BHT, PG e TBHQ, são adicionados para prevenir a degradação do mesmo, evitando danos ao sistema de combustão automotivo. Os métodos para avaliar a estabilidade oxidativa e a quantidade de antioxidantes são em geral demorados, requerem preparação amostral ou equipamentos de alto custo. Com o objetivo de contornar tais problemas, neste trabalho foi aplicada uma metodologia para análise direta de antioxidantes em amostras de biodiesel por meio de voltametria de pulso diferencial. Conjuntamente, foi estudada a viabilidade da associação dessa metodologia com técnicas quimiométricas para a determinação simultânea desses antioxidantes em misturas de antioxidantes em biodiesel. Para permitir uma análise direta, sem extração e pré-concentração dos analitos, o biodiesel foi diluído em meio etanólico. Medidas de voltametria de pulso diferencial para cada antioxidante individualmente mostraram relação linear entre as concentrações dos antioxidantes e a corrente de oxidação. Os limites de detecção individuais obtidos foram de 20,5 mg L-1 para BHA, 32,4 mg L-1 para BHT, 35,5 mg L-1 para PG e 26,5 mg L-1 para TBHQ. A modelagem quimiométrica foi aplicada por meio das ferramentas Mínimos Quadrados Clássico (CLS), Mínimos Quadrados Parciais (PLS), Redes Neuronais Artificiais (ANN), Componentes Principais-Redes Neuronais Artificiais (PC-ANN) e Árvore de Decisão-Redes Neuronais Artificiais (DT-ANN). O modelo construído por PLS se mostrou melhor quando comparado à modelagem por CLS. O modelo construído por ANN’s sem seleção de dados de entrada apresentou erros semelhantes ao PLS. Quando a redução da quantidade de dados de entrada foi aplicada em conjunto com ANN’s através de Análise por Componentes Principais (PCA) e DT, a aplicação de PCA levou a aumento de 10,2% no erro de predição, enquanto que na seleção por DT os erros de predição foram reduzidos em 8,5%. A determinação simultânea dos quatro compostos pelo modelo DT-ANN apresentou precisão satisfatória, com recuperação de 98% para BHA, 97% para BHT, 103% para PG e 100% para TBHQ, o que indica que a técnica analítica e a modelagem quimiométrica são viáveis e promissoras para aplicação no controle de qualidade do biodiesel, bem como em análises de monitoramento nas plantas industriais. / Biodiesel is becoming established as an alternative fuel because its advantages and economic importance. As the levels of biodiesel in commercial diesel grows, quality monitoring becomes increasingly important for safe marketing. However, the oxidative stability of biodiesel is smaller than that of fossil diesel and synthetic antioxidants such as BHA, BHT, PG and TBHQ are added to it in order to prevent its degradation, avoiding damage to the automotive combustion system. Methods for evaluating oxidative stability and the amount of antioxidants are usually time-consuming, require sample preparation or expensive equipment. In order to overcome such problems, in this work a methodology was applied for direct analysis of antioxidants in biodiesel samples by means of differential pulse voltammetry. The viability of associating this methodology with chemometric techniques was studied for the simultaneous determination of these antioxidants in biodiesel. To allow a direct analysis, without extraction and preconcentration of analytes, biodiesel was diluted in ethanolic medium. Differential pulse voltammetric measurements for each antioxidant individually showed a linear relationship between antioxidant concentrations and oxidation current. The individual detection limits were 20,5 mg L-1 for BHA, 32,4 mg L-1 for BHT, 35,5 mg L-1 for PG and 26,5 mg L-1 for TBHQ. The chemometric modeling was applied using the Classical Least Squares (CLS), Partial Least Squares (PLS), Artificial Neural Networks (ANN), Principal Component-Artificial Neural Networks (PC-ANN) and Decision Tree-Artificial Neural Networks (DT-ANN) techniques. The model constructed by PLS was better than that obtained with CLS. The model constructed by ANN’s without input selection presented similar deviations in comparison to PLS. When amount of input data reduction was applied together with ANNs through Principal Component Analysis (PCA) and DT, the PCA application led to a 10.2% increase in prediction error, whereas in the selection by DT prediction errors were reduced by 8,5%. The simultaneous determination of the four compounds by the DT-ANN model presented satisfactory accuracy with 98% recovery for BHA, 97% for BHT, 103% for PG and 100% for TBHQ, indicating that the analytical technique and the chemometric modeling are feasible and promising for application in biodiesel quality control, as well as in monitoring analyzes in the industrial plants.

Noninvasive glucose sensing has been studied widely. Near infrared (NIR) absorption spectroscopy and Raman scattering spectroscopy are proposed individually and combined as methods for glucose measurement in a three component sample matrix. In both techniques, the light transmits through human skin and a spectrum is collected. The research described in this thesis is like this. The use of individual NIR spectra data and individual Raman spectra data can give a good prediction ability of the partial least-squares (PLS) calibration model. Since the NIR and Raman spectroscopies have complementary nature of molecular vibrations, the research tried to prove the prediction ability of the PLS calibration model can be improved by combining NIR and Raman spectra data. Two approaches are investigated to ascertain the benefits of combining these spectral methods. First, NIR and Raman spectral data collected from a set of 60 samples concated and used to compute multivariate models based on PLS and net analyte signal (NAS) methods. The performance of models based on concated NIR-Raman spectra are compared to conventional models based on only NIR and only Raman spectra. The second strategy reported in this chapter is the simulated NIR and Raman spectra and computing PLS and NAS models by concating these simulated spectra. Spectral simulation permits systematic variations in noise levels. In both cases, various preprocessing methods are explored to find a suitable way to combine the different spectral types. The result from the real spectra data is that adding low signal-to-noise ratio (SNR) to high SNR spectra would make the calibration models worse. The result from the simulated spectra data is that with the same SNR and the same magnitude of the two spectra, the prediction ability of the calibration model can be improved.

The ability to measure glucose transcutaneously and noninvasively is an exciting prospect. Such a procedure will offer a painless way of glucose self-monitoring improving the lives of people with diabetes by lowering the barriers to optimal glycemic control. The noninvasive measurements involve collecting near-infrared spectra (4000–5000 cm-1; 2.0–2.5 µm) of skin with two optical fibers in a transmission geometry. Previous results indicate that repositioning of the fiber optic interface adversely affects both precision and accuracy of such measurements. Slight movements of the interface increase prediction errors more than 2.5–fold when performed with a stationary rat model. In this dissertation, the chemical heterogeneity of skin tissue is explored as a possible cause for the sensitivity of the measurement to the position of the optical interface. Rat and human skin tissues are mapped by using combination near infrared spectra the to provide distributions of the major components of skin: water, collagen type I protein, fat, keratin protein, and two scattering terms of constant and slope. On the basis of the measured heterogeneity, sets of rat and human skin spectra are simulated to investigate the impact of repositioning the fiber-optic interface. Glucose predictions are analyzed for each location of the interface for a series of partial least squares (PLS) calibration vectors established for different locations on the skin. Significant increases in the measurement errors are observed for the situation where the PLS calibration models are generated from spectra associated with one location of the interface while subsequent measurements are performed at slightly locations of the skin matrix. These increases in prediction errors match the 2.5–fold increase observed in vivo. The impact of replacing the spectrum of bovine fat with spectra of native fat for both rat and human skin samples is established. Principal component analysis (PCA) of the spectral residuals reveals that the magnitude of the spectral residuals and the effects of tissue fat content on the quality of the linear regression were decreased. The key implication of the research detailed in this dissertation is that chemical heterogeneity of skin tissue must be considered in multivariate models intended for noninvasive glucose measurements.

L'HexaazahexaNitroIsoWurtzitane (ou HNIW), dérivé hexanitré, encore appelé CL20, est une nouvelle molécule hautement énergétique, qui présente quatre formes polymorphes : α, β, γ et ε. La forme ε est la plus active et la commercialisation stipule de fournir un produit dont la teneur en forme ε soit supérieure à 95%, avec estimation des teneurs des autres formes ainsi que de l'impureté majoritaire (dérivé pentanitré). L'objet du présent travail est d'une part de caractériser les différentes formes polymorphes, et d'autre part de mettre au point, d'optimiser et de valider une méthode d'analyse quantitative du CL20 polymorphe par couplage spectro-métrie IRTF/chimiométrie, respectant les impératifs techniques et commerciaux et transférable sur site industriel. Les méthodes chimiométriques sont utilisées pour concevoir l'expérimentation et traiter l'information expérimentale nécessaire (étalonnage et validation).

<p>The synthesis and breakdown of sugars is one of the most important functions in Nature. Consequently, sugar structures are used both as energy storage and as building blocks to stabilise and protect the cell. The formation of these structures is performed by glycosyltransferases (GT), an enzyme group structurally conserved within all kingdoms. Until now, only two different folds have been discovered by crystallisation of GTs, i.e. GT-A and GT-B. A third fold family has however been proposed by fold predictions. In this thesis, a multivariate data analysis was successfully used in classifying and predicting both fold and reaction mechanism (inverting or retaining) of GTs. This method was also used to obtain information about the separating parameters for the reaction mechanism classification. This information could be traced back to the amino acid sequence. The method could as well be used to analyse and identify the properties of membrane binding regions of GTs, and subsequently distinguish soluble from membrane-associated enzymes. Most functionally characterised enzymes only use one substrate, synthesising one product. <i>Mycoplasma pneumoniae</i>, a common human pathogen with a small genome has only three proposed GTs. The bacterium was, however expected to have a greater number of GTs, due to its ability to make both glycolipids and capsule. Here we have determined the function of one of these enzymes, MPN483 and discovered its ability to both use different acceptors, and make elongated glycolipids with up to three galactose residues, with both DAG and ceramide as the base. Many of the synthesised glycolipids were also found to be immunogenic, hence showing their biological importance. The properties of lipids are known to be important for the function of a biological membrane. We have here shown that not only the charge but also the shape of the lipids are important for several protein mediated membrane processes in <i>Echerichia coli</i>, such as the function of the LacY.</p>

The amounts of data collected from each sample of e.g. chemical or biological materials have increased by orders of magnitude since the beginning of the 20th century. Furthermore, the number of ways to collect data from observations is also increasing. Such configurations with several massive data sets increase the demands on the methods used to analyse them. Methods that handle such data are called multiblock methods and they are the topic of this thesis. Data collected from advanced analytical instruments often contain variation from diverse mutually independent sources, which may confound observed patterns and hinder interpretation of latent variable models. For this reason, new methods have been developed that decompose the data matrices, placing variation from different sources of variation into separate parts. Such procedures are no longer merely pre-processing filters, as they initially were, but have become integral elements of model building and interpretation. One strain of such methods, called OPLS, has been particularly successful since it is easy to use, understand and interpret. This thesis describes the development of a new multiblock data analysis method called OnPLS, which extends the OPLS framework to the analysis of multiblock and path models with very general relationships between blocks in both rows and columns. OnPLS utilises OPLS to decompose sets of matrices, dividing each matrix into a globally joint part (a part shared with all the matrices it is connected to), several locally joint parts (parts shared with some, but not all, of the connected matrices) and a unique part that no other matrix shares. The OnPLS method was applied to several synthetic data sets and data sets of “real” measurements. For the synthetic data sets, where the results could be compared to known, true parameters, the method generated global multiblock (and path) models that were more similar to the true underlying structures compared to models without such decompositions. I.e. the globally joint, locally joint and unique models more closely resembled the corresponding true data. When applied to the real data sets, the OnPLS models revealed chemically or biologically relevant information in all kinds of variation, effectively increasing the interpretability since different kinds of variation are distinguished and separately analysed. OnPLS thus improves the quality of the models and facilitates better understanding of the data since it separates and separately analyses different kinds of variation. Each kind of variation is purer and less tainted by other kinds. OnPLS is therefore highly recommended to anyone engaged in multiblock or path model data analysis.

The synthesis and breakdown of sugars is one of the most important functions in Nature. Consequently, sugar structures are used both as energy storage and as building blocks to stabilise and protect the cell. The formation of these structures is performed by glycosyltransferases (GT), an enzyme group structurally conserved within all kingdoms. Until now, only two different folds have been discovered by crystallisation of GTs, i.e. GT-A and GT-B. A third fold family has however been proposed by fold predictions. In this thesis, a multivariate data analysis was successfully used in classifying and predicting both fold and reaction mechanism (inverting or retaining) of GTs. This method was also used to obtain information about the separating parameters for the reaction mechanism classification. This information could be traced back to the amino acid sequence. The method could as well be used to analyse and identify the properties of membrane binding regions of GTs, and subsequently distinguish soluble from membrane-associated enzymes. Most functionally characterised enzymes only use one substrate, synthesising one product. Mycoplasma pneumoniae, a common human pathogen with a small genome has only three proposed GTs. The bacterium was, however expected to have a greater number of GTs, due to its ability to make both glycolipids and capsule. Here we have determined the function of one of these enzymes, MPN483 and discovered its ability to both use different acceptors, and make elongated glycolipids with up to three galactose residues, with both DAG and ceramide as the base. Many of the synthesised glycolipids were also found to be immunogenic, hence showing their biological importance. The properties of lipids are known to be important for the function of a biological membrane. We have here shown that not only the charge but also the shape of the lipids are important for several protein mediated membrane processes in Echerichia coli, such as the function of the LacY.

La Ingeniería molecular se basa en el conocimiento de las características estereoelectrónicas que definen el reconocimiento molecular, que es el resultado de una complementariedad, tanto geométrica como electrónica, entre diferentes entidades moleculares. La importancia de las diferentes contribuciones electrostáticas nos permite realizar un estudio teórico de predicción de la reactividad y otras propiedades moleculares a partir de cálculos de potencial electrostático y de polarización moleculares. El presente trabajo se basa en el estudio de la utilización de los mapas de potencial electrostático y de potencial de polarización como descriptores moleculares. En primer lugar se realiza un estudio del efecto de la base y de la metodología empleada en el cálculo de propiedades eléctricas de primer y segundo orden. El análisis se lleva a cabo con las moléculas de cianuro de hidrógeno, formaldehído y urea. Las bases utilizadas son del tipo doble zeta estándar, a las cuales se han añadido funciones de polarización y difusas. En particular, se han utilizado la base doble zeta 6-31G(d), les bases doble zeta aumentadas con uno o dos conjuntos de funciones de polarización : 6-31G(d,p) , 6-31G(2d,2p) y también se ha utilizado la base 6-311G++(2d,2p), que incluye funciones difusas. Los diferentes niveles de cálculo utilizan metodologías Hartree-Fock, MÆller-Plesset de segundo y cuarto orden y teoría del funcional de la densidad (DFT) : SCF, MP2, MP4, BLYP i B3LYP. Se analiza el efecto de los diferentes conjuntos de base a la contribución de la polarización a la energía de interacción, calculando para cada sistema propiedades de primer orden, como son los momentos dipolares y los momentos cuadrupolares, y propiedades de segundo orden, como la polarizabilidad y hiperpolarizabilidad moleculares. Seguidamente se evalúa el efecto de la base y el método de cálculo en la obtención de potenciales electrostáticos y de polarización moleculares. Se realiza un estudio comparativo de los mapas calculados con diferentes bases y metodologías, en concreto un estudio de la distribución espacial y un análisis de correlación entre las diferentes bases y metodologías. Un análisis de los mapas de polarización molecular a partir del cálculo de las diferencias de polarización relativas y las desviaciones estándar correspondientes nos permite un estudio comparativo de las diferentes metodologías y bases utilizadas. En particular se realiza un análisis comparativo entre diferentes métodos de cálculo con la base 6-311G++(2d,2p), tomando como referencia el cálculo MP4. Finalmente, se utilizan los mapas de potencial electrostático, de polarización y de interacción para el análisis de las características de reconocimiento molecular de un conjunto de compuestos bioactivos, a fin de analizar la importancia de la contribución de la polarización. Por este motivo, se elige para el estudio un conjunto de moléculas con una alta polarizabilidad, y en concreto, dos familias de compuestos con abundantes átomos de cloro y con una actividad tóxica definida, que forman parte de los grupos de dioxinas y furanos. Para ello se realiza el estudio de la inclusión de la polarización molecular como descriptor en la predicción de la actividad biológica de dioxinas y furanos, realizando el cálculo de potenciales electrostáticos y de polarización, un análisis de los mapas de potencial, y definiendo las principales zonas de interacción electrostática y de polarización molecular a partir de cálculos de componentes principales (PCA), así como la predicción de la actividad biológica en base a un estudio realizado mediante cálculos de mínimos cuadrados parciales (PLS). / Molecular engineering is based on the knowledge of the stereoelectronic features that define the molecular recognition, which is the result of the complementarity of geometric and electronic features between two different molecular entities. The importance of different electrostatic contributions allows us to make a theoretical prediction of molecular properties, from calculations of electrostatic potential and molecular polarization. This work is based on the study of the use of maps of electrostatic potential and polarization potential as molecular descriptors. First there is a study of the effect of the basis set and the methodology used in the calculation of first and second order electrical properties. The analysis was carried out with the molecules of hydrogen cyanide, formaldehyde and urea. The base sets used are the standard double-zeta, to which were added polarization and diffuse functions. In particular, we have used the double zeta basis 6-31G (d), double zeta basis augmented with one or two sets of polarization functions: 6-31G (d, p), 6-31G (2d, 2p) and also used the base 6-311G + + (2d, 2p), which includes diffuse functions. The different levels of calculation methodologies used Hartree-Fock, Moller-Plesset second and fourth order and the theory of density functional (DFT): SCF, MP2, MP4, BLYP B3LYP i. We analyze the effect of different sets based on the contribution of polarization to the interaction energy, calculated for each system of first order properties, such as dipolar moments and quadrupolar moments, and second order properties such as polarizability and molecular hyperpolarizability. Further, the effect of the basis and method on the calculation of the electrostatic potential and molecular polarization potentials is evaluated. For this purpose we performed a comparative study of the maps calculated with different basis sets and methodologies, in particular a study of the spatial distribution and correlation analysis between the different data bases and methodologies was done. An analysis of molecular polarization maps by calculating the differences in polarization and relative standard deviation allows for a comparative study of different methodologies and bases used. Specifically, a comparative analysis between different methods of calculating the base 6-311G + + (2d, 2p), calculated by reference to the MP4 was done. We use maps of electrostatic, polarization and interaction potentials for the analysis of molecular recognition features of a set of bioactive compounds, to discuss the importance of the contribution of polarization. For this reason, we choose to study a set of molecules with high polarizability, specifically, two families of compounds with heavy atoms of chlorine and defined toxic activity, which are part of groups of dioxins and furans. A study of the polarization maps as indicators of biological activity of dioxins and furans, based on the best methodology is done. The inclusion of polarization as a molecular descriptor for predicting biological activity of dioxins and furans was studied from the calculation of electrostatic potentials and polarization, an analysis of the potential maps, and defining the main areas of interaction molecular electrostatic and polarization interaction from calculations of principal components (PCA) and the prediction of biological activity based on a study by calculation of partial least squares (PLS).

Cervical Cancer is the second most common type of cancer in women worldwide (500,000 cases/year) and one of the leading causes of cancer-related mortality in women in developing countries (230,000 cases/year). The Spectrx LightTouch™ device uses light to detect chemical and structural changes in cervical tissue. Light responds differently when exposed to normal cells and cancerous cells. The purpose of this research is to find the best model that can be used to diagnose the early cervical cancerous conditions. To achieve this goal, we first tried to reduce the number of variables. We use statistical and non-statistical methods to search for useful explanatory variables. Partial Least Square, Logistic Regression, CART, MARS, SVM have been used to build models. Bootstrap was adopted to estimate the threshold of PLS model. Comparison of the results indicates that PLS produces relatively better model in terms of the performances and to control over model threshold.