Resolução multivariada de curvas com mínimos quadrados alternantes: descrição, funcionamento e aplicações

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) is a resolution method that has been efficiently applied in many different fields, such as process analysis, environmental data and, more recently, hyperspectral image analysis. When applied to second order data (or to three-way data) arrays, recovery of the underlying basis vectors in both measurement orders (i.e. signal and concentration orders) from the data matrix can be achieved without ambiguities if the trilinear model constraint is considered during the ALS optimization. This work summarizes different protocols of MCR-ALS application, presenting a case study: near-infrared image spectroscopy.

VIS-NIR SPECTROMETRY, SOIL PHOSPHATE EXTRACTION METHODS AND INTERACTIONS OF SOIL ATTRIBUTES

The objective of this study was to evaluate the relationships between the spectra in the Vis-NIR range and the soil P concentrations obtained from the PM and Prem extraction methods as well as the effects of these relationships on the construction of models predicting P concentration in Oxisols. Soil samples' spectra and their PM and Prem extraction solutions were determined for the Vis-NIR region between 400 and 2500 nm. Mineralogy and/or organic matter content act as primary attributes allowing correlation of these soil phosphorus fractions with the spectra, mainly at wavelengths between 450-550, 900-1100 nm, near 1400 nm and between 2200-2300 nm. However, the regression models generated were not suitable for quantitative phosphate analysis. Solubilization of organic matter and reactions during the PM extraction process hindered correlations between the spectra and these P soil fractions. For Prem,, the presence of Ca in the extractant and preferential adsorption by gibbsite and iron oxides, particularly goethite, obscured correlations with the spectra.

SIMULTANEOUS DETERMINATION OF PROTOCATECHUIC ALDEHYDE AND PROTOCATECHUIC ACID USING THE LOCALIZED SURFACE PLASMON RESONANCE PEAK OF SILVER NANOPARTICLES AND CHEMOMETRIC METHODS

A simple and sensitive spectrophotometric method is proposed for the simultaneous determination of protocatechuic acid and protocatechuic aldehyde. The method is based on the difference in the kinetic rates of the reactions of analytes with [Ag(NH3)2]+ in the presence of polyvinylpyrrolidone to produce silver nanoparticles. The data obtained were processed by chemometric methods using principal component analysis artificial neural network and partial least squares. Excellent linearity was obtained in the concentration ranges of 1.23-58.56 µg mL-1 and 0.08-30.39 µg mL-1 for PAC and PAH, respectively. The limits of detection for PAC and PAH were 0.039 and 0.025 µg mL-1, respectively.

MCR-ALS APLICADO NO MONITORAMENTO QUANTITATIVO DO PROCESSO DE ELETRODEGRADAÇÃO DA ATRAZINA USANDO ESPECTROS UV: RESULTADOS COMPARATIVOS COM HPLC-DAD COMO UM MÉTODO DE REFERÊNCIA

Electrodegradation of atrazine in water was performed using homemade (PA and PB) and purchased (PC) boron-doped diamond anodes. The degradation was monitored off-line by analyzing total organic carbon and high performance liquid chromatography with diode array detector (HPLC-DAD) and at-line by UV spectroscopy. The spectra were recorded every 2 min. The rank deficiency problem was resolved by assembling an augmented column-wise matrix. HPLC was employed to separate the original and byproducts degradation components. Aiming the same goal, multivariate curve resolution - alternating least squares (MCR-ALS) was applied to resolve the UV spectroscopic data. Comparison between HPLC and MCR-ALS separations is presented. By using MCR-ALS the spectra of atrazine and two byproducts were successfully resolved and the resulted concentration profiles properly represented the system studied. The ALS explained variance (R2) for PA, PB and PC was equal to 99.99% for all of them and the lack of fit for PA, PB and PC were 0.39, 0.34 and 0.54 respectively. The correlation (R) between the recovered and pure spectra were calculate for each electrodegradation, validating the MCR-ALS results. The average R was equal to 0.997. The spectral and concentration profiles described with this new approach are in agreement with HPLC-DAD results. The proposed method is an alternative to classical analyses for monitoring of the degradation process, mainly due to the simplicity, fast results and economy.

APPLICATION OF PARAFAC AND OPLS-DA ANALYSES ON HPLC FINGERPRINTS FOR THE CHARACTERIZATION OF Hibiscus sabdariffaCALYXES

The calyxes of Hibiscus sabdariffa are used in traditional medicine around the world. However, quality assurance protocols and chemical variability have not been previously analyzed. In the present study, chemical characterization of a set of samples of H. sabdariffa calyxes commercialized in Colombia was accomplished with the aim to explore the chemical variability among them. Chemometrics-based analyses on the data obtained from the HPLC-UV-DAD-derived profiles were then performed. Thus, the pre-processed single-wavelength data were subjected to principal component analysis (PCA). The PCA-derived results evidenced different groups which were well-correlated to the corresponding total phenolic and total anthocyanin contents. Multi-wavelength chromatographic (HPLC-UV-DAD surfaces) data were additionally examined via parallel factor analysis (PARAFAC) as data reduction method and the obtained loadings were subsequently submitted to PCA and orthogonal partial least squares discriminant analysis (OPLS-DA). Results were thus consistent with those from single-wavelength data. PCA loadings were employed to determine those chemical components responsible for the data variance and OPLS-DA model, constructed from PARAFAC loadings, and indicated differentiation according total anthocyanin contents among samples. The present chemometric analysis therefore demonstrated to be an excellent tool for differentiation of H. sabdariffacalyxes according to their chemical composition.

Characteristics of pyridine adsorption on Au(111) and Au(210) by EIS parameters fitting procedure

Some aspects of the application of electrochemical impedance spectroscopy to studies of solid electrode / solution interface, in the absence of faradaic processes, are analysed. In order to perform this analysis, gold electrodes with (111) and (210) crystallographic orientations in an aqueous solution containing 10 mmol dm-3 KF, as supporting electrolyte, and a pyridine concentration varying from 0.01 to 4.6 mmol dm-3, were used. The experimental data was analysed by using EQUIVCRT software, which utilises non-linear least squares routines, attributing to the solid electrode / solution interface behaviour described by an equivalent circuit with a resistance in series with a constant phase element. The results of this fitting procedure were analysed by the dependence on the electrode potential on two parameters: the pre-exponential factor, Y0, and the exponent n f, related with the phase angle shift. By this analysis it was possible to observe that the pyridine adsorption is strongly affected by the crystallographic orientation of the electrode surface and that the extent of deviation from ideal capacitive behaviour is mainly of interfacial origin.

Determination of sulfur in diesel using ATR/FTIR and multivariate calibration

The aim of this present work was to provide a more fast, simple and less expensive to analyze sulfur content in diesel samples than by the standard methods currently used. Thus, samples of diesel fuel with sulfur concentrations varying from 400 and 2500 mgkg-1 were analyzed by two methodologies: X-ray fluorescence, according to ASTM D4294 and by Fourier transform infrared spectrometry (FTIR). The spectral data obtained from FTIR were used to build multivariate calibration models by partial least squares (PLS). Four models were built in three different ways: 1) a model using the full spectra (665 to 4000 cm-1), 2) two models using some specific spectrum regions and 3) a model with variable selected by classic method of variable selection stepwise. The model obtained by variable selection stepwise and the model built with region spectra between 665 and 856 cm-1 and 1145 and 2717 cm-1 showed better results in the determination of sulfur content.

Kinetic modeling of mechanisms of industrially important organic reactions in gas and liquid phase

This dissertation is based on 5 articles which deal with reaction mechanisms of the following selected industrially important organic reactions: 1. dehydrocyclization of n-butylbenzene to produce naphthalene 2. dehydrocyclization of 1-(p-tolyl)-2-methylbutane (MB) to produce 2,6-dimethylnaphthalene 3. esterification of neopentyl glycol (NPG) with different carboxylic acids to produce monoesters 4. skeletal isomerization of 1-pentene to produce 2-methyl-1-butene and 2-methyl-2-butene The results of initial- and integral-rate experiments of n-butylbenzene dehydrocyclization over selfmade chromia/alumina catalyst were applied when investigating reaction 2. Reaction 2 was performed using commercial chromia/alumina of different acidity, platina on silica and vanadium/calcium/alumina as catalysts. On all catalysts used for the dehydrocyclization, major reactions were fragmentation of MB and 1-(p-tolyl)-2-methylbutenes (MBes), dehydrogenation of MB, double bond transfer, hydrogenation and 1,6-cyclization of MBes. Minor reactions were 1,5-cyclization of MBes and methyl group fragmentation of 1,6- cyclization products. Esterification reactions of NPG were performed using three different carboxylic acids: propionic, isobutyric and 2-ethylhexanoic acid. Commercial heterogeneous gellular (Dowex 50WX2), macroreticular (Amberlyst 15) type resins and homogeneous para-toluene sulfonic acid were used as catalysts. At first NPG reacted with carboxylic acids to form corresponding monoester and water. Then monoester esterified with carboxylic acid to form corresponding diester. In disproportionation reaction two monoester molecules formed NPG and corresponding diester. All these three reactions can attain equilibrium. Concerning esterification, water was removed from the reactor in order to prevent backward reaction. Skeletal isomerization experiments of 1-pentene were performed over HZSM-22 catalyst. Isomerization reactions of three different kind were detected: double bond, cis-trans and skeletal isomerization. Minor side reaction were dimerization and fragmentation. Monomolecular and bimolecular reaction mechanisms for skeletal isomerization explained experimental results almost equally well. Pseudohomogeneous kinetic parameters of reactions 1 and 2 were estimated by usual least squares fitting. Concerning reactions 3 and 4 kinetic parameters were estimated by the leastsquares method, but also the possible cross-correlation and identifiability of parameters were determined using Markov chain Monte Carlo (MCMC) method. Finally using MCMC method, the estimation of model parameters and predictions were performed according to the Bayesian paradigm. According to the fitting results suggested reaction mechanisms explained experimental results rather well. When the possible cross-correlation and identifiability of parameters (Reactions 3 and 4) were determined using MCMC method, the parameters identified well, and no pathological cross-correlation could be seen between any parameter pair.

Motivational Influences on Transfer: Dimensions and Boundary Conditions

The focus of this dissertation is the motivational influences on transfer in higher education and professional training contexts. To estimate these motivational influences, the dissertation includes seven individual studies that are structured in two parts. Part I, Dimensions, aims at identifying the dimensionality of motivation to transfer and its structural relations with training-related antecedents and outcomes. Part II, Boundary Conditions, aims at testing the predictive validity of motivation theories used in contemporary training research under different study conditions. Data in this dissertation was gathered from multi-item questionnaires, which were analyzed differently in Part I and Part II. Studies in Part I employed exploratory and confirmatory factor analysis, structural equation modeling, partial least squares (PLS) path modeling, and mediation analysis. Studies in Part II used artifact distribution meta-analysis, (nested) subgroup analysis, and weighted least squares (WLS) multiple regression. Results demonstrate that motivation to transfer can be conceptualized as a three-dimensional construct, including autonomous motivation to transfer, controlled motivation to transfer, and intention to transfer, given a theoretical framework informed by expectancy theory, self-determination theory, and the theory of planned behavior. Results also demonstrate that a range of boundary conditions moderates motivational influences on transfer. To test the predictive validity of expectancy theory, social cognitive theory, and the theory of goal orientations under different study settings, a total of 17 boundary conditions were meta-analyzed, including age; assessment criterion; assessment source; attendance policy; collaboration among trainees; computer support; instruction; instrument used to measure motivation; level of education; publication type; social training context; SS/SMC bias; study setting; survey modality; type of knowledge being trained; use of a control group; and work context. Together, the findings cumulated in this thesis support the basic premise that motivation is centrally important for transfer, but that motivational influences need to be understood from a more differentiated perspective than commonly found in the literature, in order to account for several dimensions and boundary conditions. The results of this dissertation across the seven individual studies are reflected in terms of their implications for theory development and their significance for training evaluation and the design of training environments. Limitations and directions to take in future research are discussed.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Alternative methodology for calculating the modulus of elasticity of wooden beams of structural dimensions

This study aims to present an alternative calculation methodology based on the Least Squares Method for determining the modulus of elasticity in bending wooden beams of structural dimensions. The equations developed require knowledge of three or five points measured in displacements along the piece, allowing greater reliability on the response variable, using the statistical bending test at three points and non-destructively, resulting from imposition of measures from small displacements L/300 and L/200, the largest being stipulated by the Brazilian norm NBR 7190:1997. The woods tested were Angico, Cumaru, Garapa and Jatoba. Besides obtaining the modulus of elasticity through the alternative methodology proposed, these were also obtained employing the Brazilian norm NBR 7190:1997, adapted to the condition of non-destructive testing (small displacements) and for pieces of structural dimensions. The results of the modulus of elasticity of the four species of wood according to both calculation approaches used proved to be equivalent, implying the good approximation provided by the methodology of calculation adapted from the Brazilian norm.

White mold detection in common beans through leaf reflectance spectroscopy

ABSTRACT This study aimed to identify wavelengths based on leaf reflectance (400-1050 nm) to estimate white mold severity in common beans at different seasons. Two experiments were carried out, one during fall and another in winter. Partial Least Squares (PLS) regression was used to establish a set of wavelengths that better estimates the disease severity at a specific date. Therefore, observations were previously divided in two sub-groups. The first one (calibration) was used for model building and the second subgroup for model testing. Error measurements and correlation between measured and predicted values of disease severity index were employed to provide the best wavelengths in both seasons. The average indexes of each experiment were of 5.8% and 7.4%, which is considered low. Spectral bands ranged between blue and green, green and red, and red and infrared, being most sensitive for disease estimation. Beyond the transition ranges, other spectral regions also presented wavelengths with potential to determine the disease severity, such as red, green, and near infrared.

Novel Regression Methods For Spectral Data

Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) are some of the mathematical pre- liminaries that are discussed prior to explaining PLS and PCR models. Both PLS and PCR are applied to real spectral data and their di erences and similarities are discussed in this thesis. The challenge lies in establishing the optimum number of components to be included in either of the models but this has been overcome by using various diagnostic tools suggested in this thesis. Correspondence analysis (CA) and PLS were applied to ecological data. The idea of CA was to correlate the macrophytes species and lakes. The di erences between PLS model for ecological data and PLS for spectral data are noted and explained in this thesis. i

A temperature predicting model for manufacturing processes requiring coiling

A model for predicting temperature evolution for automatic controling systems in manufacturing processes requiring the coiling of bars in the transfer table is presented. Although the method is of a general nature, the presentation in this work refers to the manufacturing of steel plates in hot rolling mills. The predicting strategy is based on a mathematical model of the evolution of temperature in a coiling and uncoiling bar and is presented in the form of a parabolic partial differential equation for a shape changing domain. The mathematical model is solved numerically by a space discretization via geometrically adaptive finite elements which accomodate the change in shape of the domain, using a computationally novel treatment of the resulting thermal contact problem due to coiling. Time is discretized according to a Crank-Nicolson scheme. Since the actual physical process takes less time than the time required by the process controlling computer to solve the full mathematical model, a special predictive device was developed, in the form of a set of least squares polynomials, based on the off-line numerical solution of the mathematical model.

Private Equity Fund Performance Determinants: Evidence From Fund Characteristics

The purpose of this thesis is to investigate whether different private equity fund characteristics have any influence on the fund performance. Fund characteristics include fund type (venture capital or buyouts), fund size (sizes of funds are divided into six ranges), fund investment industry, fund sequence (first fund or follow-on fund) and investment market (US or EMEA). Fund performance is measured by internal rate of return, and tested by cross-sectional regression analysis with the method of Ordinary Least Squares. The data employs performance and characteristics of 997 private equity funds between 1985 and 2008. Our findings are that fund type has effect on fund performance. The average IRR of venture capital funds is 2.7% less than average IRR of buyout funds. However, We did not find any relationship between fund size and performance, and between fund sequence and performance. Funds based on US market perform better than funds based on EMEA market. The fund performance differs across different industries. The average IRRs of industrial/energy industry, consumer related industry, communications and media industry and medical/health industry are higher than the average IRR of other industries.