Distinção entre cachaças destiladas em alambiques e em colunas usando quimiometria

One hundred fifteen cachaça samples derived from distillation in copper stills (73) or in stainless steels (42) were analyzed for thirty five itens by chromatography and inductively coupled plasma optical emission spectrometry. The analytical data were treated through Factor Analysis (FA), Partial Least Square Discriminant Analysis (PLS-DA) and Quadratic Discriminant Analysis (QDA). The FA explained 66.0% of the database variance. PLS-DA showed that it is possible to distinguish between the two groups of cachaças with 52.8% of the database variance. QDA was used to build up a classification model using acetaldehyde, ethyl carbamate, isobutyl alcohol, benzaldehyde, acetic acid and formaldehyde as chemical descriptors. The model presented 91.7% of accuracy on predicting the apparatus in which unknown samples were distilled.

Hidrocarbonetos policíclicos aromáticos (HPAS) em cachaça, rum, uísque e álcool combustível

The concentration of 15 polycyclic aromatic hydrocarbons (PAHs) in 57 samples of distillates (cachaça, rum, whiskey, and alcohol fuel) has been determined by HPLC-Fluorescence detection. The quantitative analytical profile of PAHs treated by Partial Least Square - Discriminant Analysis (PLS-DA) provided a good classification of the studied spirits based on their PAHs content. Additionally, the classification of the sugar cane derivatives according to the harvest practice was obtained treating the analytical data by Linear Discriminant Analysis (LDA), using naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, benz[b]fluoranthene, and benz[g,h,i]perylene, as a chemical descriptors.

Determinação de sibutramina em formas farmacêuticas através de espectroscopia no infravermelho com refletância difusa e métodos de calibração multivariada

The goal of this work is the development and validation of an analytical method for fast quantification of sibutramine in pharmaceutical formulations, using diffuse reflectance infrared spectroscopy and partial least square regression. The multivariate model was elaborated from 22 mixtures containing sibutramine and excipients (lactose, microcrystalline cellulose, colloidal silicon dioxide and magnesium stearate) and using fragmented (750-1150/ 1350-1500/ 1850-1950/ 2600-2900 cm-1) and smoothing spectral data. Using 10 latent variables, excellent predictive capacity were observed in the calibration (n=20, RMSEC=0.004, R= 0.999) and external validation (n=5, RMSEC= 9.36, R=0.999) phases. In the analysis of synthetic mixtures the precision (SD=3,47%) was compatible with the rules of the Agencia Nacional de Vigilância Sanitária (ANVISA-Brazil). In the analysis of commercial drugs good agreement was observed between spectroscopic and chromatographic methods.

Caracterização espectroscópica multivariada do potencial antioxidante de vinhos

In this work the antioxidant capacity of red wine samples was characterized by conventional spectroscopic and chromatographic methodologies, regarding chemical parameters like color, total polyphenolic and resveratrol content, and antioxidant activity. Additionally, multivariate calibration models were developed to predict the antioxidant activity, using partial least square regression and the spectral data registered between 400 and 800 nm. Even when a close correlation between the evaluated parameters has been expected many inconsistencies were observed, probably on account of the low selectivity of the conventional methodologies. Models developed from mean-centered spectra and using 4 latent variables allowed high prevision capacity of the antioxidant activity, permitting relative errors lower than 3%.

Application of GA-PLS and GA-KPLS calculations for the prediction of the retention indices of essential oils

Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 117 diverse compounds in essential oils from 5 Pimpinella species gathered from central Turkey which were obtained by gas chromatography and gas chromatography-mass spectrometry. The square correlation coefficient leave-group-out cross validation (LGO-CV) (Q²) between experimental and predicted RI for training set by GA-PLS and GA-KPLS was 0.940 and 0.963, respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure-retention relationship (QSRR) studies.

Avaliação do perfil de dissolução de medicamentos utilizando-se espectroscopia eletrônica multivariada

In this work the evaluation of the dissolution profile of captopril-hydrochlorothiazide and zidovudine-lamivudine associations were carried out by multivariate spectroscopic method. The models were developed by partial least square regression from 20 synthetic mixtures using mean-centered spectral data. The external validation was accomplished with 5 synthetic mixtures shown mean prevision error of about 1%. Good agreement was observed in the analyses of commercial drugs (content uniformity and dissolution profile), considering the results obtained by the standard chromatographic method, with prevision error lower than 10%.

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.

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.

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.

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.

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.

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.

Treadmill exercise testing of asymptomatic men and women without evidence of heart disease

The aim of this study was to test the hypothesis of differences in performance including differences in ST-T wave changes between healthy men and women submitted to an exercise stress test. Two hundred (45.4%) men and 241 (54.6%) women (mean age: 38.7 ± 11.0 years) were submitted to an exercise stress test. Physiologic and electrocardiographic variables were compared by the Student t-test and the chi-square test. To test the hypothesis of differences in ST-segment changes, data were ranked with functional models based on weighted least squares. To evaluate the influence of gender and age on the diagnosis of ST-segment abnormality, a logistic model was adjusted; P < 0.05 was considered to be significant. Rate-pressure product, duration of exercise and estimated functional capacity were higher in men (P < 0.05). Sixteen (6.7%) women and 9 (4.5%) men demonstrated ST-segment upslope ≥0.15 mV or downslope ≥0.10 mV; the difference was not statistically significant. Age increase of one year added 4% to the chance of upsloping of segment ST ≥0.15 mV or downsloping of segment ST ≥0.1 mV (P = 0.03; risk ratio = 1.040, 95% confidence interval (CI) = 1.002-1.080). Heart rate recovery was higher in women (P < 0.05). The chance of women showing an increase of systolic blood pressure ≤30 mmHg was 85% higher (P = 0.01; risk ratio = 1.85, 95%CI = 1.1-3.05). No significant difference in the frequency of ST-T wave changes was observed between men and women. Other differences may be related to different physical conditioning.