926 resultados para Mínimos quadrados ordinários


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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%

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This work is combined with the potential of the technique of near infrared spectroscopy - NIR and chemometrics order to determine the content of diclofenac tablets, without destruction of the sample, to which was used as the reference method, ultraviolet spectroscopy, which is one of the official methods. In the construction of multivariate calibration models has been studied several types of pre-processing of NIR spectral data, such as scatter correction, first derivative. The regression method used in the construction of calibration models is the PLS (partial least squares) using NIR spectroscopic data of a set of 90 tablets were divided into two sets (calibration and prediction). 54 were used in the calibration samples and the prediction was used 36, since the calibration method used was crossvalidation method (full cross-validation) that eliminates the need for a validation set. The evaluation of the models was done by observing the values of correlation coefficient R 2 and RMSEC mean square error (calibration error) and RMSEP (forecast error). As the forecast values estimated for the remaining 36 samples, which the results were consistent with the values obtained by UV spectroscopy

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In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

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The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums

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Aiming to consumer s safety the presence of pathogenic contaminants in foods must be monitored because they are responsible for foodborne outbreaks that depending on the level of contamination can ultimately cause the death of those who consume them. In industry is necessary that this identification be fast and profitable. This study shows the utility and application of near-infrared (NIR) transflectance spectroscopy as an alternative method for the identification and classification of Escherichia coli and Salmonella Enteritidis in commercial fruit pulp (pineapple). Principal Component Analysis (PCA), Independent Modeling of Class Analogy (SIMCA) and Discriminant Analysis Partial Least Squares (PLS-DA) were used in the analysis. It was not possible to obtain total separation between samples using PCA and SIMCA. The PLS-DA showed good performance in prediction capacity reaching 87.5% for E. coli and 88.3% for S. Enteritides, respectively. The best models were obtained for the PLS-DA with second derivative spectra treated with a sensitivity and specificity of 0.87 and 0.83, respectively. These results suggest that the NIR spectroscopy and PLS-DA can be used to discriminate and detect bacteria in the fruit pulp

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The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

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This work has as main objective to find mathematical models based on linear parametric estimation techniques applied to the problem of calculating the grow of gas in oil wells. In particular we focus on achieving grow models applied to the case of wells that produce by plunger-lift technique on oil rigs, in which case, there are high peaks in the grow values that hinder their direct measurement by instruments. For this, we have developed estimators based on recursive least squares and make an analysis of statistical measures such as autocorrelation, cross-correlation, variogram and the cumulative periodogram, which are calculated recursively as data are obtained in real time from the plant in operation; the values obtained for these measures tell us how accurate the used model is and how it can be changed to better fit the measured values. The models have been tested in a pilot plant which emulates the process gas production in oil wells

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In general, an inverse problem corresponds to find a value of an element x in a suitable vector space, given a vector y measuring it, in some sense. When we discretize the problem, it usually boils down to solve an equation system f(x) = y, where f : U Rm ! Rn represents the step function in any domain U of the appropriate Rm. As a general rule, we arrive to an ill-posed problem. The resolution of inverse problems has been widely researched along the last decades, because many problems in science and industry consist in determining unknowns that we try to know, by observing its effects under certain indirect measures. Our general subject of this dissertation is the choice of Tykhonov´s regulaziration parameter of a poorly conditioned linear problem, as we are going to discuss on chapter 1 of this dissertation, focusing on the three most popular methods in nowadays literature of the area. Our more specific focus in this dissertation consists in the simulations reported on chapter 2, aiming to compare the performance of the three methods in the recuperation of images measured with the Radon transform, perturbed by the addition of gaussian i.i.d. noise. We choosed a difference operator as regularizer of the problem. The contribution we try to make, in this dissertation, mainly consists on the discussion of numerical simulations we execute, as is exposed in Chapter 2. We understand that the meaning of this dissertation lays much more on the questions which it raises than on saying something definitive about the subject. Partly, for beeing based on numerical experiments with no new mathematical results associated to it, partly for being about numerical experiments made with a single operator. On the other hand, we got some observations which seemed to us interesting on the simulations performed, considered the literature of the area. In special, we highlight observations we resume, at the conclusion of this work, about the different vocations of methods like GCV and L-curve and, also, about the optimal parameters tendency observed in the L-curve method of grouping themselves in a small gap, strongly correlated with the behavior of the generalized singular value decomposition curve of the involved operators, under reasonably broad regularity conditions in the images to be recovered

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The Tucunduba Dam, is west of Fortaleza, Ceará State. The seismic monitoring of the area, with an analogical station and seven digital stations, had beginning on June 11, 1997. The digital stations, operated from June to November 1997. The data collected in the period of digital monitoring was analyzed for determination of hypocenters, focal mechanisms, and shear-wave anisotropy analysis. For determination of hypocenters, it was possible to find an active zone of nearly 1 km in length, with depth between 4.5 and 5.2 km. A 60AZ/88SE fault plane was determined using the least-squares method and hypocenters of a selected set of 16 earthquakes recorded. Focal mechanisms were determined, in the composite fault plane solution, a strike-slip fault, trending nearly E-W, was found. Single fault plane solutions were obteined to some earthquakes presented mean values of 65 (azimuth), and 80 (dip). Shear-wave anisotropy was found in the data. Polarization directions and travel time delays, between S spliting waves, were determined. It was not possible to obtain any conclusion on the cause of the observed anisotropy. It is not clear if there is correlation between seismicity and mapped faults in the area, although the directions obtained starting from the hipocentros and focal mechanism are they are consistent with directions, observed in the area, photo, topographic and fractures directions observed in the area

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A caprinocultura leiteira no Brasil, apesar de ser uma atividade rural consolidada há algumas décadas, tem se mostrado totalmente dependente de outros países no que se refere ao melhoramento genético. A maioria dos plantéis existentes atualmente tem como base animais importados, e a renovação do material genético é feita por meio da importação de sêmen. Inexistem informações sobre o valor genético dos animais e sua evolução no decorrer dos anos. No presente trabalho, foram estimadas a herdabilidade e a repetibilidade da produção de leite utilizando o REML. Os valores obtidos foram 0,21557 e 0,21564, respectivamente. Para a predição do valor gênico dos animais, foi usado o procedimento BLUP com modelo animal. A mudança na tendência genética anual estimada por um modelo quadrático foi -0,8109 kg/ano², indicando desaceleração no ganho genético. A correlação de Pearson entre os valores gênicos dos bodes estimados com base na média da capacidade provável de produção das filhas obtida pelo método de mínimos quadrados com as estimadas pelas equações do modelo misto foi de 0,5751. A correlação de SPEARMAN entre as classificações dos bodes obtidos pelos dois métodos foi de 0,5813.

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Objetivou-se, no presente estudo, avaliar a produção de leite de caprinos leiteiros da região sudeste do Brasil, com intuito de verificar os fatores de meio e estimar os parâmetros genéticos pelo método dos mínimos quadrados (MMQ). Os controles de 1336 lactações foram inicialmente ajustados pela função multifásica (difásica) e calculou-se a produção de leite total (PLT). Os dados foram provenientes de sete propriedades e três raças (Parda Alpina, Saanen e Toggenburg). A média e o erro-padrão da PLT estimados pelo MMQ foram de 635,31 ±39,75 kg. A interação ano x estação do parto influenciou a PLT. em um dos anos estudados, a PLT foi menor para as cabras paridas no final da estação. Nas três estações de parto, observou-se comportamento quadrático da PLT, em função dos anos de parto. Para as três estações, a PLT aumentou de 1986 até meados de 1990, decrescendo em seguida. A idade de máxima PLT foi observada aos 46,65 meses. Das três raças estudadas, observou-se que as raças Parda Alpina e Saanen apresentaram alternância de superioridade na PLT em algumas fazendas, porém maiores que a Toggenburg. Os coeficientes de herdabilidade e repetibilidade da PLT estimados pelo MMQ foram de 0,296 ± 0,079 e 0,277 ± 0,033, respectivamente. Estes resultados revelam baixa confiabilidade em poucas observações dessas características ou na inconsistência das estimativas da função multifásica. Como a PLT é uma característica limitada ao sexo, sugere-se o teste de progênie como método de seleção mais eficiente para os reprodutores e uso de inseminação artificial como processo de disseminação do material genético selecionado.

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Foram analisados a relação peso-comprimento e o fator de condição de Oligosarcus hepsetus no Rio Grande do Parque Estadual da Serra do Mar - Núcleo Santa Virgínia, São Paulo. A relação peso-comprimento foi estimada para imaturos, fêmeas e machos, através da expressão P = aCb. Os parâmetros a e b foram estimados após transformação logarítmica dos dados de peso e comprimento e subsequente ajuste de uma linha reta aos pontos pelo método dos mínimos quadrados. Para fêmeas e machos foram obtidas as equações da relação peso-comprimento por período do ano (outono, inverno, primavera e verão), utilizando o peso total e o peso sem as gônadas. O fator de condição dos imaturos foi mais alto do que para machos e fêmeas, possivelmente, devido ao gasto energético com o desenvolvimento dos ovócitos e em menor grau dos espermatozóides. Esta espécie apresentou maiores valores de fator de condição no outono devido ao acúmulo de gordura e nas demais estações estes valores diminuíram devido ao longo período reprodutivo. As condições ambientais da Mata Atlântica parecem ser primordiais para o desenvolvimento das estratégias reprodutivas apresentadas por Oligosarcus hepsetus.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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This paper introduces an indirect estimate for the coefficients of distribution, hydrodynamic dispersion and retardation for contaminants commonly encountered in sanitary landfills and their liners, such as Cu2+ and K+; this estimate is based on the relationship between concentration and certain physical characteristics of typical Brazilian soils. The results of previous studies investigating the migration of contaminants were used to develop mathematical expressions from multiple non-linear regressions. Using minimal squares regression, this transport was linked to various combinations of contaminant concentration and both structural and textural characteristics of the porous medium. Various combinations of characteristics and concentrations were investigated, with a mathematical expression obtained for each. The relationship between percentage of clay and the contaminant content proved to be the most closely correlated with actual transport parameters, with coefficients close to one.

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Pós-graduação em Ciências Cartográficas - FCT