224 resultados para Principal component analysis discriminant analysis
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Foram utilizados dados de 288 codornas de corte (Coturnix coturnix coturnix) para avaliar a possibilidade de resumir a informação contida no complexo de variáveis originais, eliminando-se variáveis inexpressivas por meio da técnica de componentes principais. Foram registrados o peso vivo (PVIVO) e pesos do peito (PPEITO), das coxas (PCOXA), da gordura abdominal (GA), das vísceras comestíveis (fígado, moela e coração) (FIG, MOELA e CORA) e da carcaça eviscerada (PCEVIS). As carcaças foram secas e trituradas para a avaliação do teor matéria seca (MS), gordura (GORD) e proteína bruta (PB). Dos 11 componentes principais, sete (63,6%) apresentaram variância menor que 0,7 (autovalor inferior a 0,7), sendo sugeridas para descarte, respectivamente, em ordem de menor importância, para explicar a variação total das seguintes variáveis: PCEVIS, PPEITO, PCOXA, CORA, FIG MOELA e GORD. Com base nos resultados, recomenda-se manter as seguintes variáveis em experimentos futuros: PVIVO, MS, PB e GA.
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This paper reports on a sensor array able to distinguish tastes and used to classify red wines. The array comprises sensing units made from Langmuir-Blodgett (LB) films of conducting polymers and lipids and layer-by-layer (LBL) films from chitosan deposited onto gold interdigitated electrodes. Using impedance spectroscopy as the principle of detection, we show that distinct clusters can be identified in principal component analysis (PCA) plots for six types of red wine. Distinction can be made with regard to vintage, vineyard and brands of the red wine. Furthermore, if the data are treated with artificial neural networks (ANNs), this artificial tongue can identify wine samples stored under different conditions. This is illustrated by considering 900 wine samples, obtained with 30 measurements for each of the five bottles of the six wines, which could be recognised with 100% accuracy using the algorithms Standard Backpropagation and Backpropagation momentum in the ANNs. (C) 2003 Elsevier B.V. All rights reserved.
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The versatility of sensor arrays made from nanostructured Langmuir-Blodgett (LB) and layer-by-layer (LBL) films is demonstrated in two ways. First, different combinations of sensing units are employed to distinguish the basic tastes, viz. sweet, sour, bitter, and salty tastes, produced, respectively, by small concentrations (down to 0.01 g/mol) of sucrose, HCl, quinine, and NaCl solutions. The sensing units are comprised of LB and/or LBL films from semiconducting polymers, a ruthenium complex, and sulfonated lignin. Then, sensor arrays were used to identify wines from different sources, with the high distinguishing ability being demonstrated in principal component analysis (PCA) plots. Particularly important was the fact that the sensing ability does not depend on specific interactions between analytes and the film materials, but a judicious choice of materials is, nevertheless, required for the materials to respond differently to a given sample. It is also shown that the interaction with the analyte may affect the morphology of the nanostructured films, as indicated with scanning electron microscopy. For instance, in wine analysis these changes are not irreversible and the original film morphology is retrieved if the sensing unit is washed with copious amounts of water, thus allowing the sensor unit to be reused.
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The synthesis of a poly(azo)urethane by fixing CO2 in bis-epoxide followed by a polymerization reaction with an azodiamine is presented. Since isocyanate is not used in the process, it is termed clean method and the polymers obtained are named NIPUs (non-isocyanate polyurethanes). Langmuir films were formed at the air-water interface and were characterized by surface pressure vs mean molecular area per met unit (Pi-A) isotherms. The Langmuir monolayers were further studied by running stability tests and cycles of compression/expansion (possible hysteresis) and by varying the compression speed of the monolayer formation, the subphase temperature, and the solvents used to prepare the spreading polymer solutions. The Langmuir-Blodgett (LB) technique was used to fabricate ultrathin films of a particular polymer (PAzoU). It is possible to grow homogeneous LB films of up to 15 layers as monitored using UV-vis absorption spectroscopy. Higher number of layers can be deposited when PAzoU is mixed with stearic acid, producing mixed LB films. Fourier transform infrared (FTIR) absorption spectroscopy and Raman scattering showed that the materials do not interact chemically in the mixed LB films. The atomic force microscopy (AFM) and micro-Raman technique (optical microscopy coupled to Raman spectrograph) revealed that mixed LB films present a phase separation distinguishable at micrometer or nanometer scale. Finally, mixed and neat LB films were successfully characterized using impedance spectroscopy at different temperatures, a property that may lead to future application as temperature sensors. Principal component analysis (PCA) was used to correlate the data.
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Chemical sensors made from nanostructured films of poly(o-ethoxyaniline) POEA and poly(sodium 4-styrene sulfonate) PSS are produced and used to detect and distinguish 4 chemicals in solution at 20 mM, including sucrose, NaCl, HCl, and caffeine. These substances are used in order to mimic the 4 basic tastes recognized by humans, namely sweet, salty, sour, and bitter, respectively. The sensors are produced by the deposition of POEA/PSS films at the top of interdigitated microelectrodes via the layer-by-layer technique, using POEA solutions containing different dopant acids. Besides the different characteristics of the POEA/PSS films investigated by UV-Vis and Raman spectroscopies, and by atomic force microscopy.. it is observed that their electrical response to the different chemicals in liquid media is very fast, in the order of seconds, systematical, reproducible, and extremely dependent on the type of acid used for film fabrication. The responses of the as-prepared sensors are reproducible and repetitive after many cycles of operation. Furthermore, the use of an "electronic tongue" composed by an array of these sensors and principal component analysis as pattern recognition tool allows one to reasonably distinguish test solutions according to their chemical composition. (c) 2007 Published by Elsevier B.V.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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O objetivo deste trabalho foi relacionar as características da anatomia foliar de Brachiaria decumbens (capim-braquiária) e Brachiaria plantaginea (capim-marmelada), em três estádios de desenvolvimento da planta, com a sensibilidade a herbicidas. A semeadura foi feita em vasos plásticos, contendo solo, mantidos em casa de vegetação. Foi amostrada a porção mediana do limbo da terceira folha expandida, a partir da base do colmo, compreendendo três estádios de desenvolvimento da planta: estádio 1 (com 4-6 folhas), estádio 2 (com 3-4 perfilhos) e estádio 3 (plantas adultas no início do florescimento). Foram quantificados os seguintes descritores anatômicos das regiões da quilha (nervura central) e da asa (porção entre a nervura central e a margem do limbo): área da secção transversal; porcentagens de epiderme das faces adaxial e abaxial, esclerênquima, bainha do feixe vascular, feixe vascular e parênquima; espessura da folha; distância entre os feixes vasculares; comprimento do estômato; e número de estômatos e de tricomas (curtos e longos). Os valores obtidos foram submetidos aos testes estatísticos multivariados de Análise de Agrupamento e Análise dos Componentes Principais. Os descritores avaliados permitiram diferenciar o estádio 3 de desenvolvimento da planta em relação aos demais, o qual pode ser considerado o menos sensível à ação dos herbicidas aplicados em pós-emergência.
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As espécies Commelina benghalensis, C. villosa, C. diffusa e C. erecta são conhecidas como trapoeraba e, freqüentemente, são confundidas entre si, dificultando o controle químico, o que pode provocar prejuízos econômicos e danos ambientais. O presente trabalho teve como objetivo selecionar características morfológicas que possibilitem facilitar a identificação dessas espécies, utilizando a técnica de análise multivariada. Foram avaliadas 12 características morfológicas descritivas e 13 quantitativas, utilizando-se os métodos de análise de agrupamento e análise de componentes principais. As espécies apresentaram alto grau de dissimilaridade, sobretudo em relação às características descritivas, destacando-se: hábito da planta, pilosidade do caule e da folha, entre outros. As características quantitativas também mostraram poder discriminatório. Características que apresentaram alto valor taxonômico foram selecionadas para compor a chave de identificação para as quatro espécies de Commelina.
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This paper aims to study the ecological system of the Pardo River, at the source and lower-order passages, which are in the Botucatu area, São Paulo State, Brazil. This study was carried out to determine water quality with some chemical-physical indicators, coliforms, and chemical species of samples taken monthly, 1995/02-1996/01, from eight sampling stations sited along the Pardo River. The results in the river monitoring are discussed based on annual averages, analysis of variance, and compared to Tukey's Studentized Range-HSD, and principal component analysis (PCA) was applied to normalize data to assess association between variables. We can conclude that the variables used are very efficient for identifying and that the dry season shows the worst water quality. These were caused by organic matter, nutrients (originate) from anthropogenic sources (spatial sources) and mainly municipal wastewater, affecting the quality and hydrochemistry of the river water, which have been differentiated and assigned to polluting sources. Meanwhile, the degree of degradation of the Pardo River is low (sewage treatment carried out by the city of Pardinho is efficient), leaving the water of the river suitable for use by the population of Botucatu, after conventional treatment (Conama, Resolucao No. 20, CONAMA, Brazilia DF, 09-23, 1986-the water of the Pardo river is classified as level 03). (C) 2001 Elsevier B.V. Ltd. All rights reserved.