895 resultados para Hierarchical task analysis


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The concentrations of Cu, Pb, Zn, Cr, Ni, Al, Mn and Fe were measured by atomic absorption spectrometry, of 19 topsoil samples collected in the Teresina city urban area to discriminate natural and anthropic contributions and identify possible sources of pollution. The average concentrations of Cu, Zn, Pb and Cr of the urban soils were 6.11, 8.56, 32.12 and 7,17 mg/kg-1, respectively. Statistical analysis techniques, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to analyze the data. Mn, Ni and Cr levels were interpreted as natural contributions, whereas Pb, Zn and, in part, Cu were accounted for mainly by anthropic activities. High Pb levels were observed in the ancient avenues.

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Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

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Soils play an important role in the biogeochemical cycle of mercury as a sink for and source of this metallic species to atmospheric and hydrological compartments. In the study reported here, various types of soil were evaluated to ascertain the influence of parameters such as pH, organic matter content, Fe, Al, sand, silt, clay, C/H, C/N, C/O atomic ratios, and cation exchange capacity on the distribution of Hg in Amazonia's mid-Negro River basin. The data obtained were interpreted by multivariate exploratory analyses (hierarchical cluster analysis and principal component analysis), which indicated that organic matter plays an important role in mercury uptake in the various soils studied. The soils in floodable areas were found to contain 1.5 to 2.8-fold higher Hg concentrations than those in non-floodable areas. Since these soils are flooded almost year-round, they are less available to participate in redox processes at the soil/atmosphere interface. Hence, floodable areas, which comprise humic-rich soils, accumulate more mercury than non-floodable soils, thus playing an important role in the biogeochemical cycle of Hg in Amazonia's mid-Negro River basin.

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An activity for introducing hierarchical cluster analysis (HCA) and principal component analysis (PCA) during the Instrumental Analytical Chemistry course is presented. The posed problem involves the discrimination of mineral water samples according to their geographical origin. Thirty-seven samples of 9 different brands were considered and the results from the determination of Na, K, Mg, Ca, Sr and Ba were taken into account. Non-supervised methods for pattern recognition were explored to construct a dendrogram, score and loading plots. The devised activity can be adopted for introducing Chemometrics devoted to data handling, stressing its importance in the context of modern Analytical Chemistry.

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The Brazilian legislation requires analysis of certain parameters to classify a wine and allow its commercialization. Some physico-chemical and some color parameters were determined in this work in samples of different red wines sold in the metropolitan area of Recife. Multivariate analysis comprising principal component analysis and hierarchical cluster analysis was employed to distinguish the analyzed wines. The results for pH, chloride concentration, color parameters and ammonium content were the most important variables for sample classification. It was also possible to classify the wines as soft or dry wines and amongst the soft wines we could determine two out of four winegrowing producers.

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The hedonic level of commercial cachaças, was evaluated by consumers and by a tasters. The results of sensorial methods analyzed trough Principal Components Analysis, Hierarchical Cluster Analysis and the Pearson linear correlation indicated that the best classified cachaças were produced in copper stills and aged in oak casks. By contrast the worst classified exhibited as the main features be not aged and high alcohol percentage. The index of preference is positively correlated with the intensity of yellow color, wood flavor, sweetness and fruit aroma. There is a negative preference correlation with the acidity, the taste of alcohol and bitterness.

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This work aims to study spatial and seasonal variability of some chemical-physical parameters in the Turvo/Grande watershed, São Paulo State, Brazil. Water samples were taken monthly, 2007/07-2008/11, from fourteen sampling stations sited along the Turvo, Preto and Grande Rivers and its main tributaries. The Principal Component Analysis and hierarchical cluster analysis showed two distinct groups in this watershed, the first one associated for the places more impacted by domestic effluent (lower levels of dissolved oxygen in the studied region). The sampling places located to downstream (Turvo and Grande rivers) were discriminate by diffuse source of pollutants from flooding and agriculture runoffs in a second group.

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In this work, the organic compounds of cigar samples from different brands were analyzed. The compound extraction was made using the matrix solid-phase dispersion (MSPD) technique, followed by gas chromatography and identification by mass spectrometry (GC-MS) and standards, when available. Thirty eight organic compounds were found in seven different brands. Finally, with the objective of characterizing and discriminating the cigar samples, multivariate statistical analyses were applied to data, e.g.; principal component analysis (PCA) and hierarchical cluster analysis (HCA). With such analyses, it was possible to discriminate three main groups of three quality levels.

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Six wines were distilled in two different distillation apparatus (alembic and column) producing 24 distillates (6 for each alembic fraction - head, heart and tail; 6 column distillates). The chemical composition of distillates from the same wine was determined using chromatographic techniques. Analytical data were subjected to Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) allowing discrimination of four clusters according to chemical profiles. Both distillation processes influenced the sugarcane spirits chemical quality since two types of distillates with different quantitative chemical profiles were produced after the elimination of fermentation step influence.

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In this study, the mineral composition of leaves and teas of medicinal plants was evaluated. Ca, Cu, Fe, Mg, Mn e Zn were determined in the samples using flame atomic absorption spectrometry. Principal component analysis was applied to discriminate the samples studied. The samples were divided within the 2 groups according to their mineral composition. Copper and iron were the variables that contributed most to the separation of the samples followed by Ca, Mg, Mn and Zn. The information in the principal component analysis was confirmed by the dendrogram obtained by hierarchical cluster analysis.

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GC/MS/FID analyses of volatile compounds from cladodes and inflorescences from male and female specimens of Baccharis trimera (Less.) DC. collected in the states of Paraná and Santa Catarina, Brazil, showed that carquejyl acetate was the primary volatile component (38% to 73%), while carquejol and ledol were identified in lower concentrations. Data were subjected to hierarchical cluster analysis and principal component analysis, which confirmed that the chemical compositions of all samples were similar. The results presented here highlight the occurrence of the same chemotype of B. trimera in three southern states of Brazil.

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In this study, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to classify blends produced from diesel S500 and different kinds of biodiesel produced by the TDSP methodology. The different kinds of biodiesel studied in this work were produced from three raw materials: soybean oil, waste cooking oil and hydrogenated vegetable oil. Methylic and ethylic routes were employed for the production of biodiesel. HCA and PCA were performed on the data from attenuated total reflectance Fourier transform infrared spectroscopy, showing the separation of the blends into groups according to biodiesel content present in the blends and to the kind of biodiesel used to form the mixtures.

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Tapirira guianensis (Anacardiaceae) is used in traditional medicine and is important for the recovery of degraded areas and riparian forests because the T. guianensis fruits are highly consumed by wildlife. Volatile components from dried leaves and branches of five individual plants of T. guianensis were collected in two sandbank forests of the State of Pará (Extractive Reserve Maracanã and Area of Environmental Protection Algodoal/Maiandeua), extracted by hydrodistillation using a Clevenger-type apparatus, and analyzed by GC/MS. The ten oils obtained are comprised mostly of sesquiterpene hydrocarbons (58.49 to 100%), with (E)-caryophyllene, β-selinene, α-selinene, β-sesquiphellandrene, and α-zingiberene being the most prominent. The results of the oil compositions were processed by Hierarchical Component Analysis (HCA) allowing the establishment of three groups of essential oils for T. guianensis differentiated by the content of β-selinene/α-selinene (Type I), (E)-caryophyllene (Type II), and β-sesquiphellandrene/α-zingiberene (Type III).

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The objective of this work was to develop a free access exploratory data analysis software application for academic use that is easy to install and can be handled without user-level programming due to extensive use of chemometrics and its association with applications that require purchased licenses or routines. The developed software, called Chemostat, employs Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), intervals Principal Component Analysis (iPCA), as well as correction methods, data transformation and outlier detection. The data can be imported from the clipboard, text files, ASCII or FT-IR Perkin-Elmer “.sp” files. It generates a variety of charts and tables that allow the analysis of results that can be exported in several formats. The main features of the software were tested using midinfrared and near-infrared spectra in vegetable oils and digital images obtained from different types of commercial diesel. In order to validate the software results, the same sets of data were analyzed using Matlab© and the results in both applications matched in various combinations. In addition to the desktop version, the reuse of algorithms allowed an online version to be provided that offers a unique experience on the web. Both applications are available in English.

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AbstractThe purpose of this study was to evaluate the best operating conditions of ICP OES for the determination of Na, Ca, Mg, Sr and Fe in aqueous extract of crude oil obtained after hot extraction with organic solvents (ASTM D 6470-99 modified). Thus, the full factorial design and central composite design were used to optimize the best conditions for the flow of nebulization gas, the flow of auxiliary gas, and radio frequency power. After optimization of variables, a study to obtain correct classification of the 18 samples of aqueous extract of crude oils (E1 to E18) from three production and refining fields was carried out. Exploratory analysis of these extracts was performed by principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA), using the original variables as the concentration of the metals Na, Ca, Mg, Sr and Fe determined by ICP OES.