935 resultados para PRINCIPAL COMPONENTS-ANALYSIS


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The objective of this work was to assess and characterize two clones, 169 and 685, of Cabernet Sauvignon grapes and to evaluate the wine produced from these grapes. The experiment was carried out in São Joaquim, SC, Brazil, during the 2009 harvest season. During grape ripening, the evolution of physical-chemical properties, phenolic compounds, organic acids, and anthocyanins was evaluated. During grape harvest, yield components were determined for each clone. Individual and total phenolics, individual and total anthocyanins, and antioxidant activity were evaluated for wine. The clones were also assessed regarding the duration of their phenological cycle. During ripening, the evolution of phenolic compounds and of physical-chemical parameters was similar for both clones; however, during harvest, significant differences were observed regarding yield, number of bunches per plant and berries per bunch, leaf area, and organic acid, polyphenol, and anthocyanin content. The wines produced from these clones showed significant differences regarding chemical composition. The clones showed similar phenological cycle and responses to bioclimatic parameters. Principal component analysis shows that clone 685 is strongly correlated with color characteristics, mainly monomeric anthocyanins, while clone 169 is correlated with individual phenolic compounds.

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The objective of this work was to evaluate the biochemical composition of six berry types belonging to Fragaria, Rubus, Vaccinium and Ribes genus. Fruit samples were collected in triplicate (50 fruit each) from 18 different species or cultivars of the mentioned genera, during three years (2008 to 2010). Content of individual sugars, organic acids, flavonols, and phenolic acids were determined by high performance liquid chromatography (HPLC) analysis, while total phenolics (TPC) and total antioxidant capacity (TAC), by using spectrophotometry. Principal component analysis (PCA) and hierarchical cluster analysis (CA) were performed to evaluate the differences in fruit biochemical profile. The highest contents of bioactive components were found in Ribes nigrum and in Fragaria vesca, Rubus plicatus, and Vaccinium myrtillus. PCA and CA were able to partially discriminate between berries on the basis of their biochemical composition. Individual and total sugars, myricetin, ellagic acid, TPC and TAC showed the highest impact on biochemical composition of the berry fruits. CA separated blackberry, raspberry, and blueberry as isolate groups, while classification of strawberry, black and red currant in a specific group has not occurred. There is a large variability both between and within the different types of berries. Metabolite fingerprinting of the evaluated berries showed unique biochemical profiles and specific combination of bioactive compound contents.

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Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

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The objectives of this study were to evaluate the performance of cultivars, to quantify the variability and to estimate the genetic distances of 66 wine grape accessions in the Grape Germplasm Bank of the EMBRAPA Semi-Arid, in Juazeiro, BA, Brazil, through the characterization of discrete and continuous phenotypic variables. Multivariate statistics, such as, principal components, Tocher's optimization procedure, and the graphic of the distance, were efficient in grouping more similar genotypes, according to their phenotypic characteristics. There was no agreement in the formation of groups between continuous and discrete morpho-agronomic traits, when Tocher's optimization procedure was used. Discrete variables allowed the separation of Vitis vinifera and hybrids in different groups. Significant positive correlations were observed between weight, length and width of bunches, and a negative correlation between titratable acidity and TSS/TTA. The major part (84.12%) of the total variation present in the original data was explained by the four principal components. The results revealed little variability between wine grape accessions in the Grape Germplasm Bank of Embrapa Semi-Arid.

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In recent years there has been growing interest in composite indicators as an efficient tool of analysis and a method of prioritizing policies. This paper presents a composite index of intermediary determinants of child health using a multivariate statistical approach. The index shows how specific determinants of child health vary across Colombian departments (administrative subdivisions). We used data collected from the 2010 Colombian Demographic and Health Survey (DHS) for 32 departments and the capital city, Bogotá. Adapting the conceptual framework of Commission on Social Determinants of Health (CSDH), five dimensions related to child health are represented in the index: material circumstances, behavioural factors, psychosocial factors, biological factors and the health system. In order to generate the weight of the variables, and taking into account the discrete nature of the data, principal component analysis (PCA) using polychoric correlations was employed in constructing the index. From this method five principal components were selected. The index was estimated using a weighted average of the retained components. A hierarchical cluster analysis was also carried out. The results show that the biggest differences in intermediary determinants of child health are associated with health care before and during delivery.

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This paper presents a composite index of early childhood health using a multivariate statistical approach. The index shows how child health varies across Colombian departments, -administrative subdivisions-. In recent years there has been growing interest in composite indicators as an efficient analysis tool and a way of prioritizing policies. These indicators not only enable multi-dimensional phenomena to be simplified but also make it easier to measure, visualize, monitor and compare a country’s performance in particular issues. We used data collected from the Colombian Demographic and Health Survey, DHS, for 32 departments and the capital city, Bogotá, in 2005 and 2010. The variables included in the index provide a measure of three dimensions related to child health: health status, health determinants and the health system. In order to generate the weight of the variables and take into account the discrete nature of the data, we employed a principal component analysis, PCA, using polychoric correlation. From this method, five principal components were selected. The index was estimated using a weighted average of the components retained. A hierarchical cluster analysis was also carried out. We observed that the departments ranking in the lowest positions are located on the Colombian periphery. They are departments with low per capita incomes and they present critical social indicators. The results suggest that the regional disparities in child health may be associated with differences in parental characteristics, household conditions and economic development levels, which makes clear the importance of context in the study of child health in Colombia.

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In recent years, new analytical tools have allowed researchers to extract historical information contained in molecular data, which has fundamentally transformed our understanding of processes ruling biological invasions. However, the use of these new analytical tools has been largely restricted to studies of terrestrial organisms despite the growing recognition that the sea contains ecosystems that are amongst the most heavily affected by biological invasions, and that marine invasion histories are often remarkably complex. Here, we studied the routes of invasion and colonisation histories of an invasive marine invertebrate Microcosmus squamiger (Ascidiacea) using microsatellite loci, mitochondrial DNA sequence data and 11 worldwide populations. Discriminant analysis of principal components, clustering methods and approximate Bayesian computation (ABC) methods showed that the most likely source of the introduced populations was a single admixture event that involved populations from two genetically differentiated ancestral regions - the western and eastern coasts of Australia. The ABC analyses revealed that colonisation of the introduced range of M. squamiger consisted of a series of non-independent introductions along the coastlines of Africa, North America and Europe. Furthermore, we inferred that the sequence of colonisation across continents was in line with historical taxonomic records - first the Mediterranean Sea and South Africa from an unsampled ancestral population, followed by sequential introductions in California and, more recently, the NE Atlantic Ocean. We revealed the most likely invasion history for world populations of M. squamiger, which is broadly characterized by the presence of multiple ancestral sources and non-independent introductions within the introduced range. The results presented here illustrate the complexity of marine invasion routes and identify a cause-effect relationship between human-mediated transport and the success of widespread marine non-indigenous species, which benefit from stepping-stone invasions and admixture processes involving different sources for the spread and expansion of their range.

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A continuous random variable is expanded as a sum of a sequence of uncorrelated random variables. These variables are principal dimensions in continuous scaling on a distance function, as an extension of classic scaling on a distance matrix. For a particular distance, these dimensions are principal components. Then some properties are studied and an inequality is obtained. Diagonal expansions are considered from the same continuous scaling point of view, by means of the chi-square distance. The geometric dimension of a bivariate distribution is defined and illustrated with copulas. It is shown that the dimension can have the power of continuum.

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Water quality was monitored at the upper course of the Rio das Velhas, a major tributary of the São Francisco basin located in the state of Minas Gerais, over an extension of 108 km from its source up to the limits with the Sabara district. Monitoring was done at 37 different sites over a period of 2 years (2003-2004) for 39 parameters. Multivariate statistical techniques were applied to interpret the large water-quality data set and to establish an optimal long-term monitoring network. Cluster analysis separated the sampling sites into groups of similarity, and also indicated the stations investigated for correlation and recommended to be removed from the monitoring network. Principal component analysis identified four components, which are responsible for the data structure explaining 80% of the total variance of the data. The principal parameters are characterized as due to mining activities and domestic sewage. Significant data reduction was achieved.

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The psychometric properties of the Personal Wellbeing Index are analyzed on a Spanish and Portuguese adolescent sample. We test the reliability of the scale using Cronbach’s alpha. And complementarily we analyze the item-total correlations in the different wellbeing domains included. We execute an exploratory factor analysis (principal components) and a multigroup Confirmatory Factor Analysis (CFA). The results show that Cronbach’s alpha is 0.79 for the Chilean version and in the Brazilian version is 0.78 confirming adequate levels of reliability found in previous studies. Correlations between fields of well-being shows values ranging between 0.224 and 0.496 for Chile and from 0.24 to 0.46 for Brazil. The results are similar to those obtained in other countries. The monofactorial structure of the scale is cinfirmed, also the adjustment to the scale structure to the data of the two samples and the comparability of means of global indices. The results suggest the existence of other well-being domains that had not been considered in the original proposal of the scale

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The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

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Psychometric analysis of the AF5 multidimensional scale of self-concept in a sample of adolescents and adults in Catalonia. The aim of this study is to carry out a psychometric study of the AF5 scale in a sample of 4.825 Catalan subjects from 11 to 63 years-old. They are students from secondary compulsory education (ESO), from high school, middle-level vocational training (CFGM) and from the university. Using a principal component analysis (PCA) the theoretical validity of the components is established and the reliability of the instrument is also analyzed. Differential analyses are performed by gender and normative group using a 2 6 factorial design. The normative group variable includes the different levels classifi ed into 6 sub-groups: university, post-compulsory secondary education (high school and CFGM), 4th of ESO, 3rd of ESO, 2nd of ESO and 1st of ESO. The results indicate that the reliability of the Catalan version of the scale is similar to the original scale. The factorial structure also fi ts with the original model established beforehand. Signifi cant differences by normative group in the four components of self-concept explored (social, family, academic/occupational and physical) are observed. By gender, signifi cant differences appear in the component of physical self-concept, academic and social but not in the family component

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Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

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The hydroalcoholic extracts prepared from standard leaves of Maytenus ilicifolia and commercial samples of espinheira-santa were evaluated qualitatively (fingerprinting) and quantitatively. In this paper, fingerprinting chromatogram coupled with Principal Component Analysis (PCA) is described for the metabolomic analysis of standard and commercial espinheira-santa samples. The epicatechin standard was used as an external standard for the development and validation of a quantitative method for the analysis in herbal medicines using a photo diode array detector. This method has been applied for quantification of epicatechin in commercialized herbal medicines sold as espinheira-santa in Brazil and in the standard sample of M. ilicifolia.

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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.