Avaliação sensorial de cachaça

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.

Discrimination of native wood charcoal by infrared spectroscopy

Brazil is one of the largest producers and consumers of charcoal in the world. About 50% of its charcoal comes from native forests, with a large part coming from unsustainable operations. The anatomic identification of charcoal is subjective; an instrumental technique would facilitate the monitoring of forests. This study aimed to verify the feasibility of using medium and near infrared reflectance spectroscopy to discriminate native (ipê) from plantation charcoals (eucalyptus). Principal Components Analysis, followed by Discriminant Factorial Analysis formed two different groups indicated by Mahalanobis distances of 40.6 and 80.3 for near and mid infrared, respectively. Validation of the model showed 100% efficacy.

Classificação periódica: um exemplo didático para ensinar análise de componentes principais

A dataset of chemical properties of the elements is used herein to introduce principal components analysis (PCA). The focus in this article is to verify the classification of the elements within the periodic table. The reclassification of the semimetals as metals or nonmetals emerges naturally from PCA and agrees with the current SBQ/IUPAC periodic table. Dataset construction, basic preprocessing, loading and score plots, and interpretation have been emphasized. This activity can be carried out even when students with distinct levels of formation are together in the same learning environment.

Ácidos orgânicos em aguardentes produzidas em alambique e em coluna

The concentration of 14 organic acids of 50 sugarcane spirits samples was determined by gas chromatography using flame ionization detection. The organic acids analytical quantitative profile in stills and column distilled spirits from wines obtained from the same must were compared. The comparison was also carried in "head", "heart" and "tail fractions of stills distilled spirits. The experimental data were analyzed by Principal Components Analysis (PCA) and pointed out that the distillation process (stills and column) strongly influences the lead spirits' organic acid composition and that producers' operational "cuts off" to produce "tail", "heart" and "head", fractions should be optimized.

Identificação de adulteração de biocombustível por adição de óleo residual ao diesel por espectrofluorimetria total 3D e análise das componentes principais

Total spectrofluorimetry associated to Principal Components Analysis (PCA) were used to classify into different groups the samples of diesel oil, biodiesel, vegetal oil and residual oil, as well as, to identify addition of non-transesterified residual vegetable oil, instead of biodiesel, to the diesel oil. Using this method, the samples of diesel oil, mixtures of biodiesel in diesel and mixtures of residual oil in diesel were separated into well-defined groups.

Composição química da precipitação úmida da região metropolitana de Porto Alegre, Brasil, 2005- 2007

This work aims to quantify the wet precipitation the Metropolitan Area of Porto Alegre (MAPA), in southern Brazil, through the analysis of major ions (by ion chromatography) and metallic elements (ICP/AES). By principal components analysis and cluster analysis was possible to identify the influence of natural and anthropic sources in wet precipitation. The results indicated of the higher contribution to the ions NH4+, SO4(2-) and Ca2+. Thus it was possible to identify the contribution of anthropogenic sources in wet precipitation in the study area, such as power plants, oil refineries, steel and vehicle emissions.

Correlação entre precursores e voláteis em café arábica brasileiro processado pelas vias seca, semiúmida e úmida e discriminação através da análise por componentes principais

The influence of pre-processing of arabica coffee beans on the composition of volatile precursors including sugars, chlorogenic acids, phenolics, proteins, aminoacids, trigonelline and fatty acids was assessed and correlated with volatiles formed during roasting. Reducing sugars and free aminoacids were highest for natural coffees whereas total sugars, chlorogenic acids and trigonelline were highest for washed coffees. The highest correlation was observed for total phenolics and volatile phenolics (R= 0.999). Experimental data were evaluated by Principal Components Analysis and results showed that washed coffees formed a distinct group in relation to semi-washed and natural coffees.

Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy and chemometric techniques for the determination of adulteration in petrodiesel/biodiesel blends

We propose an analytical method based on fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy to detect the adulteration of petrodiesel and petrodiesel/palm biodiesel blends with African crude palm oil. The infrared spectral fingerprints from the sample analysis were used to perform principal components analysis (PCA) and to construct a prediction model using partial least squares (PLS) regression. The PCA results separated the samples into three groups, allowing identification of those subjected to adulteration with palm oil. The obtained model shows a good predictive capacity for determining the concentration of palm oil in petrodiesel/biodiesel blends. Advantages of the proposed method include cost-effectiveness and speed; it is also environmentally friendly.

REDUÇÃO DE VARIÁVEIS DE ENTRADA DE REDES NEURAIS ARTIFICIAIS A PARTIR DE DADOS DE ANÁLISE DE COMPONENTES PRINCIPAIS NA MODELAGEM DE OXIGÊNIO DISSOLVIDO

The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

Evaluating multidimensional visualization techniques in data mining tasks

Visual data mining (VDM) tools employ information visualization techniques in order to represent large amounts of high-dimensional data graphically and to involve the user in exploring data at different levels of detail. The users are looking for outliers, patterns and models – in the form of clusters, classes, trends, and relationships – in different categories of data, i.e., financial, business information, etc. The focus of this thesis is the evaluation of multidimensional visualization techniques, especially from the business user’s perspective. We address three research problems. The first problem is the evaluation of projection-based visualizations with respect to their effectiveness in preserving the original distances between data points and the clustering structure of the data. In this respect, we propose the use of existing clustering validity measures. We illustrate their usefulness in evaluating five visualization techniques: Principal Components Analysis (PCA), Sammon’s Mapping, Self-Organizing Map (SOM), Radial Coordinate Visualization and Star Coordinates. The second problem is concerned with evaluating different visualization techniques as to their effectiveness in visual data mining of business data. For this purpose, we propose an inquiry evaluation technique and conduct the evaluation of nine visualization techniques. The visualizations under evaluation are Multiple Line Graphs, Permutation Matrix, Survey Plot, Scatter Plot Matrix, Parallel Coordinates, Treemap, PCA, Sammon’s Mapping and the SOM. The third problem is the evaluation of quality of use of VDM tools. We provide a conceptual framework for evaluating the quality of use of VDM tools and apply it to the evaluation of the SOM. In the evaluation, we use an inquiry technique for which we developed a questionnaire based on the proposed framework. The contributions of the thesis consist of three new evaluation techniques and the results obtained by applying these evaluation techniques. The thesis provides a systematic approach to evaluation of various visualization techniques. In this respect, first, we performed and described the evaluations in a systematic way, highlighting the evaluation activities, and their inputs and outputs. Secondly, we integrated the evaluation studies in the broad framework of usability evaluation. The results of the evaluations are intended to help developers and researchers of visualization systems to select appropriate visualization techniques in specific situations. The results of the evaluations also contribute to the understanding of the strengths and limitations of the visualization techniques evaluated and further to the improvement of these techniques.

Soil penetration resistance analysis by multivariate and geostatistical methods

The penetration resistance (PR) is a soil attribute that allows identifies areas with restrictions due to compaction, which results in mechanical impedance for root growth and reduced crop yield. The aim of this study was to characterize the PR of an agricultural soil by geostatistical and multivariate analysis. Sampling was done randomly in 90 points up to 0.60 m depth. It was determined spatial distribution models of PR, and defined areas with mechanical impedance for roots growth. The PR showed a random distribution to 0.55 and 0.60 m depth. PR in other depths analyzed showed spatial dependence, with adjustments to exponential and spherical models. The cluster analysis that considered sampling points allowed establishing areas with compaction problem identified in the maps by kriging interpolation. The analysis with main components identified three soil layers, where the middle layer showed the highest values of PR.

Multivariate statistical analysis to support the minimum streamflow regionalization

ABSTRACT This study aimed to develop a methodology based on multivariate statistical analysis of principal components and cluster analysis, in order to identify the most representative variables in studies of minimum streamflow regionalization, and to optimize the identification of the hydrologically homogeneous regions for the Doce river basin. Ten variables were used, referring to the river basin climatic and morphometric characteristics. These variables were individualized for each of the 61 gauging stations. Three dependent variables that are indicative of minimum streamflow (Q7,10, Q90 and Q95). And seven independent variables that concern to climatic and morphometric characteristics of the basin (total annual rainfall – Pa; total semiannual rainfall of the dry and of the rainy season – Pss and Psc; watershed drainage area – Ad; length of the main river – Lp; total length of the rivers – Lt; and average watershed slope – SL). The results of the principal component analysis pointed out that the variable SL was the least representative for the study, and so it was discarded. The most representative independent variables were Ad and Psc. The best divisions of hydrologically homogeneous regions for the three studied flow characteristics were obtained using the Mahalanobis similarity matrix and the complete linkage clustering method. The cluster analysis enabled the identification of four hydrologically homogeneous regions in the Doce river basin.

Evaluation of phenolic compounds content and in vitro antioxidant activity of red wines produced from Vitis labrusca grapes

Wine production in the northern Curitiba, Paraná, Brazil, specifically the communes of Colombo and Almirante Tamandaré, is based mainly on the utilization of Vitis labrusca grapes var. Bordô (Ives). Total sugar content, pH, and total acidity were analyzed in red wine samples from 2007 and 2008 vintages following official methods of analysis. Moreover, total phenolic, flavonoid, and tannin contents were analyzed by colorimetric methodologies and the antioxidant activity was determined using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical methodology. Phenolic compounds were identified by high performance liquid chromatography. The total phenolic content of wine samples presented concentrations varying between 1582.35 and 2896.08 mg gallic acid.L-1 since the major part corresponds to flavonoid content. In these compounds' concentration range, a direct relationship between phenolic compounds content and levels of antioxidant activity was not observed. Among the identified phenolic compounds, chlorogenic, caffeic, and syringic acids were found to be the major components. Using three principal components, it was possible to explain 81.36% of total variance of the studied samples. Principal Components Analysis does not differentiate between vintages.

Application of exploratory factor analysis to assess fish consumption in a university community

The objective of this research was to use the technique of Exploratory Factor Analysis (EFA) for the adequacy of a tool for the assessment of fish consumption and the characteristics involved in this process. Data were collected during a campaign to encourage fish consumption in Brazil with the voluntarily participation of members of a university community. An assessment instrument consisting of multiple-choice questions and a five-point Likert scale was designed and used to measure the importance of certain attributes that influence the choice and consumption of fish. This study sample was composed of of 224 individuals, the majority were women (65.6%). With regard to the frequency of fish consumption, 37.67% of the volunteers interviewed said they consume the product two or three times a month, and 29.6% once a week. The Exploratory Factor Analysis (EFA) was used to group the variables; the extraction was made using the principal components and the rotation using the Quartimax method. The results show clusters in two main constructs, quality and consumption with Cronbach Alpha coefficients of 0.75 and 0.69, respectively, indicating good internal consistency.

Characterization of biomasses from the north and northeast regions of Brazil for processes in biorefineries

Abstract In search for renewable energy sources, the Brazilian residual biomasses stand out due to their favorable physical and chemical properties, low cost, and their being less pollutant. Therefore, they are likely to be used in biorefineries in the production of chemical inputs to substitute fossil fuels. This substitution is possible due to the high contents of carbohydrates (>40%), low contents of extractives (<20%), ashes (<8%) and moisture (<8%) found in these residual biomasses. High calorific values of all residues also offer them the chance to be used in combustion. A principal components analysis (PCA) was performed for better understanding of the samples and their hysic-chemical properties. Thus, this study aimed to characterize biomasses from the north (babassu residues, such as mesocarp and endocarp; pequi and Brazil nut) and northeast (agave and coconut) regions of Brazil, in order to contribute to the preservation of the environment and strengthen the economy of the region.