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.

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.

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.

Multivariate study of Nile tilapia byproducts enriched with omega-3 and dried with different methods

Abstract The present work aimed at studying the effect of different drying methods applied to tilapia byproducts (heads, viscera and carcasses) fed with flaxseed, verifying the contents of omega-3 fatty acids. Two diets were given to the tilapia: a control and a flaxseed formulation, over the course of 60 days. After this period, they were slaughtered and their byproducts (heads, viscera and carcasses) were collected. These fish parts were analyzed in natura, lyophilized and oven dried. Byproducts from tilapia fed with flaxseed presented docosapentaenoic, eicopentaenoic and docosahexanoic fatty acids as a result of the enzymatic metabolism of the fish. The byproducts from the oven drying process had lower levels of polyunsaturated fatty acids. In the multivariate analysis, the byproducts from fish fed with flaxseed had a greater composition of fatty acids. The addition of flaxseed in fish diets, as well as the utilization of their byproducts, may become a good business strategy. Additionally, the byproducts may be dried to facilitate transport and storage.

Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI), remaining phosphorus (P-rem), and micronutrients (Zn, Fe, Mn, Cu and B). The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

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.

Integrated multivariate analysis to evaluate effects of pre-slaughter handling on pork quality

The aim of this study was to investigate the effect of pre-slaughter handling on the occurrence of PSE (Pale, Soft, and Exudative) meat in swine slaughtered at a commercial slaughterhouse located in the metropolitan region of Dourados, Mato Grosso do Sul, Brazil. Based on the database (n=1,832 carcasses), it was possible to apply the integrated multivariate analysis for the purpose of identifying, among the selected variables, those of greatest relevance to this study. Results of the Principal Component Analysis showed that the first five components explained 89.28% of total variance. In the Factor Analysis, the first factor represented the thermal stress and fatiguing conditions for swine during pre-slaughter handling. In general, this study indicated the importance of the pre-slaughter handling stages, evidencing those of greatest stress and threat to animal welfare and pork quality, which are transport time, resting period, lairage time before unloading, unloading time, and ambience.

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.

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.