The type A gasoline samples were analyzed by gas chromatography with flame ionization detector (GC-FID) which allowed quantifying and classifying of the various compounds into different classes of hydrocarbons. Several physicochemical parameters were evaluated according to the official methods in order to compare the results obtained against the limits established by the Agência Nacional de Petróleo, Gás Natural e Biocombustíveis (ANP, 2011). Additionally, principal component analysis (PCA) was applied to discriminate the samples studied, which revealed the separation of four groups according to their chemical composition determined in samples collected from the eight fuel distributors in the State of Pará.

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.

Os solos de terra preta arqueológica são ricos em matéria orgânica, contêm fragmentos cerâmicos e artefatos líticos e apresentam nutrientes em concentrações mais elevadas do que outros tipos de solos. Com o intuito de contribuir com informações sobre concentrações de micronutrientes disponíveis em solos de terra preta, foram avaliadas através de extrações químicas sequenciais, a distribuição e disponibilidade de Cu, Fe, Mn e Zn em um perfil de terra preta arqueológica no município de Juruti, estado do Pará. As maiores concentrações de Cu, Fe, Mn e Zn no perfil são encontradas na fração residual. Este estudo mostrou que ferro, manganês e zinco são preferencialmente disponíveis a partir das frações, associados aos óxidos de Fe-Mn, variando de 1265,39 a 1818,12 mg kg-1, 0,83 a 48,51 mg kg-1 e 1,92 a 12,05 mg kg-1, respectivamente, e o cobre a partir da matéria orgânica, variando de 0,13 a 0,45 mg kg-1.

The archaeological black earth soils are rich in organic matter, contain ceramic fragments and lithics artifacts and feature nutrients in higher concentrations than other types of soils. To contribute information about micronutrient concentrations available in black earth soils, the distribution and availability of Cu, Fe, Mn and Zn in the archaeological black earth profile from the municipal district of Juruti, Pará State, was evaluated through sequential chemical extractions. The largest concentrations of Cu, Fe, Mn and Zn in the profile are found in the residual fraction. This study showed that the iron, manganese and zinc are preferentially available from the fractions associated with Fe-Mn oxides ranging from 1265.39 to 1818.12 mg kg-1, 0.83 to 48.51 mg kg-1 and 1.92 to 12.05 mg kg-1 respectively, and the copper from organic matter, ranging from 0.13 to 0.45 mg kg-1.

The application of multivariate calibration techniques to multicomponent analysis by UV-VIS molecular absorption spectrometry is a powerful tool for simultaneous determination of several chemical species. However, when this methodology is accomplished manually, it is slow and laborious, consumes high amounts of reagents and samples, is susceptible to contaminations and presents a high operational cost. To overcome these drawbacks, a flow-batch analyser is proposed in this work. This analyser was developed for automatic preparation of standard calibration and test (or validation) mixtures. It was applied to the simultaneous determination of Cu2+, Mn2+ and Zn2+ in polyvitaminic and polymineral pharmaceutical formulations, using 4-(2-piridilazo) resorcinol as reagent and a UV-VIS spectrophotometer with a photodiode array detector. The results obtained with the proposed system are in good agreement with those obtained by flame atomic absorption spectrometry, which was employed as reference method. With the proposed analyser, the preparation of calibration and test mixtures can be accomplished about four hours, while the manual procedure requires at least two days. Moreover, it consumes smaller amounts of reagents and samples than the manual procedure. After the preparation of calibration and test mixtures, 60 samples h-1 can be carried out with the proposed flow-batch analyser.

Este artigo apresenta uma aplicação do método para determinação espectrofotométrica simultânea dos íons divalentes de cobre, manganês e zinco à análise de medicamento polivitamínico/polimineral. O método usa 4-(2-piridilazo) resorcinol (PAR), calibração multivariada e técnicas de seleção de variáveis e foi otimizado o empregando-se o algoritmo das projeções sucessivas (APS) e o algoritmo genético (AG), para escolha dos comprimentos de onda mais informativos para a análise. Com essas técnicas, foi possível construir modelos de calibração por regressão linear múltipla (RLM-APS e RLM-AG). Os resultados obtidos foram comparados com modelos de regressão em componentes principais (PCR) e nos mínimos quadrados parciais (PLS). Demonstra-se a partir do erro médio quadrático de previsão (RMSEP) que os modelos apresentam desempenhos semelhantes ao prever as concentrações dos três analitos no medicamento. Todavia os modelos RLM são mais simples pois requerem um número muito menor de comprimentos de onda e são mais fáceis de interpretar que os baseados em variáveis latentes.

The application of the method for the simultaneous spectrophotometric determination of the divalent ions of copper, manganese and zinc for analysis of a pharmaceutical formulation of polivitaminic/polimineral is reported. This method uses 4-(2-pyridylazo) resorcinol (PAR) and multivariate calibration and was optimized using the successive projections algorithm (SPA) and a genetic algorithm (GA) for choosing the best series of wavelengths for analysis. Thus the construction of calibration models based on multiple linear regression (MLR-SPA and MLR-GA respectively) was made possible. The results obtained were compared with models based on latent variables, principal component regression (PCR) and partial least square regression (PLS) through the criterion of the root mean square error of prediction (RMSEP). All the methods presented even performance but the RLM methods are simpler since they require a smaller number of wavelengths and are easier to interpret than those based on latent variables.