116 resultados para Principle Component Analysis


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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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A study on the monitoring of glycerol oxidation catalyzed by gold nanoparticles supported on activated carbon under mild conditions by chemometric methods is presented. The reaction was monitored by mass spectrometry-electrospray ionization (ESI-MS) and comparatively by mid infrared spectroscopy (MIR). Concentration profiles of reagent and products were determined by chemometric tools such as Principal Component Analysis (PCA), Evolving Factor Analysis (EFA) and Multivariate Curve Resolution (MCR). The gold nanoparticle catalyst was relatively active in glycerol oxidation, favoring formation of high added value products. It was found that the reaction stabilization was reached at four hours, with approximately 70% glycerol conversion and high selectivity for glycerate.

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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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This study describes the use of Principal Component Analysis to evaluate the chemical composition of water produced from eight oil wells in three different production areas. A total of 609 samples of produced water, and a reference sample of seawater, were characterized according to their levels of salinity, calcium, magnesium, strontium, barium and sulphate (mg L-1) contents, and analyzed by using PCA with autoscaled data. The method allowed the identification of variables salinity, calcium and strontium as tracers for formation water, and variables magnesium and sulphate as tracers for seawater.

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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.

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In this research work the effects of four solvents and their mixtures on the extraction of chlorogenic acids, caffeine and trigonelline in crude extracts of four coffee cultivars, traditional red bourbon, IAPAR59, IPR101 and IPR108 cultivars, were investigated by UV spectrophotometry and UV spectra obtained from RP-HPLC-DAD. The experimental results and the principal component analysis of UV spectra showed that the effect of solvent extraction of the metabolites does not depend on cultivars, because the spectral characteristics are similar, but the concentrations are different. The UV and UV-DAD spectra for four simplex centroid design mixtures were also similar but the concentrations of caffeine, trigonelline and the chlorogenic acids are different and depend on the solvent used in the extraction.

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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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The antioxidant activities and polyphenolic levels of "assa peixe," "cambara," and "morrão de candeia" Brazilian honeys were investigated. Phenolic extracts of 11 honeys were evaluated spectrophotometrically to determine their total phenolic and flavonoid contents, and their antioxidant activities were measured using DPPH, ABTS, and FRAP assays. High-performance liquid chromatography coupled with diode array detection was applied to determine the phenolic composition of the honey extracts. The presence of fourteen phenolic compounds was established (eleven phenolic acids and three flavonoids), as well as HMF and abscisic acid. Principal component analysis was applied to classify the honey samples according to their floral origins.

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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 application of automated correlation optimized warping (ACOW) to the correction of retention time shift in the chromatographic fingerprints of Radix Puerariae thomsonii (RPT) was investigated. Twenty-seven samples were extracted from 9 batches of RPT products. The fingerprints of the 27 samples were established by the HPLC method. Because there is a retention time shift in the established fingerprints, the quality of these samples cannot be correctly evaluated by using similarity estimation and principal component analysis (PCA). Thus, the ACOW method was used to align these fingerprints. In the ACOW procedure, the warping parameters, which have a significant influence on the alignment result, were optimized by an automated algorithm. After correcting the retention time shift, the quality of these RPT samples was correctly evaluated by similarity estimation and PCA. It is demonstrated that ACOW is a practical method for aligning the chromatographic fingerprints of RPT. The combination of ACOW, similarity estimation, and PCA is shown to be a promising method for evaluating the quality of Traditional Chinese Medicine.

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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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A simple and sensitive spectrophotometric method is proposed for the simultaneous determination of protocatechuic acid and protocatechuic aldehyde. The method is based on the difference in the kinetic rates of the reactions of analytes with [Ag(NH3)2]+ in the presence of polyvinylpyrrolidone to produce silver nanoparticles. The data obtained were processed by chemometric methods using principal component analysis artificial neural network and partial least squares. Excellent linearity was obtained in the concentration ranges of 1.23-58.56 µg mL-1 and 0.08-30.39 µg mL-1 for PAC and PAH, respectively. The limits of detection for PAC and PAH were 0.039 and 0.025 µg mL-1, respectively.

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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.

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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.