33 resultados para SPME sampling


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The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

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The technique of solid phase microextraction (SPME) was used for the extraction of halogenated contaminants of water samples from three cities of the State of São Paulo and the extracts were submitted to gas chromatographic analysis with electron capture detection (GC-ECD). In the samples of water collected at the city of São Paulo the detected level of trihalomethanes (THM) expressed as the sum of chloroform, dibromochloromethane and dichlorobromomethane, were higher than the permissible limit established by the Brazilian regulation. In the samples collected at the two other cities the level of any of the three THM remained below the sensitivity of the ECD.

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The pyrethroids bifenthrin, permethrin, cypermethrin and deltamethrin were extracted by solid phase extraction (SPE) and solid phase microextraction (SPME). The analysis were performed on a gas chromatograph with electron capture detection (GC-ECD). Octadecil Silano-C18, Florisil and Silica stationary phases were studied for SPE. Better results were obtained for Florisil which gave recoveries from 80% to 108%. Pyrethroids extraction by SPME showed a linear response and a detection limit of 10 pg ml-1. Although the data showed that the two extraction methods were able to isolate the pesticide residues from water samples, the best results were obtained by using SPME which is more sensitive, faster, cheeper, being a more useful technique for the analysis of pyrethroids in drinking water.

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In this study a new approach, solid phase micro extraction (SPME), is used in the evaluation of the infinite dilution activity coefficient of the solute in a given solvent. It is the purpose of the current work to demonstrate a different approach to obtain the data needed for studying the solution thermodynamics of binary liquid mixtures as well as for designing multi-component separations. The solutes investigated at the temperature 298.15 K were toluene, ethyl benzene and xylene in the solvent methanol.

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Evaluation of the pollution by the herbicides alachlor, propanil and atrazine in water samples from four rivers in the cities of Turvo and Meleiro, south of Santa Catarina State, was made using the SPME-GC-ECD method. The proposed method was optimized and validated. The correlation coefficients were higher than 0.997 and linear ranges of the analytical curves were 0.1-4; 0.1-2.5 and 0.1-5 µg L-1 for atrazine, alachlor and propanil, respectively. The herbicides were quantified by GC-ECD and identified by GC-MS. Both of the selected rivers presented contamination by at least one of the studied herbicides.

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Methyl ethyl ketone (MEK) is a solvent commonly used in chemical, paint and shoe industry. The aim of this study was to develop and validate a method for urinary quantification of MEK, employing headspace solid phase micro extraction sampling (SPME) coupled to gas chromatography with flame ionization detection (GC-FID). The calibration curve (y=4.6851x-0.0011) presented good linearity with r²=0.9993. Accuracy (94-109%), intra-assay precision (4.07-5.91%) and inter-assay precision (3.03-5.62%) were acceptable. The quantification limit was 0.19 mg/L. This low cost method can be used routinely in the biological monitoring of occupational exposure to MEK, according to the requirements of the Brazilian legislation.

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In this work a new method (SPME-GC/FID) was developed to analyze the activity of binary liquid mixtures. The purpose is to demonstrate that SPME is capable to be used to determinate activity coefficients at infinite dilution knowing the fiber properties, with a lower cost than the conventional methods encountered in literature such as GLC. The activity coefficients at infinite dilution in furfural for n-hexane, n-heptane and cyclohexane at 298.15 K was determined using SPME and deviations of literature data was about 7%.

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This paper reports on the identification of volatile and semi-volatile compounds and a comparison of the chromatographic profiles obtained by Headspace Solid-Phase Microextraction/Gas Chromatography with Mass Spectrometry detection (HS-SPME-GC-MS) of dried leaves of Mikania glomerata Sprengel (Asteraceae), also known as 'guaco.' Three different types of commercial SPME fibers were tested: polydimethylsiloxane (PDMS), polydimethylsiloxane/divinylbenzene (PDMS/DVB) and polyacrylate (PA). Fifty-nine compounds were fully identified by HS-SPME-HRGC-MS, including coumarin, a marker for the quality control of guaco-based phytomedicines; most of the other identified compounds were mono- and sesquiterpenes. PA fibers performed better in the analysis of coumarin, while PDMS-DVB proved to be the best choice for a general and non-selective analysis of volatile and semi-volatile guaco-based compounds. The SPME method is faster and requires a smaller sample than conventional hydrodistillation of essential oils, providing a general overview of the volatile and semi-volatile compounds of M. glomerata.

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This study was carried to evaluate the efficiency of the Bitterlich method in growth and yield modeling of the even-aged Eucalyptus stands. 25 plots were setup in Eucalyptus grandis cropped under a high bole system in the Central Western Region of Minas Gerais, Brazil. The sampling points were setup in the center of each plot. The data of four annual mesurements were colleted and used to adjust the three model types using the age, the site index and the basal area as independent variables. The growths models were fitted for volume and mass of trees. The efficiency of the Bitterlich method was confirmed for generating the data for growth and yield modeling.

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In order to verify Point-Centered Quarter Method (PCQM) accuracy and efficiency, using different numbers of individuals by per sampled area, in 28 quarter points in an Araucaria forest, southern Paraná, Brazil. Three variations of the PCQM were used for comparison associated to the number of sampled individual trees: standard PCQM (SD-PCQM), with four sampled individuals by point (one in each quarter), second measured (VAR1-PCQM), with eight sampled individuals by point (two in each quarter), and third measuring (VAR2-PCQM), with 16 sampled individuals by points (four in each quarter). Thirty-one species of trees were recorded by the SD-PCQM method, 48 by VAR1-PCQM and 60 by VAR2-PCQM. The level of exhaustiveness of the vegetation census and diversity index showed an increasing number of individuals considered by quadrant, indicating that VAR2-PCQM was the most accurate and efficient method when compared with VAR1-PCQM and SD-PCQM.

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Taking into account that the sampling intensity of soil attributes is a determining factor for applying of concepts of precision agriculture, this study aims to determine the spatial distribution pattern of soil attributes and corn yield at four soil sampling intensities and verify how sampling intensity affects cause-effect relationship between soil attributes and corn yield. A 100-referenced point sample grid was imposed on the experimental site. Thus, each sampling cell encompassed an area of 45 m² and was composed of five 10-m long crop rows, where referenced points were considered the center of the cell. Samples were taken from at 0 to 0.1 m and 0.1 to 0.2 m depths. Soil chemical attributes and clay content were evaluated. Sampling intensities were established by initial 100-point sampling, resulting data sets of 100; 75; 50 and 25 points. The data were submitted to descriptive statistical and geostatistics analyses. The best sampling intensity to know the spatial distribution pattern was dependent on the soil attribute being studied. The attributes P and K+ content showed higher spatial variability; while the clay content, Ca2+, Mg2+ and base saturation values (V) showed lesser spatial variability. The spatial distribution pattern of clay content and V at the 100-point sampling were the ones which best explained the spatial distribution pattern of corn yield.

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The aim of this study was to compare two methods of tear sampling for protein quantification. Tear samples were collected from 29 healthy dogs (58 eyes) using Schirmer tear test (STT) strip and microcapillary tubes. The samples were frozen at -80ºC and analyzed by the Bradford method. Results were analyzed by Student's t test. The average protein concentration and standard deviation from tears collected with microcapillary tube were 4.45mg/mL ±0.35 and 4,52mg/mL ±0.29 for right and left eyes respectively. The average protein concentration and standard deviation from tears collected with Schirmer Tear Test (STT) strip were and 54.5mg/mL ±0.63 and 54.15mg/mL ±0.65 to right and left eyes respectively. Statistically significant differences (p<0.001) were found between the methods. In the conditions in which this study was conducted, the average protein concentration obtained with the Bradford test from tear samples obtained by Schirmer Tear Test (STT) strip showed values higher than those obtained with microcapillary tube. It is important that concentration of tear protein pattern values should be analyzed according the method used to collect tear samples.

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Bioanalytical data from a bioequivalence study were used to develop limited-sampling strategy (LSS) models for estimating the area under the plasma concentration versus time curve (AUC) and the peak plasma concentration (Cmax) of 4-methylaminoantipyrine (MAA), an active metabolite of dipyrone. Twelve healthy adult male volunteers received single 600 mg oral doses of dipyrone in two formulations at a 7-day interval in a randomized, crossover protocol. Plasma concentrations of MAA (N = 336), measured by HPLC, were used to develop LSS models. Linear regression analysis and a "jack-knife" validation procedure revealed that the AUC0-¥ and the Cmax of MAA can be accurately predicted (R²>0.95, bias <1.5%, precision between 3.1 and 8.3%) by LSS models based on two sampling times. Validation tests indicate that the most informative 2-point LSS models developed for one formulation provide good estimates (R²>0.85) of the AUC0-¥ or Cmax for the other formulation. LSS models based on three sampling points (1.5, 4 and 24 h), but using different coefficients for AUC0-¥ and Cmax, predicted the individual values of both parameters for the enrolled volunteers (R²>0.88, bias = -0.65 and -0.37%, precision = 4.3 and 7.4%) as well as for plasma concentration data sets generated by simulation (R²>0.88, bias = -1.9 and 8.5%, precision = 5.2 and 8.7%). Bioequivalence assessment of the dipyrone formulations based on the 90% confidence interval of log-transformed AUC0-¥ and Cmax provided similar results when either the best-estimated or the LSS-derived metrics were used.

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The aim of the present study was the assessment of volatile organic compounds produced by Sporidiobolus salmonicolor (CBS 2636) using methyl and ethyl ricinoleate, ricinoleic acid and castor oil as precursors. The analysis of the volatile organic compounds was carried out using Head Space Solid Phase Micro-Extraction (HS - SPME). Factorial experimental design was used for investigating extraction conditions, verifying stirring rate (0-400 rpm), temperature (25-60 ºC), extraction time (10-30 minutes), and sample volume (2-3 mL). The identification of volatile organic compounds was carried out by Gas Chromatography with Mass Spectrum Detector (GC/MSD). The conditions that resulted in maximum extraction were: 60 ºC, 10 minutes extraction, no stirring, sample volume of 2.0 mL, and addition of saturated KCl (1:10 v/v). In the bio-production of volatile organic compounds the effect of stirring rate (120-200 rpm), temperature (23-33 ºC), pH (4.0-8.0), precursor concentration (0.02-0.1%), mannitol (0-6%), and asparagine concentration (0-0.2%) was investigated. The bio-production at 28 ºC, 160 rpm, pH 6,0 and with the addition of 0.02% ricinoleic acid to the medium yielded the highest production of VOCs, identified as 1,4-butanediol, 1,2,2-trimethylciclopropilamine, beta-ionone; 2,3-butanodione, pentanal, tetradecane, 2-isononenal, 4-octen-3-one, propanoic acid, and octadecane.

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(E)-2-nonenal is considered an important off-flavor of beer, related to the flavor of beer staling. In this study, a new method for determination of (E)-2-nonenal in beer using headspace solid-phase microextraction and gas chromatographic coupled mass spectrometry (HS-SPME-GC-MS) was developed and applied in Brazilian beer samples. The extractions were carried out in CAR-PDMS (carboxen-polydimethylsiloxane) fiber and the best results were found with 15 minutes of equilibrium and 90 minutes of extraction at 50 °C. The method was linear in the range from 0.02 to 4.0 μg.L-1 with correlation coefficient of 0.9994. The limits of detection and quantification were 0.01 and 0.02 μg.L-1, respectively. 96.5% of recovery and 4% precision (RSD) were obtained in the fortification of beer samples with 2.0 μg.L-1 of (E)-2-nonenal. The developed method proved to be simple, efficient and highly sensitive to the determination of this analyte being easily applied in the quality control of the brewery. (E)-2-nonenal was found in all beer samples analyzed with levels between 0.17 and 0.42 μg.L-1.