7 resultados para GC analysis

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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This paper presents the development of a procedure, which enables the analysis of nine pharmaceutical drugs in wastewater using gas chromatography-mass spectrometry (GC-MS) associated with solid-phase microextraction (SPME) for the sample preparation. Experimental design was applied to optimize the in situ derivatization and the SPME extraction conditions. Ethyl chloroformate (ECF) was employed as derivatizing agent and polydimethylsiloxane-divinylbenzene (PDMS-DVB) as the SPME fiber coating. A fractional factorial design was used to evaluate the main factors for the in situ derivatization and SPME extraction. Thereafter, a Doehlert matrix design was applied to find out the best experimental conditions. The method presented a linear range from 0.5 to 10 mu g/L, and the intraday and interday precision were lower than 16%. Applicability of the method was verified from real influent and effluent samples of a wastewater treatment plant, as well as from samples of an industry wastewater and a river.

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A methodology to analyze organochlorine pesticides (OCPs) in water samples has been accomplished by using headspace stir bar sorptive extraction (HS-SBSE). The bars were in house coated with a thick film of PDMS in order to properly work in the headspace mode. Sampling was done by a novel HS-SBSE system whereas the analysis was performed by capillary GC coupled mass spectrometric detection (HS-SBSE-GC-MS). The extraction optimization, using different experimental parameters has been established by a standard equilibrium time of 120 min at 85 degrees C. A mixture of ACN/toluene as back extraction solvent promoted a good performance to remove the OCPs sorbed in the bar. Reproducibility between 2.1 and 14.8% and linearity between 0.96 and 1.0 were obtained for pesticides spiked in a linear range between 5 and 17 ng/g in water samples during the bar evaluation.

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Comprehensive two-dimensional gas chromatography (GC x GC) is a powerful technique that provides excellent separation and identification of analytes in highly complex samples with considerable increase in GC peak capacities. However, since second dimension analyses are very fast, detectors with a rapid acquisition rate are required. Over the last years, quite a number of studies have discussed the potential and limitations of the combination GC x GC with a variety of quadrupole mass spectrometers. The present research focuses on the evaluation of qMS effectiveness at a 10,000-amu/s scan speed and 20-Hz scan frequency for the identification (full scan mode acquisition-TIC) and quantification (extracted ion chromatogram) of target pesticide residues in tomato samples. The following MS parameters have been evaluated: number of data points per peak, mass spectrum quality, peak skewing, and sensitivity. The validated proposed GC x GC/qMS method presented satisfactory results in terms of repeatability (coefficient of variation lower than 15%), accuracy (84-117%), and linearity (ranging from 25 to 500 ng/g), while significant enhancement in sensitivity was observed (a factor of around 10) under scan conditions. (C) 2012 Elsevier B.V. All rights reserved.

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A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions. The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC(max). The output of GC(max) coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC(max) is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC(max) algorithm runs in linear time with respect to the variable M=|C|+|Z|, where |C| is the image scene size and |Z| is the size of the allowable range, Z, of the associated weight/affinity function. For most implementations, Z is identical to the set of allowable image intensity values, and its size can be treated as small with respect to |C|, meaning that O(M)=O(|C|). In such a situation, GC(max) runs in linear time with respect to the image size |C|. We show that the output of GC(max) constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the a"" (a) norm ayenF (P) ayen(a) of the map F (P) that associates, with every element e from the boundary of an object P, its weight w(e). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ayenF (P) ayen (q) for qa[1,a]. Of these, the best known minimization problem is for the energy ayenF (P) ayen(1), which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm. We notice that a minimization problem for ayenF (P) ayen (q) , qa[1,a), is identical to that for ayenF (P) ayen(1), when the original weight function w is replaced by w (q) . Thus, any algorithm GC(sum) solving the ayenF (P) ayen(1) minimization problem, solves also one for ayenF (P) ayen (q) with qa[1,a), so just two algorithms, GC(sum) and GC(max), are enough to solve all ayenF (P) ayen (q) -minimization problems. We also show that, for any fixed weight assignment, the solutions of the ayenF (P) ayen (q) -minimization problems converge to a solution of the ayenF (P) ayen(a)-minimization problem (ayenF (P) ayen(a)=lim (q -> a)ayenF (P) ayen (q) is not enough to deduce that). An experimental comparison of the performance of GC(max) and GC(sum) algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as the influence of the choice of the seeds on the output.

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Background and Aim: The identification of gastric carcinomas (GC) has traditionally been based on histomorphology. Recently, DNA microarrays have successfully been used to identify tumors through clustering of the expression profiles. Random forest clustering is widely used for tissue microarrays and other immunohistochemical data, because it handles highly-skewed tumor marker expressions well, and weighs the contribution of each marker according to its relatedness with other tumor markers. In the present study, we e identified biologically- and clinically-meaningful groups of GC by hierarchical clustering analysis of immunohistochemical protein expression. Methods: We selected 28 proteins (p16, p27, p21, cyclin D1, cyclin A, cyclin B1, pRb, p53, c-met, c-erbB-2, vascular endothelial growth factor, transforming growth factor [TGF]-beta I, TGF-beta II, MutS homolog-2, bcl-2, bax, bak, bcl-x, adenomatous polyposis coli, clathrin, E-cadherin, beta-catenin, mucin (MUC) 1, MUC2, MUC5AC, MUC6, matrix metalloproteinase [ MMP]-2, and MMP-9) to be investigated by immunohistochemistry in 482 GC. The analyses of the data were done using a random forest-clustering method. Results: Proteins related to cell cycle, growth factor, cell motility, cell adhesion, apoptosis, and matrix remodeling were highly expressed in GC. We identified protein expressions associated with poor survival in diffuse-type GC. Conclusions: Based on the expression analysis of 28 proteins, we identified two groups of GC that could not be explained by any clinicopathological variables, and a subgroup of long-surviving diffuse-type GC patients with a distinct molecular profile. These results provide not only a new molecular basis for understanding the biological properties of GC, but also better prediction of survival than the classic pathological grouping.

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Insect cuticular hydrocarbons including relatively non-volatile chemicals play important roles in cuticle protection and chemical communication. The conventional procedures for extracting cuticular compounds from insects require toxic solvents, or non-destructive techniques that do not allow storage of subsequent samples, such as the use of SPME fibers. In this study, we describe and tested a non-lethal process for extracting cuticular hydrocarbons with styrene-divinylbenzene copolymers, and illustrate the method with two species of bees and one species of beetle. The results demonstrate that these compounds can be efficiently trapped by ChromosorbA (R) (SUPELCO) and that this method can be used as an alternative to existing methods.

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The objective of this study was to evaluate the push-out bond strength of fiberglass resin reinforced bonded with five ionomer cements. Also, the interface between cement and dentin was inspected by means of SEM. Fifty human canines were chose after rigorous scrutiny process, endodontically treated and divided randomly into five groups (n = 3) according to cement tested: Group I – Ionoseal (VOCO), Group II – Fugi I (GC), Group III – Fugi II Improved (GC), Group IV – Rely X Luting 2 (3M ESPE), Group V – Ketac Cem (3M ESPE). The post-space was prepared to receive a fiberglass post, which was tried before cementation process. No dentin or post surface pretreatment was carried out. After post bonding, all roots were cross-sectioned to acquire 3 thin-slices (1 mm) from three specific regions of tooth (cervical, medium and apical). A Universal test machine was used to carry out the push-out test with cross-head speed set to 0.5mm/mim. All failed specimens were observed under optical microscope to identify the failure mode. Representative specimens from each group was inspected under SEM. The data were analyzed by Kolmogorov-Smirnov and Levene’s tests and by two-way ANOVA, and Tukey’s port hoc test at a significance level of 5%. It was compared the images obtained for determination of types of failures more occurred in different levels. SEM inspection displayed that all cements filled the space between post and dentin, however, some imperfections such bubles and voids were noticed in all groups in some degree of extension. The push-out bond strength showed that cement Ketac Cem presented significant higher results when compared to the Ionoseal (P = 0.02). There were no statistical significant differences among other cements.