966 resultados para chemical components


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Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

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A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

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The knowledge of how animals deposited chemical components as water, protein, fat and ash in the carcass is importance for the formulation of a balanced diet, allowing maximum performance with a low environmental impact. So, the study was carried out to evaluate the influence of different tilapia strains (Chitralada, Commercial, Red and Universidade Federal de Lavras [UFLA]) on the deposition of bodily chemical components in the carcass. The bodily components analyzed were water, protein, fat and ash. For the determination of the bodily chemical deposition curves by age, the exponential, Brody, logistic, Gompertz and von Bertalanffy models were adjusted. The Commercial and UFLA strains deposited water at a faster speed (P<0.05) compared with the remaining strains. As for protein, the Red strain had a lower estimated maturity weight (49.37 g), and was more precocious (202 days) with regard to maximum deposition in comparison to the other strains (Chitralada, UFLA and Commercial) in which there was an estimated maturity weight of 231.5 g and maximum depositionfor 337 days. There were no differences (P>0.05) for the logistic model parameter between Red, UFLA and Commercial strains for fat, which presented a maximum fat deposition (0.23 g) at 310 days of age. Regarding ash deposition, the Commercial strain presented a higher maximum deposition (0.10 g) at 337 days, occurring later than the other strains that presented maximum deposition (0.033g) at 254 days of age. Thus, it was concluded that the genetic strains evaluated differ in chemical deposition curves of water, protein, fat and ash.

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The determination of the chemical composition of body and carcass is important in nutritional and growth regulation studies. The purpose of this study was to develop equations to predict the chemical composition of body and carcass using chemical composition of body components. Twenty 3/4Boer x 1/4Saanen crossbred male kids, weighing from 20 to 35 kg, were used in this study. The empty body chemical composition was measured by grinding all body components and sampling for chemical analyses. The body components used to estimate body and carcass composition were: neck, fore leg, ribs, loin, hind leg, 9-11 th rib section, non-carcass components (head plus feet, organs plus blood, and hide), visceral fat, and kidney fat. The chemical composition of organs plus blood and 9-11 th rib section had the highest precision to estimate percentage of fat, protein, and water in the body (r(2) of 0.94, 0.82, and 0.90, respectively). For carcass composition, the chemical composition of ribs was the best component to predict all carcass chemical components; however, the equations to estimate the percentages of protein and ash showed a low precision (r(2) = 0.48, 0.44, respectively). The 9-11 th rib section was accurate and precise to estimate carcass fat percentage. We concluded the chemical composition of the body of 3/4Boer x 1/4Saanen crossbred male kids was highly correlated with the composition of body parts, specifically organs plus blood and 9-11 th rib section. Further studies should focus on evaluating these body parts for different breeds and genders under different production scenarios. (C) 2007 Elsevier B.V. All rights reserved.

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In order to evaluate the influence of ambient aerosol particles on cloud formation, climate and human health, detailed information about the concentration and composition of ambient aerosol particles is needed. The dura-tion of aerosol formation, growth and removal processes in the atmosphere range from minutes to hours, which highlights the need for high-time-resolution data in order to understand the underlying processes. This thesis focuses on characterization of ambient levels, size distributions and sources of water-soluble organic carbon (WSOC) in ambient aerosols. The results show that in the location of this study typically 50-60 % of organic carbon in fine particles is water-soluble. The amount of WSOC was observed to increase as aerosols age, likely due to further oxidation of organic compounds. In the boreal region the main sources of WSOC were biomass burning during the winter and secondary aerosol formation during the summer. WSOC was mainly attributed to a fine particle mode between 0.1 - 1 μm, although different size distributions were measured for different sources. The WSOC concentrations and size distributions had a clear seasonal variation. Another main focus of this thesis was to test and further develop the high-time-resolution methods for chemical characterization of ambient aerosol particles. The concentrations of the main chemical components (ions, OC, EC) of ambient aerosol particles were measured online during a year-long intensive measurement campaign conducted on the SMEAR III station in Southern Finland. The results were compared to the results of traditional filter collections in order to study sampling artifacts and limitations related to each method. To achieve better a time resolution for the WSOC and ion measurements, a particle-into-liquid sampler (PILS) was coupled with a total organic carbon analyzer (TOC) and two ion chromatographs (IC). The PILS-TOC-IC provided important data about diurnal variations and short-time plumes, which cannot be resolved from the filter samples. In summary, the measurements made for this thesis provide new information on the concentrations, size distribu-tions and sources of WSOC in ambient aerosol particles in the boreal region. The analytical and collection me-thods needed for the online characterization of aerosol chemical composition were further developed in order to provide more reliable high-time-resolution measurements.