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A simple TLC and HPTLC method for separation of selected steroid drugs

100%
Nowakowska J., Pikul P., Ciura K., Piotrowicz J.
Open Chemistry
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2013
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vol. 11
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issue 8
1297-1308
EN
Chromatographic properties of five steroid drugs: cortisone, hydrocortisone, methylprednisolone, prednisolone and norgestrel have been studied by normal-, reversed-phase and hydrophilic neutral cyano-bonded silica stationary phase with five binary mobile phases (acetonitrile-water, acetonitrile-DMSO, acetonitrile-methanol, acetone-petroleum ether, acetone-water) in which the concentration of organic modifier was varied from 0 to 100% (v/v). This study reports the optimization of steroid hormones separation. Chromatographic retention data and possible retention mechanisms are discussed. Separation abilities of mobile and stationary phases were studied using the principal component analysis method. The best separation of methylprednisolone and prednisolone is with a chromatographic system included silica gel as stationary phase and mixture of acetonitrile and DMSO (10:90 v/v). These two anti-inflammatory drugs can be fast separated from norgestrel when CN is used as stationary phase and acetone and water (40:60 v/v) as mobile phase. The highest values of the parameter Δ(ΔG°) and alfa for cortisone and hydrocortisone was observed in case of using CN as stationary phase and water-acetonitryle (40:60 v/v) as mobile phase. [...]
2
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Assessment of elemental contamination in the bottom sediments from a dam reservoir using a sequential extraction technique and chemometric analysis

100%
Dabioch M., Kita A., Zerzucha P., Pytlakowska K.
Open Chemistry
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2013
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vol. 11
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issue 12
1981-1995
EN
The concentration of elements in sediments is an important aspect of the quality of water ecosystems. The element concentrations in bottom sediments from Goczalkowice Reservoir, Poland, were investigated to determine the levels, accumulation and distribution of elements; to understand the contamination and potential toxicity of elements; and to trace the possible source of pollution. Sediments were collected from 8 sampling points. The functional speciation, mobility and bioavailability of elements were evaluated by means of modified Tessier sequential extraction. The element contents were measured by optical emission spectrometry with inductively coupled plasma. The experimental results were analyzed using chemometric methods such as principal component analysis and cluster analysis to elucidate the metal distributions, correlations and associations. The highest concentrations of most elements were found at the center of the reservoir. The distribution of metals in the individual fractions was varied. To assess the extent of anthropogenic impact indices, contamination factor, degree of contamination, metal pollution index and risk assessment code were applied. The calculated factors showed the highest contamination factor and the ability of chromium to be released from sediments. The degree of contamination showed that the area is characterized by a very high contamination. Strontium and manganese showed high potential ecological risk for sediments. [...]
3
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Spatial variations in the distribution of trace ionic impurities in the water-steam cycle in a thermal power plant based on a multivariate statistical approach

100%
Živojinović D., Rajaković-Ognjanović V., Onjia A., Rajaković L.
Open Chemistry
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2013
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vol. 11
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issue 9
1456-1470
EN
Abstract In this study, a multivariate statistical approach was used to identify the key variables responsible for process water quality in a power plant. The ion species that could cause corrosion in one of the major thermal power plants (TPP) in Serbia were monitored. A suppressed ion chromatographic (IC) method for the determination of the target anions and cations at trace levels was applied. In addition, some metals important for corrosion, i.e., copper and iron, were also analysed by the graphite furnace atomic absorption spectrophotometric (GFAAS) method. The control parameters, i.e., pH, dissolved oxygen and silica, were measured on-line. The analysis of a series of representative samples from the TPP Nikola Tesla, collected in different plant operation modes, was performed. Every day laboratory and on-line analysis provides a large number of data in relation to the quality of water in the water-steam cycle (WSC) which should be evaluated and processed. The goal of this investigation was to apply multivariate statistical techniques and choose the most applicable technique for this case. Factor analysis (FA), especially principal component analysis (PCA) and cluster analysis (CA) were investigated. These methods were applied for the evaluation of the spatial/temporal variations of process water and for the estimation of 13 quality parameters which were monitored at 11 locations in the WSC in different working conditions during a twelve month period. It was concluded that PCA was the most useful method for identifying functional relations between the elements. After data reduction, four main factors controlling the variability were identified. Hierarchical cluster analysis (HCA) was applied for sample differentiation according to the sample location and working mode of the TPP. On the basis of this research, the new design of an optimal monitoring strategy for future analysis was proposed with a reduced number of measured parameters and with reduced frequency of their measurements. Graphical abstract [...]
4
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SVD-based principal component analysis of geochemical data

86%
Praus P.
Open Chemistry
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2005
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vol. 3
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issue 4
731-741
EN
Principal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration. The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.
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