Cluster analysis

Chemometrically assisted RP-HPLC method development for efficient separation of ivabradine and its eleven impurities

The aim of this study was to develop a novel reversed-phase high-performance liquid chromatography (RP-HPLC) method for efficient separation of ivabradine and its 11 impurities. Similar polarity of impurities in the sample mixture made method optimization challenging and accomplishable only when different chemometric tools, such as principal component analysis (PCA), Box–Behnken design (BBD), and desirability function as a multicriteria approach, were employed. The presence of 3 positional isomers (impurities III, V, and VI), keto–enol tautomerism of impurity VII, and diastereoisomers of impurity X made separation of this complex mixture even more challenging. Chromatographic retention parameters obtained with the mobile phase consisting of 30 mM phosphate buffer and acetonitrile (80:20, v/v) on four different RP-HPLC columns at varying pH values (3.0, 4.0, and 5.0) were subjected to the PCA analysis to select the column with the most appropriate selectivity. Then the column temperature, pH of the aqueous component of mobile phase, phosphate buffer molarity and the organic solvent content in the mobile phase were estimated employing BBD. Valid and reliable mathematical models towards resolution of twelve critical peak pairs were obtained. After determination of the desirability making criteria for all responses, desirability functions were established and used in optimization. The proposed optimal chromatographic conditions included the Zorbax Eclipse Plus C18 chromatographic column (100 × 4.6 mm, 3.5 μm), the column temperature of 34 °C, the mobile phase flow rate of 1.6 mL min−1 and the UV detection at 220 nm. The mobile phase consisted of the 28 mM phosphate buffer at pH 6.0 and acetonitrile (85:15, v/v). Separation of one pair of positional isomers was not achieved, so methanol was added to the organic part of mobile phase in small increments with the optimal ratio of methanol to acetonitrile 59:41, v/v. The overall organic component of the mobile phase also increased to 18%, accelerating the chromatographic analysis.

Testing of “special base” columns in reversed-phase liquid chromatography

A methodology for building a chromatographic test aiming at characterizing special base stationary phases was described. Instead of choosing its conditions a priori, a "full" comprehensive test based on extended running conditions was performed on a 12 column set. The conditions were carefully chosen from their ability to take into account the solvent and the pH effects. Principal component analysis (PCA) has been combined to hierarchical cluster analysis both to provide interpreted classifications and to reduce drastically the test itself by eliminating redundant information. The final reduced test can be considered optimal because the minimized set of test conditions allows to provide as much information as in the initial full test.

Analysis of sources of dioxin contamination in sediments and soils using multivariate statistical methods and neural networks

Multivariate statistical methods and neuronal networks were used to evaluate the concentration dioxin patterns of a large data set (407 samples) in order to identify the dioxin sources of contaminated waters (sediment and suspended particulate matter samples). The evaluations indicated that a considerable proportion of the dioxin contamination of the river Elbe in the section between the Mulde tributary and the North Sea and their flood plains (soil samples) and the Port of Hamburg was caused by pollution originating from the Bitterfeld region, an industrial area of the former German Democratic Republic. The dioxin patterns of sediment samples from tributaries of the river Elbe in the Bitterfeld area itself are similar to dioxin patterns that can be attributed to metalworking processes. The dioxin patterns of the Hamburg inner city waters could be attributed to "incineration" dioxin sources, for example waste incineration plants. The results of cluster analysis applying different modes of distance measure and linkage compared well with neuronal networks. The number of clusters was determined based on the stability of the results of different cluster analyses and background information.

Assessment of relevant factors and relationships concerning human dermal exposure to pesticides in greenhouse applications

Principal component analysis (PCA) was applied to the gas chromatographic data obtained from 23 different greenhouse trials. This was used to establish which factors, including application technique (very small, small, medium and large drop-size), crop characteristics (short/tall, thin/dense) and pattern application of the operator (walking towards or away from the treated area) are relevant to the dermal exposure levels of greenhouse applicators. The results showed that the highest exposure by pesticides during field applications in greenhouses, in the climatic conditions and in the crop conditions typical of a southern European country, occurs on the lower legs and front thighs of the applicators. Similar results were obtained by hierarchical cluster analysis (HCA). Drop-size seems to be very important in determining total exposure, while height and density of crops have little influence on total exposure under the conditions of the present study. No pesticide type is a major factor in total exposure. The application of multiple regression analysis (MRA) allowed assessment of the relationships between the pesticide exposure of the less affected parts of the body with the most affected parts.