Marek Błaś, Żaneta Polkowska, Vasil Simeonov, Stefan Tsakovski, Mieczysław Sobik, Katarzyna Kozak and Jacek Namieśnik
Snow samples were collected during winter 2011/2012 in three posts in the Western Sudety Mountains (Poland) in 3 consecutive phases of snow cover development, i.e. stabilisation (Feb 1st), growth (Mar 15th) and its ablation (Mar 27th). To maintain a fixed number of samples, each snow profile has been divided into six layers, but hydrochemical indications were made for each 10 cm section of core. The complete data set was subjected in the first run of chemometric data interpretation to Cluster Analysis as well as Principal Components Analysis. Further, Self-Organizing Maps, type of neutral network described by Kohonen were used for visualization and interpretation of large high-dimensional data sets. For each site the hierarchical Ward’s method of linkage, squared Euclidean distance as similarity measure, standardized raw data, cluster significance test according to Sneath’s criterion clustering of the chemical variables was done. Afterwards this grouping of the chemical variables was confirmed by the results from Principal Components Analysis. The major conclusion is that the whole system of three sampling sites four patterns of variable groupings are observed: the first one is related to the mineral salt impact; the second one - with the impact of secondary emissions and organic pollutants; next one - with dissolved matter effect and the last one - with oxidative influence, again with relation to anthropogenic activities like smog, coal burning, traffic etc. It might be also concluded that specificity of the samples is determined by the factors responsible for the data set structure and not by particular individual or time factors.
Plamen Katsarov, Georgi Gergov, Aylin Alin, Bissera Pilicheva, Yahya Al-Degs, Vasil Simeonov and Margarita Kassarova
The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.