One of the purposes of chemical analysis is to find quick and efficient methods to answer complex analytical questions in the life sciences. New analytical methods, in particular, produce a flood of data which are often very badly arranged. An effective way to overcome this problem is to apply chemometric methods. As part of the following investigations, three brands of oregano were analysed to identify their volatile aroma-active compounds. Two techniques were applied--gas chromatograpy-olfactometry (GC-O) and human sensory evaluation. Aroma-impact compounds could be identified in the main brands of oregano with the aid of chemometric methods (principal-components analysis, hierarchical cluster analysis, linear discriminant analysis, partial least-squares regression). Therefore, it is possible to reduce the analysis of sensory and olfactometry to relevant attributes. This makes classifying new species easier, much faster, and less expensive and is the premise for quick and more economic identification of new potential genotypes for oregano plant breeding. A comprehensive list of oregano key odourants, determined by GC-O and human sensory evaluation using different methods of supervised and unsupervised pattern cognition, has not previously been published.

The purpose of this paper is to present the potential of the combination of source apportionment methods and geostatistics. We want to outline the possibilities of this combination for the investigation of soil pollution. Therefore, we focused on the identification of sources in the vicinity of an iron smelter and the different element distribution in this area. We determined the concentration of 15 elements in the aqua regia digestion of 60 soil samples in an area of 12 km 2 . In the current study, the application of two different source apportionment methods onto the data set and comparison of the results are presented. The focus was on absolute principal components score analysis with multiple linear regression and multivariate curve resolution with alternating least-squares. Four different sources in the region of interest could be detected. The source composition profiles and contribution profiles for both methods are alike. Furthermore, the distribution of the elements caused by each source with isoline plots could be visualized. The distribution is unique for each source and hence, element-and source-specific. Thus, the combination of the results of source apportionment methods with geostatistics is a powerful tool to evaluate and describe the content and distribution of metals in soil.Abbreviations: APCS-MLR, absolute principal components scores analysis followed by multiple linear regression; MCR-ALS, multivariate curve resolution with alternating least-squares; PMF, positive matrix factorization; RMSEP, root mean squared error of prediction. 877

The impact of copper should be examined in further investigations. The Pichoy River, with high concentrations of iron in the filtrate, should be examined as well. Studies about the chemical equilibrium of iron and manganese oxides in relation to the alkaline and earth alkaline elements should take place.

The surroundings of the former Kremikovtzi steel mill near Sofia (Bulgaria) are influenced by various emissions from the factory. In addition to steel and alloys, they produce different products based on inorganic compounds in different smelters. Soil in this region is multiply contaminated. We collected 65 soil samples and analyzed 15 elements by different methods of atomic spectroscopy for a survey of this field site. Here we present a novel hybrid approach for environmental risk assessment of polluted soil combining geostatistical methods and source apportionment modeling. We could distinguish areas with heavily and slightly polluted soils in the vicinity of the iron smelter by applying unsupervised pattern recognition methods. This result was supported by geostatistical methods such as semivariogram analysis and kriging. The modes of action of the metals examined differ significantly in such a way that iron and lead account for the main pollutants of the iron smelter, whereas, e.g., arsenic shows a haphazard distribution. The application of factor analysis and source-apportionment modeling on absolute principal component scores revealed novel information about the composition of the emissions from the different stacks. It is possible to estimate the impact of every element examined on the pollution due to their emission source. This investigation allows an objective assessment of the different spatial distributions of the elements examined in the soil of the Kremikovtzi region. The geostatistical analysis illustrates this distribution and is supported by multivariate statistical analysis revealing relations between the elements.