Culturable bacterial populations associated with ectomycorrhizae of Norway spruce stands with different degrees of decline in the Czech Republic.

The aim of this study was to determine which species of culturable bacteria are associated with ectomycorrhizae (ECM) of Norway spruce (Picea abies (L.) Karst) in the Sudety Mountains, exposed for years to atmospheric pollutants, acid rain, and climatic stress, and to identify particular species that have adapted to those conditions. Biolog identification was performed on bacterial species from ECM of adult spruce trees and seedlings of stands with low, intermediate, and high forest decline. Bacterial diversity in ECM associated with adult spruce trees, seedlings, and seedlings grown on monoliths was calculated; although the expected values appeared to vary widely, no significant differences among sites were observed. Dendrograms based on the identified bacterial species showed that stands with low forest decline clustered separately from the others. Principal component analysis of the normalized data for ECM-associated species showed a clear separation between stands with high forest decline and stands with low forest decline for seedlings and a less evident separation for adult spruce trees. In conclusion, shifts in ECM-associated culturable bacterial populations seem to be associated with forest decline in Norway spruce stands. Some bacterial species were preferentially associated with mycorrhizal roots depending on the degree of forest decline; this was more evident in seedlings where the species Burkholderia cepacia and Pseudomonas fluorescens were associated with, respectively, ECM of the most damaged stands and those with low forest decline.

Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.

INTRODUCTION: Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. OBJECTIVE: To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. METHODOLOGY: Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. RESULTS: Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. CONCLUSIONS: SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.