Different multivariate statistical analyses were applied to phospholipid fatty acids representing the biomass composition and to different biogeochemical parameters measured in 37 samples from a landfill contaminated aquifer at Grindsted Landfill (Denmark). Principal component analysis and correspondence analysis were used to identify groups of samples showing similar patterns with respect to biogeochemical variables and phospholipid fatty acid composition. The principal component analysis revealed that for the biogeochemical parameters the first principal component was linked to the pollution effect and to redox processes and the second principal component described the geological and geochemical features of the samples. Dependent on the data transformation of the phospholipid fatty acid profiles in either absolute concentrations (logarithm transformed) or in mol% of total phospholipid fatty acids, different groups of samples and outliers were revealed by the principal component analysis. The principal component analysis on data in absolute concentrations revealed that many phospholipid fatty acids reflected the pollution effect on the biomass composition. In contrast, the phospholipid fatty acids in mol% divided the samples into one group of the more polluted samples and another with the nearly unpolluted samples. The important phospholipid fatty acids for this grouping were mainly a few of the normal saturated phospholipid fatty acids (10:0, 16:0 and 18:0). Discriminant analysis was used to allocate samples of phospholipid fatty acids into predefined classes. A large percentages of samples were classified correctly when discriminating samples into groups of dissolved organic carbon and specific conductivity, indicating that the biomass is highly influenced by the pollution. In contrast, the discriminant analysis revealed that on the basis of the profiles of phospholipid fatty acids no good discrimination between samples showing dominant sulfate reduction and dominant iron reduction could be made, nor between samples showing dominant nitrate reduction and aerobic respiration. Partial least square analysis related the phospholipid fatty acids data to the biogeochemical parameters assuming linear relationships. After selection of the optimal phospholipid fatty acid combination by genetic algorithms, good partial least squares models with low prediction errors were gained primarily between the biogeochemical parameters describing total contents of carbon, pH and chloride. The models predicting specific activity in terms of, e.g., sulfate reduction activity in a sample had relatively higher prediction errors and low correlation coefficients. This indicates that the phospholipid fatty acid profiles from complex habitats have limited value for identifying more specific microbial populations.
|Journal||F E M S Microbiology Reviews|
|Publication status||Published - 1997|