Determination of community structure through deconvolution of PLFA-FAME signature of mixed population

Phospholipid fatty acids (PLFAs) as biomarkers are well established in the literature. A general method based on least square approximation (LSA) was developed for the estimation of community structure from the PLFA signature of a mixed population where biomarker PLFA signatures of the component species were known. Fatty acid methyl ester (FAME) standards were used as species analogs and mixture of the standards as representative of the mixed population. The PLFA/FAME signatures were analyzed by gas chromatographic separation, followed by detection in flame ionization detector (GC-FID). The PLFAs in the signature were quantified as relative weight percent of the total PLFA. The PLFA signatures were analyzed by the models to predict community structure of the mixture. The LSA model results were compared with the existing "functional group" approach. Both successfully predicted community structure of mixed population containing completely unrelated species with uncommon PLFAs. For slightest intersection in PLFA signatures of component species, the LSA model produced better results. This was mainly due to inability of the "functional group" approach to distinguish the relative amounts of the common PLFA coming from more than one species. The performance of the LSA model was influenced by errors in the chromatographic analyses. Suppression (or enhancement) of a component's PLFA signature in chromatographic analysis of the mixture, led to underestimation (or overestimation) of the component's proportion in the mixture by the model. In mixtures of closely related species with common PLFAs, the errors in the common components were adjusted across the species by the model.