| Metabolomics is built upon a potent suite of technologies that aims to use the dynamics of the total metabolite composition of an organism to reflect influences of genetics and environment on cellular function.; Metabolomics, in conjunction with transcriptomics and proteomics, offers the potential to identify roles of genes with unknown functions. The full range of secondary metabolites may comprise thousands of compounds, with a richness only partially understood. Modern analytical tools only allow for subsets of the metabolome to be analyzed. The development of analytical strategies is necessary to monitor dynamic levels of secondary metabolites. More selective, sensitive, and robust analytical strategies will increase the ability to monitor numerous metabolites from a single injection.; This thesis presents an analytical strategy detailing the recognition, identification, and quantification of plant secondary metabolites, specifically polyphenols. Polyphenol subclass recognition was achieved using LC/ESI-/MS/MS. Fragmentation pathways were used to establish substructure screening functions for analysis of both cotton and pin oak leaf extracts. This allowed for recognition of 20 polyphenols from five different subclasses.; The method was further expanded by invoking in-source fragmentation using LC/ESI-/MS with alternating low/high cone potential scans. Low energy scans provided molecular mass information, while high energy scans provided structural information. This allowed for identification of secondary metabolites in an unbiased, nonspecific manner compared to LC/MS/MS. The resulting data set was inherently more complex due to extensive fragmentation. Data processing software developed for GC/MS data was adapted for analysis of LC/MS data. This software enabled recognition of more than 200 metabolites from within a 30 minute analysis. Recognized metabolites were then sorted into groups of polyphenols and non-polyphenols by a newly developed algorithm based upon percent carbon by composition. Once target compounds were sorted, LC/MS and LC/MS/MS were used for identification. More than 60 metabolites were identified from the cotton extract, tripling previous measurements.; Finally, samples were quantified using multiple LC/MS techniques for comparison. Detection limits were as low as 340 pg/muL with LC/SIM, and levels of more than 30 compounds were measured per each 10 muL injection. Analytical precision was compared between full scan LC/MS and SIM methods. |
