Determination of terpenoid content in pine by organic solvent extraction and fast-GC analysis

Terpenoids, naturally occurring compounds derived from isoprene units present in pine oleoresin, are a valuable source of chemicals used in solvents, fragrances, flavors and have shown potential use as a biofuel. This paper describes a method to extract and analyze the terpenoids present in loblolly pine saplings and pine lighter wood. Various extraction solvents were tested over different times and temperatures. Samples were analyzed by pyrolysis-molecular beam mass spectrometry before and after extractions to monitor the extraction efficiency. The pyrolysis studies indicated that the optimal extraction method used a 1:1 hexane/acetone solvent system at 22°C for 1 h. Extracts from the hexane/acetone experiments were analyzed using a low thermal mass modular accelerated column heater for fast-GC/FID analysis. The most abundant terpenoids from the pine samples were quantified, using standard curves, and included the monoterpenes, α- and β- pinene, camphene and δ-carene. Sesquiterpenes analyzed included caryophyllene, humulene and α-bisabolene. Diterpenoid resin acids were quantified in derivatized extractions, including pimaric, isopimaric, levopimaric, palustric, dehydroabietic, abietic and neoabietic acids

Abundance of Major Cell Wall Components in Natural Variants and Pedigrees of Populus trichocarpa

The rapid analysis of biopolymers including lignin and sugars in lignocellulosic biomass cell walls is essential for the analysis of the large sample populations needed for identifying heritable genetic variation in biomass feedstocks for biofuels and bioproducts. In this study, we reported the analysis of cell wall lignin content, syringyl/guaiacyl (S/G) ratio, as well as glucose and xylose content by high-throughput pyrolysis-molecular beam mass spectrometry (py-MBMS) for >3,600 samples derived from hundreds of accessions of Populus trichocarpa from natural populations, as well as pedigrees constructed from 14 parents (7 × 7). Partial Least Squares (PLS) regression models were built from the samples of known sugar composition previously determined by hydrolysis followed by nuclear magnetic resonance (NMR) analysis. Key spectral features positively correlated with glucose content consisted of m/z 126, 98, and 69, among others, deriving from pyrolyzates such as hydroxymethylfurfural, maltol, and other sugar-derived species. Xylose content positively correlated primarily with many lignin-derived ions and to a lesser degree with m/z 114, deriving from a lactone produced from xylose pyrolysis. Models were capable of predicting glucose and xylose contents with an average error of less than 4%, and accuracy was significantly improved over previously used methods. The differences in the models constructed from the two sample sets varied in training sample number, but the genetic and compositional uniformity of the pedigree set could be a potential driver in the slightly better performance of that model in comparison with the natural variants. Broad-sense heritability of glucose and xylose composition using these data was 0.32 and 0.34, respectively. In summary, we have demonstrated the use of a single high-throughput method to predict sugar and lignin composition in thousands of poplar samples to estimate the heritability and phenotypic plasticity of traits necessary to develop optimized feedstocks for bioenergy applications