Lindsey M. Kline

Two-year field analysis of reduced recalcitrance transgenic switchgrass.

Switchgrass (Panicum virgatum L.) is a leading candidate for a dedicated lignocellulosic biofuel feedstock owing to its high biomass production, wide adaptation and low agronomic input requirements. Lignin in cell walls of switchgrass, and other lignocellulosic feedstocks, severely limits the accessibility of cell wall carbohydrates to enzymatic breakdown into fermentable sugars and subsequently biofuels. Low-lignin transgenic switchgrass plants produced by the down-regulation of caffeic acid O-methyltransferase (COMT), a lignin biosynthetic enzyme, were analysed in the field for two growing seasons. COMT transcript abundance, lignin content and the syringyl/guaiacyl lignin monomer ratio were consistently lower in the COMT-down-regulated plants throughout the duration of the field trial. In general, analyses with fully established plants harvested during the second growing season produced results that were similar to those observed in previous greenhouse studies with these plants. Sugar release was improved by up to 34% and ethanol yield by up to 28% in the transgenic lines relative to controls. Additionally, these results were obtained using senesced plant material harvested at the end of the growing season, compared with the young, green tissue that was used in the greenhouse experiments. Another important finding was that transgenic plants were not more susceptible to rust (Puccinia emaculata). The results of this study suggest that lignin down-regulation in switchgrass can confer real-world improvements in biofuel yield without negative consequences to biomass yield or disease susceptibility.

Rapid Assessment of Lignin Content and Structure in Switchgrass (Panicum virgatum L.) Grown Under Different Environmental Conditions

Switchgrass (Panicum virgatum L.) is a candidate feedstock in bioenergy, and plant breeding and molecular genetic strategies are being used to improve germplasm. In order to assess these subsequent modifications, baseline biomass compositional data are needed in a relevant variety of environments. In this study, switchgrass cv. Alamo was grown in the field, greenhouse, and growth chamber and harvested into individual leaf and stem tissue components. These components were analyzed with pyrolysis vapor analysis using molecular beam mass spectrometry, Fourier transform infrared, and standard wet chemistry methods to characterize and compare the composition among the different growth environments. The details of lignin content, S/G ratios, and degree of cross-linked lignin are discussed. Multivariate approaches such as projection to latent structures regression found a very strong correlation between the lignin content obtained by standard wet chemistry methods and the two high throughput techniques employed to rapidly assess lignin in potential switchgrass candidates. The models were tested on unknown samples and verified by wet chemistry. The similar lignin content found by the two methods shows that both approaches are capable of determining lignin content in biomass in a matter of minutes.

Activation of lignocellulosic biomass by ionic liquid for biorefinery fractionation.

Fractionation of lignocellulosic biomass is an attractive solution to develop an economically viable biorefinery by providing a saccharide fraction to produce fuels and a lignin stream that can be converted into high value products such as carbon fibers. In this study, the analysis of ionic liquid-activated biomass demonstrates that in addition of decreasing crystallinity, the selected ILs (1-butyl-3-methylimidazolium acetate, 1-butyl-3-methylimidazolium chloride and 1-ethyl-3-methylimidazolium acetate) deacetylate Yellow poplar under mild conditions (dissolution at 60-80 °C), and lower the degradation temperature of each biomass polymeric component, thereby reducing the recalcitrance of biomass. Among the three tested ILs, 1-ethyl-3-methylimidazolium acetate performed the best, providing a strong linear relationship between the level of deacetylation and the rate of enzymatic saccharification for Yellow poplar.

Fast biomass compositional analysis using Fourier Transform Near-infrared Technique

The objectives of this research were to determine the variation of chemical composition across botanical fractions of cornstover, and to use Fourier Transform Near-infrared (FT-NIR) techniques to qualitatively classify separated cornstover fractions, and develop calibration model for the quantitative analysis of chemical compositions of cornstover. Large variations of biomass chemical composition for wide calibration ranges were achieved by manually separating the cornstover samples into six botanical fractions, and their chemical compositions were determined by conventional wet chemical analyses, which proved that chemical composition varies significantly among different botanical fractions of cornstover. Husk, followed by rind and pith, has the highest sugar (glucan+xylan) content; node has the lowest sugar content. Based on FT-NIR spectra acquired on the biomass, classification by Soft Independent Modeling of Class Analogy (SIMCA) was employed to conduct qualitative classification of cornstover and Partial Least Square (PLS) regression was used for quantitative chemical composition analysis. SIMCA was demonstrated successfully in classifying botanical fractions of cornstover. The developed PLS models yielded root mean square error of prediction (RMSEP) of 1.058, 1.539, 0.987, and 1.435 for glucan, xylan, lignin, and ash, respectively. The FT-NIR techniques in combination with multivariate analysis are very useful to biomass feedstock suppliers, bio-ethanol manufacturers, and bio-power producers.