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As interest in lignocellulosic biomass feedstocks for conversion into transportation fuels grows, the summative compositional analysis of biomass, or plant-derived material, becomes ever more important. The sulfuric acid hydrolysis of biomass has been used to measure lignin and structural carbohydrate content for more than 100 years. Researchers have(More)
In the context of algal biofuels, lipids, or better aliphatic chains of the fatty acids, are perhaps the most important constituents of algal biomass. Accurate quantification of lipids and their respective fuel yield is crucial for comparison of algal strains and growth conditions and for process monitoring. As an alternative to traditional solvent-based(More)
The most common procedures for characterizing the chemical components of lignocellulosic feedstocks use a two-stage sulfuric acid hydrolysis to fractionate biomass for gravimetric and instrumental analyses. The uncertainty (i.e., dispersion of values from repeated measurement) in the primary data is of general interest to those with technical or financial(More)
New, rapid, and inexpensive methods that monitor the chemical composition of corn stover and corn stover-derived samples are a key element to enabling the commercialization of processes that convert stover to fuels and chemicals. These new techniques combine near infrared (NIR) spectroscopy and projection to latent structures (PLS) multivariate analysis to(More)
Biomass compositional methods are used to compare different lignocellulosic feedstocks, to measure component balances around unit operations and to determine process yields and therefore the economic viability of biomass-to-biofuel processes. Four biomass reference materials (RMs NIST 8491–8494) were prepared and characterized, via an interlaboratory(More)
BACKGROUND In an effort to find economical, carbon-neutral transportation fuels, biomass feedstock compositional analysis methods are used to monitor, compare, and improve biofuel conversion processes. These methods are empirical, and the analytical variability seen in the feedstock compositional data propagates into variability in the conversion yields,(More)
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