Amie D. Sluiter

Compositional Analysis of Lignocellulosic Feedstocks. 1. Review and Description of Methods

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 applied these methods to measure the lignin and structural carbohydrate contents of woody materials, estimate the nutritional value of animal feed, analyze the dietary fiber content of human food, compare potential biofuels feedstocks, and measure the efficiency of biomass-to-biofuels processes. The purpose of this paper is to review the history and lineage of biomass compositional analysis methods based on a sulfuric acid hydrolysis. These methods have become the de facto procedure for biomass compositional analysis. The paper traces changes to the biomass compositional analysis methods through time to the biomass methods currently used at the National Renewable Energy Laboratory (NREL). The current suite of laboratory analytical procedures (LAPs) offered by NREL is described, including an overview of the procedures and methodologies and some common pitfalls. Suggestions are made for continuing improvement to the suite of analyses.

Rapid biomass analysis: new tools for compositional analysis of corn stover feedstocks and process intermediates from ethanol production.

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 allow the compositional analysis of hundreds of samples in 1 d at a cost of about

Rapid Biomass Analysis

10 each. The new NIR/PLS rapid analysis methods can also be used to support a variety of research projects that would have been too costly to pursue by traditional methods.

Near infrared calibration models for pre-treated corn stover slurry solids, isolated and in situ

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 allow the compositional analysis of hundreds of samples in 1 d at a cost of about

Assessing corn stover composition and sources of variability via NIRS

10 each. The new NIR/PLS rapid analysis methods can also be used to support a variety of research projects that would have been too costly to pursue by traditional methods.

Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover

Biomass pretreatment processes often yield slurry, a two-phase material consisting of an aqueous phase with solubilised components and a solid phase with insoluble constituents. Chemical characterisation of this material using conventional wet chemical analysis requires that the two phases be analysed separately. We have previously demonstrated near infrared (NIR) models that successfully predict the chemical composition of the solid phase after separation, washing and drying. In this work, we present the current version of this calibration model, as well as a model that uses spectra of the whole slurry samples (without separation) to predict the solids composition in situ. Removing the slurry solid/liquid separation step saves large amounts of time and effort during analysis. The model using washed and dried solids provided predicted vs measured correlation coefficient (R2) values of 0.97, 0.99 and 0.98 and root mean square error of calibration (RMSEC) values of 1.5, 0.8 and 0.8 dry weight percent for glucan, xylan and lignin, respectively. These RMSEC values are similar to established wet chemical analysis uncertainties. Validation samples also showed similar uncertainties and an average r2 value of 0.98 for the major constituents. The whole slurry model provided R2 values of 0.93, 0.93 and 0.95 and RMSEC values of 2.3,1.7 and 1.0 dry weight percent for glucan, xylan and lignin, respectively. The RMSEC values are larger than established wet chemical analysis uncertainties. Validation samples showed uncertainties and r2 values that were not statistically significantly different (p = 0.05) from calibration model values for glucan, xylan, and lignin. The slurry model was not equivalent to the washed and dried pretreated solids model, with relative increases in RMSEC values of 20%–50% for major constituents. However, the model was highly successful for the intended purpose, which was to predict the composition of samples without the significant added effort of separating, washing and drying solids prior to scanning.