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Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

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Published on: March 12, 2010

Compositional analysis of lignocellulosic feedstocks. 2. Method uncertainties.

David W Templeton¹, Christopher J Scarlata, Justin B Sluiter

¹National Bioenergy Center, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, Colorado 80401-3305, USA. David.Templeton@nrel.gov

Journal of Agricultural and Food Chemistry

|July 31, 2010

Summary

This study quantifies the uncertainty in lignocellulosic feedstock analysis using sulfuric acid hydrolysis. Results show reliable component measurements (1-3% RSD) are achievable, crucial for biomass conversion technology.

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High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release

Published on: September 15, 2015

Area of Science:

Biomass analytical chemistry
Biomass conversion technologies
Chemical analysis of lignocellulose

Background:

Standardized methods for lignocellulosic feedstock characterization are essential for biomass conversion.
Sulfuric acid hydrolysis is a common technique for biomass fractionation and analysis.
Understanding data uncertainty is critical for technical and financial assessments in the bioenergy sector.

Purpose of the Study:

To evaluate the uncertainty in chemical component measurements of lignocellulosic feedstocks.
To report the standard deviation for key biomass components using established laboratory analytical procedures (LAPs).
To assess the impact of analytical method variations and equipment issues on data reliability.

Main Methods:

Two-stage sulfuric acid hydrolysis for biomass fractionation.
Gravimetric and instrumental analyses of homogenized corn stover and NIST sugarcane bagasse.
Statistical analysis of replicate measurements (154 samples, 13 batches, 7 analysts, 2 labs) to determine uncertainty.
Reporting of both censored and uncensored data sets to account for equipment issues.

Main Results:

Relative standard deviations (RSD) of 1-3% were reported for major components like glucan, xylan, lignin, and extractives.
Minor components showed higher RSD of 4-10%.
Similar standard deviations were observed for corn stover and NIST sugarcane bagasse, indicating method-driven uncertainty.

Conclusions:

The analytical method, rather than feedstock type, is the primary driver of uncertainty in lignocellulosic component analysis.
Reliable quantification of major biomass components is achievable with current LAPs.
Censored data provide a 'best case' scenario, while uncensored data highlight the impact of method variability.