Variability of biomass chemical composition and rapid analysis using FT-NIR techniques
- University of Tennessee, Knoxville (UTK)
- University of Tennessee
- ORNL
A quick method for analyzing the chemical composition of renewable energy biomass feedstock was developed by using Fourier transform near-infrared (FT-NIR) spectroscopy coupled with multivariate analysis. The study presents the broad-based model hypothesis that a single FT-NIR predictive model can be developed to analyze multiple types of biomass feedstock. The two most important biomass feedstocks corn stover and switchgrass were evaluated for the variability in their concentrations of the following components: glucan, xylan, galactan, arabinan, mannan, lignin, and ash. A hypothesis test was developed based upon these two species. Both cross-validation and independent validation results showed that the broad-based model developed is promising for future chemical prediction of both biomass species; in addition, the results also showed the method's prediction potential for wheat straw.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 988247
- Journal Information:
- Carbohydrate Polymers, Vol. 81, Issue 2010; ISSN 0144-8617
- Country of Publication:
- United States
- Language:
- English
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