Variability of biomass chemical composition and rapid analysis using FT-NIR techniques

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 b...

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Bibliographic Details
Published in:Carbohydrate polymers 2010-07, Vol.81 (4), p.820-829
Main Authors: Liu, Lu, Ye, X. Philip, Womac, Alvin R., Sokhansanj, Shahab
Format: Article
Language:English
Subjects:
09 BIOMASS FUELS
AGRICULTURAL WASTES
Agronomy. Soil science and plant productions
Applied sciences
Biological and medical sciences
BIOMASS
Broad-based model
CHEMICAL COMPOSITION
Corn stover
Exact sciences and technology
FORECASTING
FT-NIR
Fundamental and applied biological sciences. Psychology
General agronomy. Plant production
HYPOTHESIS
LIGNIN
Lignin and derivatives
MAIZE
MULTIVARIATE ANALYSIS
Natural polymers
Physicochemistry of polymers
SPECTROSCOPY
Starch and polysaccharides
STRAW
Switchgrass
Triticum aestivum
Use of agricultural and forest wastes. Biomass use, bioconversion
VALIDATION
WHEAT
Wheat straw
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Summary: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.
ISSN:0144-8617
1879-1344
DOI:10.1016/j.carbpol.2010.03.058