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

• Published in: Carbohydrate polymers Vol. 81; no. 4; pp. 820 - 829
• Main Authors: Liu, Lu, Ye, X. Philip, Womac, Alvin R., Sokhansanj, Shahab
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 23.07.2010; Elsevier
• 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; FT-NIR; Corn stover; Switchgrass; Wheat straw; Broad-based model; Chemical composition; Biomass; Monocotyledones; Fourier transformation; Coupled method; Variability; Perennial plant; Multivariate analysis; Experimental study; Near infrared spectrometry; Thatch; Gramineae; Angiospermae; Panicum; Spermatophyta; Corn by product; Agricultural waste; Measurement method
• ISSN: 0144-8617; 1879-1344
• DOI: 10.1016/j.carbpol.2010.03.058

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.