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

Abstract

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.