Rapid Detection of Ash and Inorganics in Bioenergy Feedstocks Using Fourier Transform Infrared Spectroscopy Coupled with Partial Least-Squares Regression

Abstract

The rapid determination of ash and inorganic elements in biomass is critical for feedstock screening for thermochemical conversion processes. In this study, 225 lignocellulosic biomass samples composed of switchgrass and hybrid poplar (wood and bark fractions and wood/bark blends) were used to construct feedstock agnostic predictive models using Fourier transform infrared (FTIR) spectroscopy coupled with partial least-squares regression for ash content and the concentration of inorganic elements. The models for ash, potassium, calcium, magnesium, sulfur, and silicon performed well with validation correlation coefficient (rval) values of 0.94–0.98 and residual predictive deviation (RPD) values of 2.75–5.18. The phosphorus model was not as robust, with a RPD of 2.50 and a rval of 0.91; however, the model may be suitable for screening purposes. This work shows that FTIR combined with a multivariate regression technique is a viable tool for the rapid determination of ash and inorganics in multiple feedstocks.