Non-destructive prediction of the properties of forest biomass for chemical and bioenergy applications using near infrared spectroscopy

Forest biomass will play a key role as a feedstock for bioproducts as the bioeconomy develops. Rapid assessment of this heterogeneous resource will help determine its suitability as feedstock for specific applications, aid in feedstock improvement programmes and enable better process control that will optimise the biorefinery process. In this study, near infrared spectroscopy coupled with partial least-squares regression was used to predict important chemical and thermal reactivity properties of biomass made up of needles, twigs, branches, bark and wood of Pinus taeda (loblolly pine). Models developed with the raw spectra for property prediction used between three and eight factors to yield R-2 values ranging from a low of 0.34 for higher heat values to a high of 0.92 for volatile matter. Pretreating the raw spectra with first derivatives improved the fit statistics for all properties (i.e. min 0.57, max 0.92; with two or three factors). The best-performing models were for extractives, lignin, glucose, cellulose, volatile matter and fixed carbon (R-2 >= 0.80, residual predictive deviation/ratio of performance to deviation >= 1.5). This study provided the capacity to predict multiple chemical and thermal/energy traits from a single spectrum across an array of materials that differ considerably in chemistry type and distribution. Models developed should be able to rapidly predict the studied properties of similar biomass types. This will be useful in rapidly allocating feedstocks that optimise biomass conversion technologies.