JOURNAL OF NEAR INFRARED SPECTROSCOPY 93 ISSN: 0967-0335 © IM Publications LLP 2015 doi: 10.1255/jnirs.1153 All rights reserved Non-destructive prediction of the properties of forest biomass for chemical and bioenergy applications using near infrared spectroscopy Gifty E. Acquah, a, * Brian K. Via, a Oladiran O. Fasina b and Lori G. Eckhardt c a Forest Products Development Center, School of Forestry and Wildlife Sciences, Auburn University, 520 Devall Drive, Auburn, AL 36849, USA. E-mail: gea0002@auburn.edu b Center for Bioenergy and Bioproducts, Department of Biosystems Engineering, Auburn University, 350 Mell Street, Auburn, AL 36849, USA c Forest Health Dynamics Laboratory, School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL 36849, USA 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. Keywords: heterogeneous forest biomass, chemical composition, proximate analysis, energy value, near infrared spectroscopy, partial least- squares regression Introduction The use of biomass as an alternative source of energy, fuels and chemicals derived from fossil fuel will reduce our depend- ence on non-renewable resources and also minimise net greenhouse gas emissions. Biomass is mostly sourced from forestry and agricultural sources in the USA and other coun- tries. It is estimated that some 93 million dry tons of forest biomass is available per year, out of which 73% is logging resi- dues comprising tops, branches and limbs, salvageable dead trees and small trees. 1 Even though most of this resource is currently left on site, a significant portion of it will become economically feasible for removal as new markets for bioen- ergy and bioproducts emerge. 2 In addition to the conventional combustion of forest biomass for heat and power, it can also be thermochemically converted into syngas or bio oils that G.E. Acquah et al., J. Near Infrared Spectrosc. 23, 93–102 (2015) Received: 13 November 2014 n Revised: 11 March 2015 n Accepted: 5 May 2015 n Publication: 14 May 2015