Biofuels & Bioenergy

It is important to know the lignocellulosic composition of a feedstock in order to ascertain its potential value for biorefining. The standard laboratory methods of analysis are costly and time consuming. Celignis personnel have worked on the development of rapid, low cost methods of analysis using near infrared spectroscopy (NIR). Over 1000 biomass samples have been collected and processed for conventional analysis with the NIR spectra of each sample collected at several stages of sample preparation. The dried samples were then analysed via reference methods for a number of lignocellulosic constituents, ash, extractives, and elemental composition. Following this NIR models were developed for a large number of constituents (including glucan, arabinan, galactan, xylan, mannan, rhamnan, total sugars, Klason lignin, acid soluble lignin, extractives, ash, and nitrogen) using a calibration set and the predictive abilities of the models were tested on an independent set. Separate models were developed on specific sample groups (Miscanthus, pre-treated biomass, peat, straws, waste paper/cardboard, sugarcane bagasse and others). In addition a global model was developed incorporating all those samples as well as many other sample types including: trees, energy crops, agricultural residues, animal excreta, biorefinery residues, grasses, municipal wastes, composts etc. The models developed were highly accurate and robust for important lignocellulosic constituents. For example, the root mean square errors of prediction (RMSEP) [and R2 in prediction] for the global dataset were 1.84% [0.976], 0.75% [0.989], and 1.73% [0.983] for glucan, xylan, and Klason lignin, respectively. This work is significant since it is the first demonstration of the utility of NIR in the commercial analysis of such a wide variety of biomass samples for all these lignocellulosic constituents.