Prediction of Resin and Fatty Acid Content of Biorefinery Feedstock by On-line Near-Infrared (NIR) Spectroscopy

Abstract

Extractives in biorefinery feedstock are a source of precursor chemicals and biofuel products. Resin and fatty acids (RFAs) in such extractives constitute an interesting fraction, which may contain both chemically attractive precursors and also problematic volatile organic compounds. On-line near-infrared (NIR) spectra were collected from a process stream, designed experimentally and involving softwood lignocelluloses; the data were regressed using partial least-squares to give RFA concentrations that varied between 0.1 and 0.5% (dry weight basis). At-line NIR models were also constructed using spectral data from pelletized samples from the process stream. In addition, off-line NIR modeling was conducted using softwoods with a wider RFA variation range (0.1-1.0% dry weight basis). All of the calibration models obtained exhibited good predictive abilities. The laboratory-based off-line NIR model explained 94.5% of the variation in concentrations and had a prediction error of 0.070% for the RFA content. The coefficient of variation (CV), representing the percentage of the ratio between the prediction error and the average concentration, was 17.8%. The on-line and at-line models explained 71.3-79.6% of the variation in the RFA concentrations and had prediction errors within the range of 0.026-0.041% and CVs of 13.7-18.1%. This was excellent in comparison to the ca. 10% error in accuracy when determining the RFA reference values. The results illustrate that on-line NIR spectroscopy provides a valid method for real-time predictions of RFA concentrations in biomaterials. This should facilitate better monitoring and process control as well as targeted pretreatments to obtain tailor-made biorefinery feedstock, thus adding value to the production process.

The state of the art in woody biomass comminution and sorting in Northern Europe

“…The moisture, lignin, carbohydrate monomer, and wood contents of bark have been measured on a moving conveyor belt using near-infrared (NIR) spectroscopy (Axrup et al 2000). NIR spectroscopy can also be used to distinguish heartwood from sapwood (Sandberg & Sterley 2009, Eriksson et al 2012, to measure the wood's density and modulus of elasticity (Xu et al 2011), and to determine the resin and fatty acid contents of softwood pellets (Lestander & Samuelsson 2010). In the mining industry, NIR has been used to detect crystalline minerals (Goetz et al 2009).…”

Modeling of Cosolvent and Temperature Effects on Ethanolic Extraction of Pequi and Murici Seeds

“…Variation coefficients (CV) were found to be 3.1 and 2.2% for pequi and murici seeds, respectively. Low CVs (< 10%) clearly indicate a high degree of predictability regarding experimental responses and a good deal of reliability regarding the experimental data (Lestander and Samuelsson, 2010). In the case of a quadratic model, other parameters should be evaluated other than the R 2 and CV in order to study the significancy of the model, such as the F-value (F Regression/Residue and F Lack of fit/Pure error ), which indicates the significance of each controlled factor on each tested model , the residual standard error, the independency and the normality of the residues, and also the analytical discernment of the scientist should be taken into account.…”

An Institutional Approach to Solar Fuels Research

“…The research of this consortium covers improvements of forest feedstock to production of advanced fuels and chemicals in an environmentally friendly way. [24,35,[46][47][48][49][50][51][52][53][54][55] Bio4Energy is led by Stellan Marklund and is funded with 20 million euros by the Swedish government for the initial 5-year period 2010-14. Further constellations at Umeå University that deal with forest feedstock optimization include Formas-financed Strong Research Environments BioImprove (2.5 million euros) and FuncFiber (2.5 million euros), [56][57][58][59][60][61][62][63] the Wallenberg-financed Conifer Genome Consortium (7.5 million euros), and the UPSC Berzelii Centre for Forest Biotechnology (10 million euros) [47,64,65] financed by the Swedish Research councils VR and Vinnova.…”