June 16-21, 2019
With the growth of biomass processing in biorefineries, there is an increasing need to develop rapid and simple methods for biomass characterization. One important component of biomass that is of signification interest in pyrolysis and liquefaction research is lignin, which is the world’s predominant source of renewable aromatic carbon. Due to its complex, heterogeneous nature and distinct variations among different biomass feedstocks, the characterization of lignins presents a unique challenge. In this study, we will show that clear divisions and comparisons can be made among a variety of lignins based on their FTIR spectra quantitatively assessed through principal component analysis (PCA). The eight lignins so far characterized are: two varieties of softwood kraft, one hardwood kraft, corn stover milled wood lignin (MWL), Douglas fir MWL, hybrid poplar MWL, wheat straw soda, and wheat straw organosolv. These samples were analyzed using a Bruker Alpha FTIR-ATR instrument (in addition to other characterization techniques). The spectra were baseline corrected and normalized, and the intensities of 15 peaks were recorded. The 15 spectral peaks were chosen based on previous work published by Li and McDonald (Industrial Crops and Products, 62, 2014, 67-76). PCA and data visualization was done using Python in the Jupyter Notebook environment.
Fig. 1: Explained Variance of Each PC
Principal component analysis revealed that among the eight lignins, they can be qualitatively grouped based on both their feedstock variety (e.g., hardwood, softwood) and isolation method (i.e., kraft, MWL, soda, organosolv). Quantification of the explained variances for each principal component (PC) suggests that three PC’s are necessary to capture over 90% of the variation among the samples (Figure 1). The principal component plots (Figure 2) show that softwood kraft lignins are clustered, corn stover and Douglas fir MWLs are clustered, and hardwood kraft and wheat straw soda are clustered. The two wheat straw and two hardwood samples tend to have positive values for PC1, while the softwoods and corn stover are negative. The MWLs are closely grouped along PC2, and the organosolv wheat straw is uniquely large along PC3. Depending on which of the three PC plots are assessed, other possible groupings can be reasonably drawn, suggesting that the FTIR spectral characteristics of lignins are distinctly affected by both their original biomass feedstock and isolation or extraction method. The analysis of FTIR spectra with PCA is a simple and efficient way to quickly assess the characteristics of an unknown or poorly-understood lignin sample, based on its comparison with other well-studied lignins. Previous work has shown that PCA is a robust technique for analyzing bio-oil MS data (Jia, et al., Energy & Fuels, 29, 2015, 7364-7374; Pattiya, et al., Fuel, 89(1), 2010, 244-253), and this work shows that such analysis can easily be extended to FTIR spectra of contrasting lignins. Continuing research will further assess the FTIR data for other important characteristics (both qualitative and quantitative), as well as to include additional technical lignins in the analyses.
Please click Additional Files below to see the full abstract.
Manuel Garcia-Perez, Erika Bartolomei, Yann Le Brech, Anthony Dufour, and Evan Terrell, "Comparison among technical and milled wood lignins through principal component analysis of FTIR spectra" in "Pyroliq 2019: Pyrolysis and Liquefaction of Biomass and Wastes", Franco Berruti, ICFAR, Western University, Canada Anthony Dufour, CNRS Nancy, France Wolter Prins, University of Ghent, Belgium Manuel Garcia-Pérez, Washington State University, USA Eds, ECI Symposium Series, (2019). https://dc.engconfintl.org/pyroliq_2019/36