Predicting Molecular Weight Characteristics of Reductively Depolymerized Lignins by ATR-FTIR and Chemometrics

Abstract

Recent scientific advances in the valorization of lignin, through e.g., (partial-)catalytic depolymerization, require equally state-of-the-art approaches for the analysis of the obtained depolymerized lignins (DLs) or lignin bio-oils. The use of chemometrics in combination with infrared (IR) spectroscopy is one avenue to provide rapid access to pertinent lignin parameters, such as molecular weight (MW) characteristics, which typically require analysis via time-consuming size-exclusion methods, or diffusion-ordered NMR spectroscopy. Importantly, MW serves as a marker for the degree of depolymerization (or recondensation) that the lignin has undergone, and thus probing this parameter is essential for the optimization of depolymerization conditions to achieve DLs with desired properties. Here, we show that our ATR-IR-based chemometrics approach used previously for technical lignin analysis can be extended to analyze these more processed, lignin-derived samples as well. Remarkably, also at this lower end of the MW scale, the use of partial least-squares (PLS) regression models well-predicted the MW parameters for a sample set of 57 depolymerized lignins, with relative errors of 9.9-11.2%. Furthermore, principal component analysis (PCA) showed good correspondence with features in the regression vectors for each of the biomass classes (hardwood, herbaceous/grass, and softwood) obtained from PLS-discriminant analysis (PLS-DA). Overall, we show that the IR spectra of DLs are still amenable to chemometric analysis and specifically to rapid, predictive characterization of their MW, circumventing the time-consuming, tedious, and not generally accessible methods typically employed.