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.