As society is rapidly converting from fossil-based materials to greener alternatives, the valorization of lignin through chemical modification has been given considerable attention. Characterizing this highly heterogeneous biopolymer is a constant challenge, and an emerging strategy for dealing with variations in material characteristics is combining traditional analytical techniques with chemometrics, such as Fourier-transform infrared (FTIR) spectroscopy with partial least squares regression (PLSR). Here, a calibration data set was built based on FTIR spectra and the total carbon-hydrogen bond (CHB) content of mixtures of technical lignins and alkanes, meant to emulate esterified samples. From this data, a PLSR model was built which predicted the CHB content of esterified lignin reaction products with an RMSECV=5.685 mmol/g and RMSEPred=5.827 mmol/g, and from which the weight percentage of ester-to-lignin was determined. When compared to wet-chemical analysis, good agreement between the techniques was found with an obtained RMSEPred=8.3 % and a R2Train=0.9752 for the degree of esterification. This indicates high model predictability and goodness of fit, and that the calibration data set successfully emulated esterified lignin samples.
This work was part of the “LignoWax – Green Wax Inhibitorsand Production Chemicals based on Lignin” project (grantnumber 326876). The authors thank the Norwegian ResearchCouncil, Equinor ASA and ChampionX Norge AS for financialsupport, and the staff at RISE PFI AS for technical support.