A translatable IR-chemometrics model for the rapid prediction of structural and material properties of technical lignins

Abstract

Technical lignins are an industrial byproduct of plant biomass processing, for example, paper production or biorefinery operations. They are highly functional and aromatic, making them potentially suitable for a diverse range of applications; however, their exact structural composition depends on the plant species and the industrial process involved. A major bottleneck to lignin valorization and to biorefining in general is the equipment and time investment required for the full characterization of each sample. An array of wet chemical, spectroscopic, chromatographic and thermal methods are typically required to effectively characterize a given lignin sample. To ease the analytical burden, measured lignin properties can be correlated with detailed spectroscopic data obtained from a rapid analytical technique, such as attenuated total reflectance (ATR) Fourier-transform infrared (IR) spectroscopy, which requires minimal sample preparation. With sufficient sensitivity of the spectroscopic data, partial least squares regression models can be calibrated and, thus, predict these properties for future samples for which only the ATR–IR spectra are recorded. So far, several structural and macromolecular properties of lignin have been correlated with ATR–IR spectral data and quantitatively predicted in such a manner, including molecular weight, hydroxyl group content ([OH]), interunit linkage abundance and glass transition temperature. The protocol to apply this powerful lignin characterization methodology is described herein. Here, we also present a simple calibration transfer step, which when implemented before partial least squares regression, addresses the problem of instrument dependency. With the calibrated model, it is possible to determine lignin properties from a single ATR–IR spectral measurement (in ~5 min per sample).