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Aus hölzernen Cellulosen und Hemicellulosen können durch enzymatische Hydrolyse fermentierbare Zucker für die Herstellung von Chemikalien und Treibstoffen gewonnen werden. Die bisherige Forschung fokussiert sich oft auf die Nutzung dieser Zucker zur Gewinnung von Ethanol. Daneben muss aber auch die stoffliche Nutzung zur Gewinnung von Grundchemikalien berücksichtigt werden. Eine solche Grundchemikalie ist Itakonsäure. Obwohl die biotechnologische Itaconsäureproduktion bereits eingehend untersucht und etabliert ist, gestaltet sie sich im Rahmen von Bioraffinerien der zweiten Generation als schwierig, da der überwiegend verwendete Produktionsorganismus gegen eine weite Bandbreite von Inhibitoren sensibel ist. Die Herstellung von Itaconsäure aus Buchenholzhydrolysaten wird im Rahmen der deutschen Lignocellulose-Bioraffinerie entwickelt. Die unbehandelten Hydrolysate ermöglichen weder das Wachstum von Aspergillus terreus noch die Bildung von Itaconsäure. Daher werden Möglichkeiten zur Konditionierung des Hydrolysates mit dem Ziel einer Itaconsäureproduktion mit hohen Ausbeuten und Konzentrationen vorgestellt.
Although several successful applications of benchtop nuclear magnetic resonance (NMR) spectroscopy in quantitative mixture analysis exist, the possibility of calibration transfer remains mostly unexplored, especially between high- and low-field NMR. This study investigates for the first time the calibration transfer of partial least squares regressions [weight average molecular weight (Mw) of lignin] between high-field (600 MHz) NMR and benchtop NMR devices (43 and 60 MHz). For the transfer, piecewise direct standardization, calibration transfer based on canonical correlation analysis, and transfer via the extreme learning machine auto-encoder method are employed. Despite the immense resolution difference between high-field and low-field NMR instruments, the results demonstrate that the calibration transfer from high- to low-field is feasible in the case of a physical property, namely, the molecular weight, achieving validation errors close to the original calibration (down to only 1.2 times higher root mean square errors). These results introduce new perspectives for applications of benchtop NMR, in which existing calibrations from expensive high-field instruments can be transferred to cheaper benchtop instruments to economize.
The molecular weight properties of lignins are one of the key elements that need to be analyzed for a successful industrial application of these promising biopolymers. In this study, the use of 1H NMR as well as diffusion-ordered spectroscopy (DOSY NMR), combined with multivariate regression methods, was investigated for the determination of the molecular weight (Mw and Mn) and the polydispersity of organosolv lignins (n = 53, Miscanthus x giganteus, Paulownia tomentosa, and Silphium perfoliatum). The suitability of the models was demonstrated by cross validation (CV) as well as by an independent validation set of samples from different biomass origins (beech wood and wheat straw). CV errors of ca. 7–9 and 14–16% were achieved for all parameters with the models from the 1H NMR spectra and the DOSY NMR data, respectively. The prediction errors for the validation samples were in a similar range for the partial least squares model from the 1H NMR data and for a multiple linear regression using the DOSY NMR data. The results indicate the usefulness of NMR measurements combined with multivariate regression methods as a potential alternative to more time-consuming methods such as gel permeation chromatography.