Tapping the Full Potential of Infrared Spectroscopy for the Analysis of Technical Lignins

Abstract

We present an approach to overcome the challenges associated with the increasing demand of high‐throughput characterization of technical lignins, a key resource in the emerging bioeconomies. Our approach offers a resort from the lack of direct, simple, and low‐cost analytical techniques for lignin characterization by employing multivariate calibration models based on infrared (IR) spectroscopy to predict structural properties of lignins (i.e., functionality, molar mass). By leveraging a comprehensive database of over 500 well‐characterized technical lignin samples – a factor of 10 larger than previously used sets – our chemometric models achieved high levels of quality and statistical confidence for the determination of different functional group contents (RMSEPs of 4–16%). However, the statistical moments of the molar mass distribution are still best determined by size‐exclusion chromatography. Analyses of over 500 technical lignins offered a great opportunity to provide information on the general variability in kraft lignins and lignosulfonates (from different origins). Overall, the effected savings in analysis time (>>7 h), resources, and required sample mass combined with non‐destructiveness of the measurement satisfy key demands for efficient high‐throughput lignin analyses. Finally, we discuss the advantages, disadvantages, and limitations of our approach, along with critical insights into the associated chemical‐analytical and spectroscopic challenges.

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

“…Modeling of M w was found to provide more favorable RMSECV, R 2 CV, RMSEP, and relative error (RE) values than when predicting M n (Figure and Table ). This, as well as the relatively large bias in the regression line for the M n model, is in contrast to the observations by Sumerskii et al and as has been reported previously in our group for kraft lignin-based models. , The use of the logarithm of M w and M n was found to provide higher R 2 CV values and similar RMSECV/RMSEC (Table ), indicating that there is an improvement in the model’s reliability and no significant change in the degree of overfitting. The reduced RE in each case is due to linearization of the molecular weight values and thus the error, as has been reported by our group previously. , To our surprise, however, when changing from M n to log­( M n ), a decrease in R 2 Pred was found and observed, in spite of the similarly large increase in R 2 CV.…”

“… 38 Very recently, Sumerskii et al followed up on this concept with a greatly expanded sample set (500+ lignins), with models correlating either mid-IR (MIR) or near-IR (NIR) spectra with lignin structural properties ( Figure 1 ). 40 Functional group contents could be well-predicted in this study. The M w range of the 500+ samples was vast, from 2170 to 38 9500 g mol –1 for the lignosulfonates, and from 1190 to 70 900 g mol –1 for the kraft lignin samples.…”

“…Recent advances in molecular weight prediction by ATR-IR by Lancefield et al, Khalili et al, and Sumerskii and in this work.…”

“…This, as well as the relatively large bias in the regression line for the M n model, is in contrast to the observations by Sumerskii et al and as has been reported previously in our group for kraft lignin-based models. 38,40 The use of the logarithm of M w and M n was found to provide higher R 2 CV values and similar RMSECV/RMSEC (Table 3), indicating that there is an improvement in the model's reliability and no significant change in the degree of overfitting. The reduced RE in each case is due to linearization of the molecular weight values and thus the error, as has been reported by our group previously.…”

“…The elution solvent was tetrahydrofuran EMPLURA (Merck). The flow rate was 0.8 mL min −1 , the column temperature was 40 °C, and UV detection was recorded at 254 38 Khalili et al, 39 and Sumerskii 40 Size exclusion chromatography (SEC) was performed on nondepolymerised lignin samples. Here, samples were loaded on Waters Ultrahydrogel 500 (7.8 mm × 300 mm; 10 μm particle size) and Ultrahydrogel 120 (7.8 mm × 300 mm; 6 μm particle size) columns coupled in series.…”