Classification of papyrus fragments by chemometric analysis of near-infrared spectra.

Papyrus has been used for millennia to record information, for sophisticated works of art as well as mundane notes. The collection, identification, and translation of papyrus fragments therefore opens a gateway into the past. To aid the efforts to access the history recorded in papyri, we investigated the suitability of NIR spectroscopy to perform two tasks: One is to support the authentication of ancient papyri, by differentiation of papyri that were manufactured more recently and subjected to accelerated ageing to resemble the originals. The other is the extensive task to piece together papyrus fragments into readable texts again. In museums around the world, more than 100,000 ancient papyrus fragments still wait for their proper assembly, deciphering and publication. The papyrus writing-ground was analysed by near-infrared (NIR) spectroscopy, and the spectra were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares discriminant analysis (PLS-DA), and self-organizing maps (SOM). Cluster analysis and PLS-DA proved to be useful tools for distinguishing modern papyri from ancient papyri which were provided by collections in Vienna and Leipzig. Neither natural nor accelerated ageing affected the classification. A PLS-DA classification model, constructed from NIR spectra of 89 model scores, detected recent Papyri samples with 100 % sensitivity and specificity, even after accelerated ageing. The identification of groups of fragments of ancient papyri based on NIR spectra and chemometry is not straightforward. HCA, which focuses on the differences between samples, only grouped the fragments of 4 out of 20 papyri correctly. SOM, which rather focuses on the similarities, grouped 6 sets of fragments correctly. An automated grouping of fragments remains difficult, since the fragments themselves are heterogeneous while similarities between unrelated ancient papyri can be large.

Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy.

BACKGROUND: Multivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model. RESULTS: We describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value. SIGNIFICANCE: Robust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.

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

We present an approach to overcome the challenges associated with the increasing demand of high-throughput characterization of technical lignins, a key resource in 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 also 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.

A novel approach to analyze the impact of lytic polysaccharide monooxygenases (LPMOs) on cellulosic fibres.

Enzymatic treatment of cellulosic fibres is a green alternative to classical chemical modification. For many applications, mild procedures for cellulose alteration are sufficient, in which the fibre structure and, therefore, the mechanical performance of cellulosic fibres are preserved. Lytic polysaccharide monooxygenases (LPMOs) bear a great potential to become a green reagent for such targeted cellulose modifications. An obstacle for wide implementation of LPMOs in tailored cellulose chemistry is the lack of suitable techniques to precisely monitor the LPMO impact on the polymer. Soluble oxidized cello-oligomers can be quantified using chromatographic and mass-spectrometric techniques. A considerable portion of the oxidized sites, however, remain on the insoluble cellulose fibres, and their quantification is difficult. Here, we describe a method for the simultaneous quantification of oxidized sites on cellulose fibres and changes in their molar mass distribution after treatment with LPMOs. The method is based on quantitative, heterogeneous, carbonyl-selective labelling with a fluorescent label (CCOA) followed by cellulose dissolution and size-exclusion chromatography (SEC). Application of the method to reactions of seven different LPMOs with pure cellulose fibres revealed pronounced functional differences between the enzymes, showing that this CCOA/SEC/MALS method is a promising tool to better understand the catalytic action of LPMOs.

Profiling of historical rag papers by their non-cellulosic polysaccharide composition.

Hemicellulose and pectin are noteworthy components of historical European rag papers, and have not been studied in detail so far. Rag papers were made from used textiles, and fiber-based utilities, such as ropes and bags. These had been prepared until the mid-19th century from plant-based fibers. Their polysaccharide composition could relate to their condition and history. This information can be expected to hold importance for the preservation and conservation of historical objects. We investigated a collection of rag papers of different age for their composition of non-cellulosic polysaccharides, and compared the findings with modern rag papers and wood pulps. Furthermore, a non-destructive determination of the hemicellulose and pectin content by near-infrared spectroscopy was developed. Historical rag papers had a lower hemicellulose/pectin content than pulps; the fractions of rhamnose, galactose, and arabinose were higher, while xylose was lower. In modern rag papers, xylose tended to be at the higher end of the range, which suggests a degradation of hemicelluloses/pectin over time or a change in raw materials and manufacturing. Rag papers also showed higher crystallinity than wood pulp papers. These findings provide insights into rag paper characteristics and offer potential classification methods.