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Background Multiple analytical methods have been developed to determine the ratios of aromatic lignin units, particularly the syringyl/guaiacyl (S/G) ratio, of lignin biopolymers in plant cell walls. Chemical degradation methods such as thioacidolysis produce aromatic lignin units that are released from certain linkages and may induce chemical changes rendering it difficult to distinguish and determine the source of specific aromatic lignin units released, as is the case with nitrobenzene oxidation methodology. NMR methods provide powerful tools used to analyze cell walls for lignin composition and linkage information. Pyrolysis-mass spectrometry methods are also widely used, particularly as high-throughput methodologies. However, the different techniques used to analyze aromatic lignin unit ratios frequently yield different results within and across particular studies, making it difficult to interpret and compare results. This also makes it difficult to obtain meaningful insights relating these measurements to other characteristics of plant cell walls that may impact biomass sustainability and conversion metrics for the production of bio-derived fuels and chemicals. Results The authors compared the S/G lignin unit ratios obtained from thioacidolysis, pyrolysis-molecular beam mass spectrometry (py-MBMS), HSQC liquid-state NMR and solid-state (ss) NMR methodologies of pine, several genotypes of poplar, and corn stover biomass. An underutilized approach to deconvolute ssNMR spectra was implemented to derive S/G ratios. The S/G ratios obtained for the samples did not agree across the different methods, but trends were similar with the most agreement among the py-MBMS, HSQC NMR and deconvoluted ssNMR methods. The relationship between S/G, thioacidolysis yields, and linkage analysis determined by HSQC is also addressed. Conclusions This work demonstrates that different methods using chemical, thermal, and non-destructive NMR techniques to determine native lignin S/G ratios in plant cell walls may yield different results depending on species and linkage abundances. Spectral deconvolution can be applied to many hardwoods with lignin dominated by S and G units, but the results may not be reliable for some woody and grassy species of more diverse lignin composition. HSQC may be a better method for analyzing lignin in those species given the wealth of information provided on additional aromatic moieties and bond linkages. Additionally, trends or correlations in lignin characteristics such as S/G ratios and lignin linkages within the same species such as poplar may not necessarily exhibit the same trends or correlations made across different biomass types. Careful consideration is required when choosing a method to measure S/G ratios and the benefits and shortcomings of each method discussed here are summarized.

The inherent reactivity of lignin in conventional biomass processing commonly prevents isolation of native lignin and limits monomer yields from catalytic depolymerization strategies that target aryl-ether bonds. Here we show that flow-through solvolysis with methanol at 225 °C produces native-like lignin from poplar, enabling the study of intrinsic lignin properties and evaluation of steady-state lignin depolymerization processes.

Correction for ‘Flow-through solvolysis enables production of native-like lignin from biomass’ by David G. Brandner et al., Green Chem., 2021, 23, 5437–5441, DOI: 10.1039/D1GC01591E.

Biomass analysis is a slow and tedious process and not solely due to the long generation time for most plant species. Screening large numbers of plant variants for various geno-, pheno-, and chemo-types, whether naturally occurring or engineered in the lab, has multiple challenges. Plant cell walls are complex, heterogeneous networks that are difficult to deconstruct and analyze. Macroheterogeneity from tissue types, age, and environmental factors makes representative sampling a challenge and natural variability generates a significant range in data. Using high throughput (HTP) methodologies allows for large sample sets and replicates to be examined, narrowing in on more precise data for various analyses. This review provides a comprehensive survey of high throughput screening as applied to biomass characterization, from compositional analysis of cell walls by NIR, NMR, mass spectrometry, and wet chemistry to functional screening of changes in recalcitrance via HTP thermochemical pretreatment coupled to enzyme hydrolysis and microscale fermentation. The advancements and development of most high-throughput methods have been achieved through utilization of state-of-the art equipment and robotics, rapid detection methods, as well as reduction in sample size and preparation procedures. The computational analysis of the large amount of data generated using high throughput analytical techniques has recently become more sophisticated, faster and economically viable, enabling a more comprehensive understanding of biomass genomics, structure, composition, and properties. Therefore, methodology for analyzing large datasets generated by the various analytical techniques is also covered.

Large populations of potential cellulosic biomass feedstocks are currently being screened for fuel and chemical applications. The monomeric sugar content, released through hydrolysis, is of particular importance and is currently measured with time-consuming HPLC methods. A method for sugar detection is presented here that employs (1)H NMR spectra regressed against primary HPLC sugar concentration data to build partial least squares (PLS) models. The PLS2 model is able to predict concentrations of both major sugar components, like glucose and xylose, and minor sugars, such as arabinose and mannose, in biomass hydrolysates. The model was built with 65 samples from a variety of different biomass species and covers a wide range of sugar concentrations. Model predictions were validated with a set of 15 samples which were all within error of both HPLC and NMR integration measurements. The data collection time for these NMR measurements is less than 20 min, offering a significant improvement to the 1 h acquisition time that is required for HPLC.