Rapid determination of sugar content in biomass hydrolysates using nuclear magnetic resonance spectroscopy

Abstract

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.

Recent innovations in analytical methods for the qualitative and quantitative assessment of lignin

“…S-H bonds [332,335] Physical differences in samples detrimental to spectra [335] NMR Un-paralleled structural information available [27] Multi-dimensional NMR provides enhanced spectral resolution [26,27] Non-destructive [27] Non-invasive [27] Recent advances probing higher-throughput capabilities [339] Lignin quantitation is difficult due to spectral overlap [26] Most techniques considered low-throughput [95] Lack of spectral resolution, especially in lignin region [27,162] Lack of sensitivity [27] Costly instrumentation [340] …”

Special issue: Application of biotechnology for biofuels: transforming biomass to biofuels

“…However, to identify changes in cell wall properties resulting from genetic manipulation of specific genes in these pathways requires screening of large number of mutant plants. High throughput (HTP) analytical techniques for screening and characterizing cell wall components of large sample sizes of mutant plants are essential (Foston and Ragauskas 2012; Gjersing et al 2012), as are rapid techniques for evaluating the susceptibility of these mutants to pretreatment and enzymatic hydrolysis (Decker et al 2009). Recent developments have significantly advanced our ability to screen large number of plant variants for changes in recalcitrance, however these techniques require specialized equipment and expensive robotics (Selig et al 2010).…”

“…Similarly, understanding the cell wall composition of these same large sample sets is also a challenge. The current standard analytical technique for quantifying biomass cell wall components utilizes a two-stage acid-hydrolysis followed by high-performance liquid chromatography (HPLC) and gravimetric determinations, which are both time and labor intensive (Gjersing et al 2012). Therefore, new HTP analytical technologies for faster and more accurate determination of cell wall components are essential to make the entire process truly high throughput and cost-effective.…”

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

“…While wet chemical data are preferable, a number of high-throughput biomass characterization approaches are available based on indirect characterization methods. These high-throughput approaches for characterizing plant cell wall composition, properties, or the response to processing include non-destructive techniques such as infrared spectroscopy [27][28][29][30], Raman scattering spectroscopy [27], NMR spectroscopy [31], and destructive characterization techniques such as analytical pyrolysis [32,33]. While direct correlations between the data obtained from these techniques and the plant cell wall property of interest have been established [34], these techniques are typically coupled to chemometric models.…”