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.