Determination of Sugar Concentrations in Aqueous Solution Using Multivariate Predictions Based on 1H-NMR Spectroscopy

Abstract

Renewable chemicals from carbohydrate-rich wastes, like furfural and 5-hydroxymethylfurfural (HMF), are gaining prominence as alternatives to petroleum-based resources. Assessing the suitability of biomass as feedstock for furfural and HMF production requires knowledge of its composition. This study focuses on developing and validating predictive models for individual sugar concentrations in hydrolysates using quantitative 1H NMR data. Utilizing partial least square (PLS) regression, the dataset includes 137 NMR spectra of multi-component sugar standards (arabinose, fructose, galactose, glucose, mannose, maltose, sucrose, and xylose). The best-performing model achieved an R² of 0.987–0.999 and RMSECV of 0.37–1.56 mM and is based on the non-overlapping area of the NMR spectrum. Real-world samples were used for validation, resulting in predicted sugar concentrations with a mean standard deviation of 0.5 mM. This high accuracy and streamlined analysis process make these models practical for quantifying large sample sets, showcasing the reliability and accessibility of extracting statistical information from H-NMR data.