2D HSQC-derived “dark forest” image with enhanced local resolution via first derivative processing–logarithmic cosine transformation (FDP–LCT): Demonstration on per-O-ethylated kappa- and iota-carrageenans

Abstract

Solution-state two-dimensional (2D) ¹H–¹³C HSQC NMR is a powerful tool for polysaccharide structure elucidation but often suffers from limited sensitivity and broad peaks due to the low natural abundance of ¹³C and poor digital resolution of the indirect dimension, respectively, as well as the typically low concentration and high viscosity of polysaccharide solutions. It is therefore pivotal to improve the resolution of 2D ¹H–¹³C HSQC spectra for accurate peak picking and assignment, particularly in the indirect ¹³C dimension. In this study, we developed an algorithm that combines first derivative processing with a novel logarithmic cosine transformation (FDP–LCT) to convert 2D ¹H–¹³C HSQC spectra into local-resolution-enhanced images resembling a dark forest of straight, densely standing trees. These images revealed sharpened spectral features and enabled extraction of precise ¹H and ¹³C chemical shifts, as demonstrated using per-O-ethylated kappa- and iota-carrageenans, two sulfated galactans differing only by a single substitution at the O-2 position of anhydrogalactose. In conclusion, this approach provides an effective post-acquisition strategy for enhancing digital resolution in 2D HSQC spectra and improving the structural analysis of closely related complex polysaccharides.