## 2 Evolution of NMR Methodologies
### Multidimensional and Advanced NMR Techniques
#### 2.3.1 Multidimensional NMR  
Multivariate NMR experiments, like 2D HSQC-TOCSY and 3D NOESY-HSQC, offer extremely good resolution for complex polysaccharides. Scientists can use these techniques to resolve overlapping signals and determine long-range correlations. To systematically compare the advantages and limitations of key multidimensional NMR techniques (HSQC vs. HMBC vs. NOESY) for polysaccharide analysis, we summarize their characteristics in Table 2. These findings are pivotal for understanding the structures of complex polysaccharides. For example, [PERSON] et al. used 2D NMR to characterize the stability of a tamarind seed polysaccharide balsam that was changed with octenylsuccinic anhydride, and they found that it could be a useful ingredient in functional foods [13]. [PERSON] and [PERSON] studied molecular mobility in starch polysaccharides using two-dimensional ssNMR spectroscopy, revealing dynamic properties critical to food applications [48]. This study demonstrates that multidimensional NMR can uncover subtle changes and link them to functional characteristics. [PERSON] et al. used 3D NOESY-HSQC to study polysaccharides and glycoprotein polysaccharides. They managed to improve the spectral resolution and identify unique structural patterns that influence biological activity [47]. Their work shows that multidimensional NMR is important for connecting the structure and function of bioactive polysaccharides. In addition, [PERSON] et al. used CASPER along with multivariate NMR data to determine the sequence and structure of complex oligosaccharides [6]. [PERSON] et al. demonstrated that combining computational tools with NMR data can be used to analyze O-antigen polysaccharides, further expanding the practical use of multidimensional NMR in explaining structures [49]. [PERSON] et al. brought up the problem of resolving overlapping signals. They especially focused on \begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **Advantages** & **Limitations** & **Typical Applications** \\ \hline HSQC & \({}^{1}\)H-\({}^{13}\)C direct correlation; ideal & & Primary structure \\  & for glycosidic bond & Lower sensitivity for & determination (branching \\  & identification and & low-abundance samples & patterns, anomeric \\  & monosaccharide composition & & configurations) \\ \hline HMBC & Detects \({}^{1}\)H-\({}^{13}\)C long-range & Weak signal intensity; requires & Sequence determination in \\  & couplings (2–4 bonds); reveals & longer acquisition time; challenging & complex polysaccharides \\  & cross-glycosic linkages & for overlapping signals & (e.g., bacterial O-antigens) \\ \hline NOESY & Provides spatial proximity & & \\  & information (\(<\)5 Å); reveals & Sensitive to molecular motion; & Conformational studies \\  & conformation and & difficult to quantify & (e.g., food gels, biofilms) \\  & intermolecular interactions & & \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of key NMR techniques in polysaccharides (e.g., HSQC vs. HMBC vs. NOESY). advanced methods for decoding the nuclear Overhauser effect [50]. This combination of computational analysis and experimental NMR data shows that multidimensional methods have potential in automated structure elucidation. [PERSON] et al. used multidimensional relaxation time NMR to study dual mixtures of liquids and polysaccharides. Through this study, they developed some ideas about the molecular fluidity and behavior of the mixture [51]. These examples illustrate that multidimensional NMR technology has become very important in polysaccharide research, particularly for dealing with the complex structures of polysaccharides and studying the relationship between their structure and functions. As the resolution and sensitivity of multidimensional NMR continuously improves, it is paving the way for new discoveries in polysaccharide science fields like food science, biomedical science, and environmental science. This study is notable for the application-oriented study, demonstrating how structured analytics can offer information for product development.