Qualitative and quantitative analysis of polysaccharides in herb formula remain challenge due to the limited choices of analytical methods concerning the intrinsic characteristics of large molecular mass. Herein, an oligosaccharide-marker approach was newly developed for quality assessment of polysaccharides in herbal materials, using Dendrobium officinale as a case study. This method involved partial acid hydrolysis of D. officinale polysaccharide (DOP) followed by p-aminobenzoic ethyl ester (ABEE) derivatization. Two ABEE-labeled oligosaccharides namely, Te-Man-ABEE and Pen-Man-ABEE, were selected as chemical markers due to their high specificity in herb formula. The linear relationship between the content of these two markers and the content of DOP was then successfully established respectively. The linear relationship was further transformed to that between peak area of chemical markers and DOP content so that chemical markers were not necessary to be isolated for analysis. This linear relationship was systemically validated in terms of precision and accuracy. The results showed that these two oligosaccharide-markers presented a good linear relationship with DOP (R2 ≥ 0.997) in the range of 0.68–16.02 μg. These markers also demonstrated satisfactory precision (RSD < 7.0%), and recovery (91.41%–118.30%) in real sample determination. Additionally, there was no significant difference between the results given by the two chemical markers as the RSD values were not more than 7.0%. While concerning the results given by the oligosaccharide-markers and the previously-published polysaccharide marker, the RSD value was not more than 6.4%. These suggest that the oligosaccharide-marker approach is a simple, quick, and reliable method to qualitatively and quantitatively determine of specific polysaccharide in herb formula.
|Journal||Journal of Chromatography A|
|Publication status||Published - 6 Dec 2019|
Scopus Subject Areas
- Analytical Chemistry
- Organic Chemistry
- ABEE-labeled oligosaccharide-marker
- Qualitative and quantitative analysis