Chemical Pattern Recognition and Quality Assessment of <scp> <i>Gleditsia sinensis</i> </scp> Gum Utilizing Fingerprint Spectroscopy and Quantitative Analysis

To address the limitations of the current quality standard for Gleditsia sinensis polysaccharide gum (GSPG), which uses only a single component as the quality control marker, employs low-resolution detection methods, and cannot comprehensively characterize overall chemical characteristics, this study was the first to establish a quality evaluation method combining 1-phenyl-3-methyl-5-pyrazolone (PMP) precolumn derivatization-high-performance liquid chromatography (HPLC) fingerprinting, multicomponent quantification, and chemical pattern recognition for GSPG. The derivatization and chromatographic conditions were optimized to establish the HPLC fingerprint, and the contents of four components in GSPG were assayed. Samples were distinguished via similarity analysis, cluster analysis (CA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) for identifying key components governing quality variation. Results show that the fingerprint profiles of 10 GSPG batches revealed the following six common peaks and characterized five components, with similarity values ranging from 0.907 to 0.996. CA and OPLS-DA categorized samples into two categories, while PCA stratified them into three categories, screening two differential biomarkers (VIP > 1). Content determination showed consistent monosaccharide compositions across origins, with D-mannose showing the highest level. The integration of HPLC fingerprinting, chemical pattern recognition, and content determination proved efficient, accurate, stable, and reliable for the quality assessment of GSPG.