Phytochemical analysis : PCA · 2025
Discrimination of Three Panax Plants Based on Small Molecular Weight Saccharide Fractions Using HPAEC-PAD Coupled With Multivariate Statistical Models.
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Wang Yue, Liu Xue-Qing, Liang Jun, Kuang Hai-Xue, Xia Yong-Gang
Аннотация
INTRODUCTION: Accurate discrimination of Panax ginseng, P. quinquefolius, and P. notoginseng is crucial in traditional Chinese medicine (TCM). Distinct from conventional small molecule compounds (e.g., saponins, volatile oils), proteins, or polysaccharides, this study pioneers small molecular weight saccharide fractions (SMS) as a novel class of biomarkers for species differentiation.
OBJECTIVES: This study aimed to develop an SMS-based method for distinguishing the three Panax plants.
METHODS: A simple and reproducible workflow was developed for obtaining natural SMS from the three Panax species through systematic optimization of ultrasonic extraction protocols and purification procedures. The chemical structures of three SMS were characterized by integrating non-targeted HILIC-ESI--QTOF-MSE with targeted HILIC-ESI--QTOF-MS2 analysis. SMS-based fingerprinting of the three Panax species was constructed using HPAEC-PAD, followed by multivariate statistical models (PCA, PLS-DA, and LDA models) for species discrimination and cluster analysis.
RESULTS: The optimal extraction conditions were identified as 60°C, a solid-liquid ratio of 1:30, 50 min duration, and 50% ethanol concentration. SMS with degrees of polymerization (DPs) 2 to 13 were identified, revealing preliminary structural differences among the three Panax species. The established HPAEC-PAD fingerprinting combined with multivariate models (PCA, PLS-DA, and LDA) enabled distinct species separation and achieved high classification accuracy, with successful prediction of external validation samples.
CONCLUSIONS: This study presents the first integration of HPAEC-PAD fingerprinting with multivariate statistical models for successfully discriminating three Panax species based on SMS, validating SMS as a reliable class of biomarkers for species identification and offering a distinct alternative to traditional markers.
OBJECTIVES: This study aimed to develop an SMS-based method for distinguishing the three Panax plants.
METHODS: A simple and reproducible workflow was developed for obtaining natural SMS from the three Panax species through systematic optimization of ultrasonic extraction protocols and purification procedures. The chemical structures of three SMS were characterized by integrating non-targeted HILIC-ESI--QTOF-MSE with targeted HILIC-ESI--QTOF-MS2 analysis. SMS-based fingerprinting of the three Panax species was constructed using HPAEC-PAD, followed by multivariate statistical models (PCA, PLS-DA, and LDA models) for species discrimination and cluster analysis.
RESULTS: The optimal extraction conditions were identified as 60°C, a solid-liquid ratio of 1:30, 50 min duration, and 50% ethanol concentration. SMS with degrees of polymerization (DPs) 2 to 13 were identified, revealing preliminary structural differences among the three Panax species. The established HPAEC-PAD fingerprinting combined with multivariate models (PCA, PLS-DA, and LDA) enabled distinct species separation and achieved high classification accuracy, with successful prediction of external validation samples.
CONCLUSIONS: This study presents the first integration of HPAEC-PAD fingerprinting with multivariate statistical models for successfully discriminating three Panax species based on SMS, validating SMS as a reliable class of biomarkers for species identification and offering a distinct alternative to traditional markers.