Discrimination of Three Panax Plants Based on Small Molecular Weight Saccharide Fractions Using HPAEC-PAD Coupled With Multivariate Statistical Models.

IF 2.6 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Phytochemical Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-07 DOI:10.1002/pca.70035

Yue Wang, Xue-Qing Liu, Jun Liang, Hai-Xue Kuang, Yong-Gang Xia

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引用次数: 0

Abstract

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-MS^E 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.

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基于HPAEC-PAD和多元统计模型的三种人参小分子量糖组分鉴别

摘要：准确鉴别人参、西洋参和三七在中药中具有重要意义。与传统的小分子化合物（如皂苷、挥发油）、蛋白质或多糖不同，本研究将小分子量糖组分（SMS）作为一种新的物种分化生物标志物。目的：建立一种基于短信的三七属植物鉴别方法。方法：通过对三种人参的超声提取工艺和纯化工艺进行系统优化，建立了一套简单、可重复性高的提取工艺流程。通过非靶向HILIC-ESI—QTOF-MSE和靶向HILIC-ESI—QTOF-MS2分析，对3种SMS的化学结构进行了表征。采用HPAEC-PAD构建了3种人参的sms指纹图谱，并采用多元统计模型（PCA、PLS-DA和LDA模型）进行物种判别和聚类分析。结果：最佳提取条件为60℃、料液比1:30、提取时间50 min、乙醇浓度50%。聚合度（DPs）在2 ~ 13之间的短链氨基酸被鉴定出来，初步揭示了三种人参的结构差异。建立的HPAEC-PAD指纹图谱结合多元模型（PCA、PLS-DA和LDA），物种分离清晰，分类精度高，成功预测了外部验证样品。结论：本研究首次将HPAEC-PAD指纹图谱与多元统计模型相结合，成功地基于SMS对三种人参物种进行了鉴别，验证了SMS作为一种可靠的物种鉴定生物标志物，为传统标记提供了一种独特的选择。

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来源期刊

Phytochemical Analysis 生物-分析化学

CiteScore

6.00

自引率

6.10%

发文量

审稿时长

1.7 months

期刊介绍： Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.