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Discrimination of Radix Astragali from Different Growth Patterns, Origins, Species, and Growth Years by an H1-NMR Spectrogram of Polysaccharide Analysis Combined with Chemical Pattern Recognition and Determination of Its Polysaccharide Content and Immunological Activity

线性判别分析 根(腹足类) 偏最小二乘回归 多糖 模式识别(心理学) 人工智能 黄芪甲苷 数学 多元分析 生物 化学 传统医学 色谱法 计算机科学 统计 植物 生物化学 医学 高效液相色谱法
作者
Yali Guo,Bing Wang,Lifei Gu,Guo Yin,Shuhong Wang,Meifang Li,Lijun Wang,Xie‐an Yu,Tiejie Wang
出处
期刊:Molecules [Multidisciplinary Digital Publishing Institute]
卷期号:28 (16): 6063-6063 被引量:11
标识
DOI:10.3390/molecules28166063
摘要

The fraud phenomenon is currently widespread in the traditional Chinese medicine Radix Astragali (RA) market, especially where high-quality RA is substituted with low-quality RA. In this case, focused on polysaccharides from RA, the classification models were established for discrimination of RA from different growth patterns, origins, species, and growth years. 1H Nuclear Magnetic Resonance (H1-NMR) was used to establish the spectroscopy of polysaccharides from RA, which were used to distinguish RA via chemical pattern recognition methods. Specifically, orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were used to successfully establish the classification models for RA from different growth patterns, origins, species, and growth years. The satisfactory parameters and high accuracy of internal and external verification of each model exhibited the reliable and good prediction ability of the developed models. In addition, the polysaccharide content and immunological activity were also tested, which was evaluated by the phagocytic activity of RAW 264.7. And the result showed that growth patterns and origins significantly affected the quality of RA. However, there was no significant difference in the aspects of origins and growth years. Accordingly, the developed strategy combined with chemical information, biological activity, and multivariate statistical method can provide new insight for the quality evaluation of traditional Chinese medicine.
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