首页> 外文期刊>Journal of Pharmaceutical and Biomedical Analysis: An International Journal on All Drug-Related Topics in Pharmaceutical, Biomedical and Clinical Analysis >Differentiating Puerariae Lobatae Radix and Puerariae Thomsonii Radix using HPTLC coupled with multivariate classification analyses
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Differentiating Puerariae Lobatae Radix and Puerariae Thomsonii Radix using HPTLC coupled with multivariate classification analyses

机译:使用HPTLC结合多变量分类分析区分葛根和葛根

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摘要

Puerariae Lobatae Radix (PLR), the root of Pueraria lobata, is a traditional Chinese medicine for treating diabetes and cardiovascular diseases. Puerariae Thomsonii Radix (PTR), the root of Pueraria thomsonii, is a closely related species to PLR and has been used as a PLR substitute in clinical practice. The aim of this study was to compare the classification accuracy of high performance thin-layer chromatography (HPTLC) with that of ultra-performance liquid chromatography (UPLC) in differentiating PLR from PTR. The Matlab functions were used to facilitate the digitalisation and pre-processing of the HPTLC plates. Seven multivariate classification methods were evaluated for the two chromatographic methods. The results demonstrated that the HPTLC classification models were comparable to the UPLC classification models. In particular, k-nearest neighbours, partial least square-discriminant analysis, principal component analysis-discriminant analysis and support vector machine-discriminant analysis showed the highest rate of correct species classification, whilst the lowest classification rate was obtained from soft independent modelling of class analogy. In conclusion, HPTLC combined with multivariate analysis is a promising technique for the quality control and differentiation of PLR and PTR.
机译:葛根的葛根(PLR)是治疗糖尿病和心血管疾病的传统中药。葛根(Pueraria thomsonii)的根葛(PTR)是与PLR密切相关的物种,在临床实践中已被用作PLR替代品。这项研究的目的是比较高效薄层色谱(HPTLC)和超高效液相色谱(UPLC)的分类准确性,以区分PLR和PTR。 Matlab功能用于促进HPTLC板的数字化和预处理。对两种色谱方法评估了七种多元分类方法。结果表明,HPTLC分类模型与UPLC分类模型相当。特别是,k近邻,偏最小二乘判别分析,主成分分析-判别分析和支持向量机-判别分析显示正确的物种分类率最高,而从类别的软独立建模获得的最低分类率比喻。总之,HPTLC与多变量分析相结合是对PLR和PTR进行质量控制和区分的有前途的技术。

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