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一种基于统计分析的指纹图谱建立新策略:在复方豆黄制剂质量评价中的应用。

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

机构信息

School of Pharmacy, Second Military Medical University, Shanghai 200433, China; Shanghai Key Laboratory for Pharmaceutical Metabolite Research, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.

School of Pharmacy, Second Military Medical University, Shanghai 200433, China; Shanghai Key Laboratory for Pharmaceutical Metabolite Research, School of Pharmacy, Second Military Medical University, Shanghai 200433, China; School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230031, China.

出版信息

Food Chem. 2018 Aug 30;258:189-198. doi: 10.1016/j.foodchem.2018.03.067. Epub 2018 Mar 16.

Abstract

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study.

摘要

药食同源的大豆黄卷是一种著名的中药。本研究建立了一种简单有效的质量指纹图谱分析方法,并结合化学计量学方法,用于评估大豆黄卷的质量。首先,应用相似性分析(SA)和层次聚类分析(HCA)选择定性标志物,这些标志物明显影响大豆黄卷的质量。然后,采用高分辨离子阱/飞行时间质谱(LC-IT-TOF-MS)对 21 种化学成分进行鉴定和表征。随后,进行主成分分析(PCA)和正交偏最小二乘判别分析(OPLS-DA),以选择来自不同产地的大豆黄卷样品的定量标志物。此外,对 11 种具有统计学意义的化合物进行了定量测定,为质量评价提供了准确和有价值的数据。本研究提出了一种基于“统计分析的指纹图谱建立”的新策略,为进一步研究提供了有价值的参考。

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