Chemical Fingerprinting and Multivariate Analysis of Different Grades of Farfarae Flos Using HPLC and UPLC-ESI-Q/TOF-MS/MS.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2025-01-14 DOI:10.1002/pca.3501
Yilei Zhang, Yihan Zhu, Gang Zhang, Jing Gao, Yonggang Yan, Ying Chen, Qi Guo, BingYue Yang, Liang Peng
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Abstract

Introduction: Farfarae Flos is widely used as a traditional herbal medicine. Currently, its size has been the primary grading criterion used in market circulation. Whether this empirical criterion can accurately reflect the quality of the medicinal material has not been systematically studied.

Objective: This study aimed to evaluate the quality of Farfarae Flos from different regions based on their grades.

Methods: Ultra-high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q/TOF-MS/MS) were applied to study the chemical constituents of Farfarae Flos; high performance liquid chromatography (HPLC) was used to quantify the content of Farfarae Flos samples. Meanwhile, fingerprint analysis and chemometric methods, including principal component analysis (PCA) and analysis of variance (ANOVA), were used to evaluate the quality differences among 33 batches of Farfarae Flos samples of different grades.

Results: A total of 95 individual components were identified in Farfarae Flos. Fingerprint analysis revealed 23 common peaks, with fingerprint similarity among the 33 batches ranging from 0.838 to 0.995. PCA divided the 33 batches of Farfarae Flos into three categories based on their grades. ANOVA indicated significant differences in five of the 14 main active components across different grades of Farfarae Flos, with two components showing extremely significant differences. HPLC content determination showed that the content of 11 main active components was positively correlated with the grades of Farfarae Flos.

Conclusion: This method is straightforward, efficient, and reliable, offering a valuable reference for establishing quality grading standards and ensuring the quality control of Farfarae Flos.

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HPLC和UPLC-ESI-Q/TOF-MS/MS法测定不同等级法花的化学指纹图谱及多变量分析
简介远志是一种广泛使用的传统草药。目前,市场上流通的远志主要以其大小作为分级标准。这一经验标准是否能准确反映药材的质量,目前还没有系统的研究:本研究旨在根据远志的等级评估不同地区远志的质量:方法:采用超高效液相色谱-电喷雾四极杆飞行时间质谱法(UPLC-ESI-Q/TOF-MS/MS)研究远志的化学成分;采用高效液相色谱法(HPLC)定量分析远志样品的含量。同时,采用指纹图谱分析和化学计量学方法,包括主成分分析(PCA)和方差分析(ANOVA),评估了33批次不同等级远志香精样品的质量差异:结果:在 Farfarae Flos 中总共发现了 95 个单独的成分。指纹分析显示有 23 个共同峰,33 个批次的指纹相似度在 0.838 至 0.995 之间。PCA 根据等级将 33 个批次的 Farfarae Flos 分成三类。方差分析表明,在 14 种主要有效成分中,有 5 种成分在不同等级的 Farfarae Flos 之间存在显著差异,其中有两种成分存在极显著差异。高效液相色谱含量测定显示,11 种主要活性成分的含量与 Farfarae Flos 的等级呈正相关:结论:该方法简单、高效、可靠,为建立远志的质量分级标准和确保远志的质量控制提供了有价值的参考。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
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.
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