Spectrum-effect relationship between HPLC fingerprints and antioxidant activity of Qi-Fu-Yin based on multiple statistical correlation analysis.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-10-01 Epub Date: 2024-05-22 DOI:10.1002/pca.3396
Hengyu Li, Hongwei Zhao, Lingxiao Chen, Yong Yang, Shixue Wang, Rongyu Gao, Xiaorui Cheng
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Abstract

Introduction: Qi-Fu-Yin has been used to treat Alzheimer's disease (AD) in China. Oxidative stress has been recognized as a factor in AD progress. To date, there is no quality control method to ensure batch-to-batch consistency of Qi-Fu-Yin, and the potential antioxidant compounds in Qi-Fu-Yin remain uncertain.

Objectives: The aim of this study is to identify the potential antioxidant compounds of Qi-Fu-Yin and establish quality control standards for Qi-Fu-Yin.

Methods: High-performance liquid chromatography was used to establish and quantify the fingerprints of Qi-Fu-Yin from various batches. Ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF/MS) was used to identify the common peaks. Bivariate correlation analysis, partial least squares regression analysis, and gray correlation analysis were used to establish the spectrum-effect relationship.

Results: Forty-nine common peaks were determined through the establishment of fingerprints. Among them, 35 common peaks were preliminarily characterized. The multiple statistical correlation analysis methods identified six compounds as potential antioxidant constituents of Qi-Fu-Yin, and their antioxidant activities were validated in vitro. All six antioxidant compounds derived from two herbs. Therefore, three chemical index compounds derived from other three herbs were added to the quantitative analysis, while for two herbs, no peaks could be included. Eventually, six antioxidant constituents and three index compounds were quantitatively determined to provide a relatively comprehensive quality control for Qi-Fu-Yin.

Conclusions: The study elucidated the antioxidant substance basis of Qi-Fu-Yin and provided a relatively comprehensive approach for the assay of Qi-Fu-Yin, which is a promising advance in the quality control of Qi-Fu-Yin.

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基于多重统计相关性分析的芪茯阴高效液相色谱指纹图谱与抗氧化活性之间的谱效关系
简介芪茯阴在中国被用于治疗阿尔茨海默病(AD)。氧化应激已被认为是导致阿尔茨海默病进展的一个因素。迄今为止,还没有一种质量控制方法来确保芪茯阴批次间的一致性,芪茯阴中潜在的抗氧化化合物仍不确定:方法:采用高效液相色谱法建立和量化不同批次祁福银的指纹图谱。采用超高效液相色谱-四极杆飞行时间串联质谱(UHPLC-Q-TOF/MS)鉴定常见峰。采用二元相关分析、偏最小二乘回归分析和灰色关联分析来确定谱效关系:结果:通过建立指纹图谱确定了 49 个常见峰。结果:通过建立指纹图谱确定了 49 个共同峰,其中 35 个共同峰被初步定性。通过多重统计相关分析方法,确定了六种化合物为祁福银的潜在抗氧化成分,并对其抗氧化活性进行了体外验证。这六种抗氧化化合物均来自两种药材。因此,在定量分析中加入了另外三味药材中的三个化学指标化合物,但有两味药材的指标化合物没有峰值。最终,六种抗氧化成分和三种指标化合物被定量测定,为芪茯银的质量控制提供了相对全面的依据:该研究阐明了芪茯阴的抗氧化物质基础,为芪茯阴的检测提供了一种相对全面的方法,在芪茯阴的质量控制方面取得了可喜的进步。
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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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