{"title":"基于指纹分析、化学模式识别和定量分析的扶芳神气口服液质量标记。","authors":"Yingqi Zhang, Yangling Li, Yanwei Cheng, Huiling Nan, Yuqiang Wu, Hongtao Chen, Xuejian Li, Yudong Luo, Anqiang Tan, Qing Chen","doi":"10.1002/pca.3489","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Fufang Shenqi Oral Liquid (FFSQOL) is an important Chinese medicine compound preparation with a wide range of clinical applications, which is mainly used to regulate immune function, improve cardiovascular function, and have anti-inflammatory and antibacterial effects. At present, it is of great importance to establish the quality evaluation method of FFSQOL and to investigate its quality markers (Q-markers).</p><p><strong>Objectives: </strong>The aim of this study is to establish a quality evaluation method for FFSQOL and screen its Q-markers to provide a scientific basis for its quality control.</p><p><strong>Methods: </strong>Fourteen batches of FFSQOL were subjected to high-performance liquid chromatography (HPLC) fingerprint and similarity analysis. The components of FFSQOL were identified, and their content was determined. This was combined with cluster analysis (CA) and principal component analysis (PCA) to determine the Q-markers of FFSQOL.</p><p><strong>Results: </strong>In this study, an HPLC fingerprint was established for 14 batches of FFSQOL, identifying 12 common peaks and six major components. Four components were identified as stable and reproducible: gallic acid (504.94 ~ 1219.04 μg/mL), caffeic acid (452.15 ~ 783.01 μg/mL), 7-O-glucoside (1097.72 ~ 2440.41 μg/mL), and formononetin (176.2 ~ 177.51 μg/mL). Quality evaluation of the 14 batches was conducted using chemical pattern recognition analysis. CA results indicated two distinct groups, and PCA revealed that principal components 1 and 2 were the main factors influencing batch differences. A combination of HPLC fingerprint, content determination results, and chemical pattern recognition analysis was employed to identify Q-markers for FFSQOL. The markers identified were gallic acid, caffeic acid, calycosin 7-O-glucoside, and formononetin.</p><p><strong>Conclusion: </strong>In this study, a quality evaluation method for FFSQOL was established through the implementation of fingerprint, content determination, and chemical pattern recognition analysis, resulting in the identification of four Q-Markers of FFSQOL, which laid the foundation for the formulation of FFSQOL quality standards.</p>","PeriodicalId":20095,"journal":{"name":"Phytochemical Analysis","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality Markers of Fufang Shenqi Oral Liquid Based on Integrated Fingerprint Analysis, Chemical Pattern Recognition, and Quantification.\",\"authors\":\"Yingqi Zhang, Yangling Li, Yanwei Cheng, Huiling Nan, Yuqiang Wu, Hongtao Chen, Xuejian Li, Yudong Luo, Anqiang Tan, Qing Chen\",\"doi\":\"10.1002/pca.3489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Fufang Shenqi Oral Liquid (FFSQOL) is an important Chinese medicine compound preparation with a wide range of clinical applications, which is mainly used to regulate immune function, improve cardiovascular function, and have anti-inflammatory and antibacterial effects. At present, it is of great importance to establish the quality evaluation method of FFSQOL and to investigate its quality markers (Q-markers).</p><p><strong>Objectives: </strong>The aim of this study is to establish a quality evaluation method for FFSQOL and screen its Q-markers to provide a scientific basis for its quality control.</p><p><strong>Methods: </strong>Fourteen batches of FFSQOL were subjected to high-performance liquid chromatography (HPLC) fingerprint and similarity analysis. The components of FFSQOL were identified, and their content was determined. This was combined with cluster analysis (CA) and principal component analysis (PCA) to determine the Q-markers of FFSQOL.</p><p><strong>Results: </strong>In this study, an HPLC fingerprint was established for 14 batches of FFSQOL, identifying 12 common peaks and six major components. Four components were identified as stable and reproducible: gallic acid (504.94 ~ 1219.04 μg/mL), caffeic acid (452.15 ~ 783.01 μg/mL), 7-O-glucoside (1097.72 ~ 2440.41 μg/mL), and formononetin (176.2 ~ 177.51 μg/mL). Quality evaluation of the 14 batches was conducted using chemical pattern recognition analysis. CA results indicated two distinct groups, and PCA revealed that principal components 1 and 2 were the main factors influencing batch differences. A combination of HPLC fingerprint, content determination results, and chemical pattern recognition analysis was employed to identify Q-markers for FFSQOL. The markers identified were gallic acid, caffeic acid, calycosin 7-O-glucoside, and formononetin.</p><p><strong>Conclusion: </strong>In this study, a quality evaluation method for FFSQOL was established through the implementation of fingerprint, content determination, and chemical pattern recognition analysis, resulting in the identification of four Q-Markers of FFSQOL, which laid the foundation for the formulation of FFSQOL quality standards.</p>\",\"PeriodicalId\":20095,\"journal\":{\"name\":\"Phytochemical Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Phytochemical Analysis\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/pca.3489\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Phytochemical Analysis","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/pca.3489","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
摘要
简介:复方参芪口服液(FFSQOL)是一种重要的中药复方制剂,临床应用广泛,主要用于调节免疫功能,改善心血管功能,具有抗炎、抗菌作用。目前,建立FFSQOL的质量评价方法,研究其质量标记(q -marker)具有重要意义。前言:目的:建立白芪散的质量评价方法,筛选其q标记物,为其质量控制提供科学依据。方法:对14批药材进行高效液相色谱指纹图谱和相似度分析。对其成分进行鉴定,并测定其含量。结合聚类分析(CA)和主成分分析(PCA)确定FFSQOL的q标记。结果:本研究建立了14批白藜芦醇的HPLC指纹图谱,鉴定了12个共同峰和6个主要成分。结果表明:没食子酸(504.94 ~ 1219.04 μg/mL)、咖啡酸(452.15 ~ 783.01 μg/mL)、7- o -葡萄糖苷(1097.72 ~ 2440.41 μg/mL)、刺芒柄花素(176.2 ~ 177.51 μg/mL)具有稳定的重复性。采用化学模式识别法对14批样品进行质量评价。主成分分析表明,主成分1和主成分2是影响批次差异的主要因素。采用HPLC指纹图谱、含量测定结果和化学模式识别分析相结合的方法,对FFSQOL进行q标记鉴定。鉴定的标记物为没食子酸、咖啡酸、毛蕊花素7- o -葡萄糖苷和刺芒柄花素。结论:本研究通过实施指纹图谱、含量测定、化学模式识别分析,建立了一套FFSQOL的质量评价方法,鉴定出了FFSQOL的4个q标记,为制定FFSQOL质量标准奠定了基础。
Quality Markers of Fufang Shenqi Oral Liquid Based on Integrated Fingerprint Analysis, Chemical Pattern Recognition, and Quantification.
Introduction: Fufang Shenqi Oral Liquid (FFSQOL) is an important Chinese medicine compound preparation with a wide range of clinical applications, which is mainly used to regulate immune function, improve cardiovascular function, and have anti-inflammatory and antibacterial effects. At present, it is of great importance to establish the quality evaluation method of FFSQOL and to investigate its quality markers (Q-markers).
Objectives: The aim of this study is to establish a quality evaluation method for FFSQOL and screen its Q-markers to provide a scientific basis for its quality control.
Methods: Fourteen batches of FFSQOL were subjected to high-performance liquid chromatography (HPLC) fingerprint and similarity analysis. The components of FFSQOL were identified, and their content was determined. This was combined with cluster analysis (CA) and principal component analysis (PCA) to determine the Q-markers of FFSQOL.
Results: In this study, an HPLC fingerprint was established for 14 batches of FFSQOL, identifying 12 common peaks and six major components. Four components were identified as stable and reproducible: gallic acid (504.94 ~ 1219.04 μg/mL), caffeic acid (452.15 ~ 783.01 μg/mL), 7-O-glucoside (1097.72 ~ 2440.41 μg/mL), and formononetin (176.2 ~ 177.51 μg/mL). Quality evaluation of the 14 batches was conducted using chemical pattern recognition analysis. CA results indicated two distinct groups, and PCA revealed that principal components 1 and 2 were the main factors influencing batch differences. A combination of HPLC fingerprint, content determination results, and chemical pattern recognition analysis was employed to identify Q-markers for FFSQOL. The markers identified were gallic acid, caffeic acid, calycosin 7-O-glucoside, and formononetin.
Conclusion: In this study, a quality evaluation method for FFSQOL was established through the implementation of fingerprint, content determination, and chemical pattern recognition analysis, resulting in the identification of four Q-Markers of FFSQOL, which laid the foundation for the formulation of FFSQOL quality standards.
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.