世界中医药2024，Vol.19Issue(15)：2230-2236,7.DOI:10.3969/j.issn.1673-7202.2024.15.003
基于氨基酸及特征肽测定的不同产地鸡内金质量研究
Quality of Galli Gigerii Endothelium Corneum from Different Origins Based on the Determination of Amino Acids and Characteristic Peptides
摘要
Abstract
Objective:To establish the content determination method of amino acids and characteristic peptides in Galli Gigerii En-dothelium Corneum(GGEC)and evaluate the quality of GGEC from different origins.Methods:High-performance liquid chroma-tography(HPLC)was used to measure the content of 12 amino acids in GGEC,while ultra-high-performance liquid chromatogra-phy-tandem mass spectrometry(UHPLC-MS/MS)was employed to determine the levels of two characteristic peptides.Partial least squares discriminant analysis(PLS-DA)was used to analyze the results.Results:The relative standard deviation(RSD)for the to-tal content of the 12 amino acids in 18 batches of GGEC ranged from 2.84％to 14.01％.The RSD for the levels of the two charac-teristic peptides,Chicken-derived peptide Ⅰ and Chicken-derived peptide Ⅱ,were 47.13％and 14.62％,respectively,indicating some fluctuations.PLS-DA results showed that three batches of samples from Shanxi and three batches of samples from Liaoning were distinctly grouped into separate categories,while nine batches of samples from Shandong and three batches of samples from Zhejiang were grouped together.Conclusion:The established method for determining amino acids and characteristic peptides in GGEC effectively evaluate the quality of GGEC from different origins and can provide a reference for the quality assessment of GGEC and other animal-based medicinal materials.关键词
鸡内金/产地/氨基酸/特征肽/含量测定/偏最小二乘法分析/统计学/质量控制Key words
Galli Gigerii Endothelium Corneum/Origin/Amino acid/Characteristic peptide/Content determination/Partial least squares discriminant analysis/Statistics/Quality control分类
医药卫生引用本文复制引用
李国卫,孙冬梅,胡绮萍,童培珍,邱韵静,曾熙欣,郭嘉亮,潘礼业,何嘉莹,陈向东..基于氨基酸及特征肽测定的不同产地鸡内金质量研究[J].世界中医药,2024,19(15):2230-2236,7.基金项目
国家自然科学基金项目(82373835)——基于全生命周期分析策略的抗细菌生物膜剂高内涵筛选新方法研究 （82373835）
广东省基础与应用基础研究基金项目(2020B1515120033) （2020B1515120033）
