UPLC指纹图谱结合化学计量学的多产地藜麦质量控制

采用超高效液相色谱(ultra performance liquid chromatography,UPLC)对来自世界范围多地区的30批次藜麦进行质量控制研究,通过方法学考察建立不同产地藜麦样品UPLC指纹图谱,并分析其相似度。借助聚类分析、主成分分析、偏最小二乘判别分析等化学计量学方法对其产地和质量的相关性进行分析。结果表明,30批藜麦对照图谱存在12个共有峰,样品相似度大于0.7;通过化学计量法,藜麦样品按产地被很好地分为4类,主成分分析和模式识别分析结果提示其质量差异主要与3个化合物相关。UPLC指纹图谱的构建和化学模式的识别为藜麦质量控制提供更全面的参考,为有效控制藜麦质量提供参考依据。

Application of UPLC Fingerprint Coupled with Chemometry for Quality Control of Quinoa from Different Geographical Origins

In this research, a method for the quality control of 30 batches of quinoa from many different areas of the world was established by ultra-performance liquid chromatography (UPLC). UPLC fingerprints were established after evaluation of figures of merit. The similarity was analyzed by similarity evaluation software. The relationship between geographical origin and quality was analyzed by cluster analysis, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and other chemometric methods. The results showed that there were 12 peaks common to 30 batches of quinoa, and the similarity among samples was greater than 0.7. Using the chemometric methods, the quinoa samples were classified into four categories according to their geographical orgins. The PCA and pattern recognition analysis indicated that the quality difference was mainly related to three compounds. Therefore, UPLC fingerprinting and chemical pattern recognition can provide detailed references for the quality control of quinoa.