Lifei Hu, Fengxiao Zhu, Yifan Wang, Tao Wu, Xin Wu, Zhian Huang, Daihua Sun, Mingxing Liu
{"title":"Comparison and chemometrics analysis of phenolic compounds and mineral elements in Artemisia Argyi Folium from different geographical origins.","authors":"Lifei Hu, Fengxiao Zhu, Yifan Wang, Tao Wu, Xin Wu, Zhian Huang, Daihua Sun, Mingxing Liu","doi":"10.1016/j.fochx.2024.101909","DOIUrl":null,"url":null,"abstract":"<p><p>The quality of Artemisia Argyi Folium (AAF), a traditional Chinese food ingredient, is intrinsically linked to its geographical origin, which this study explores through phenolic compounds and mineral elements. The contents of 17 phenols and 18 minerals differed significantly between geographically distinct samples according to UHPLC and ICP-MS, respectively. Chemometrics indicated that a supervised model, orthogonal partial least squares discriminant analysis (OPLS-DA), outperformed unsupervised methods at classifying AAF samples by their origins. Phenols were more effective at distinguishing samples from seven provinces, while minerals were adept at differentiating samples from the Dabie Mountain region (three provinces) and those from four other provinces. Six phenols and 10 minerals were important variables for discrimination. Complex correlations were observed between the contents of various phenols and minerals in AAF, with minerals possibly affecting the accumulation of phenols. This study provides an approach for distinguishing geographically distinct AAF samples and determining their geographical origins.</p>","PeriodicalId":12334,"journal":{"name":"Food Chemistry: X","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11533654/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Chemistry: X","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1016/j.fochx.2024.101909","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/30 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
The quality of Artemisia Argyi Folium (AAF), a traditional Chinese food ingredient, is intrinsically linked to its geographical origin, which this study explores through phenolic compounds and mineral elements. The contents of 17 phenols and 18 minerals differed significantly between geographically distinct samples according to UHPLC and ICP-MS, respectively. Chemometrics indicated that a supervised model, orthogonal partial least squares discriminant analysis (OPLS-DA), outperformed unsupervised methods at classifying AAF samples by their origins. Phenols were more effective at distinguishing samples from seven provinces, while minerals were adept at differentiating samples from the Dabie Mountain region (three provinces) and those from four other provinces. Six phenols and 10 minerals were important variables for discrimination. Complex correlations were observed between the contents of various phenols and minerals in AAF, with minerals possibly affecting the accumulation of phenols. This study provides an approach for distinguishing geographically distinct AAF samples and determining their geographical origins.
期刊介绍:
Food Chemistry: X, one of three Open Access companion journals to Food Chemistry, follows the same aims, scope, and peer-review process. It focuses on papers advancing food and biochemistry or analytical methods, prioritizing research novelty. Manuscript evaluation considers novelty, scientific rigor, field advancement, and reader interest. Excluded are studies on food molecular sciences or disease cure/prevention. Topics include food component chemistry, bioactives, processing effects, additives, contaminants, and analytical methods. The journal welcome Analytical Papers addressing food microbiology, sensory aspects, and more, emphasizing new methods with robust validation and applicability to diverse foods or regions.