姜黄
主成分分析
计算生物学
掺假者
气相色谱法
化学
色谱法
生物
人工智能
计算机科学
植物
质谱法
作者
Bian‐Xia Xue,Tenukeguli Tuliebieke,Ting-Ting Zhang,Jia‐Xin Lai,Yao Rao,Lihua Zhang,Wenzhi Yang,Miaomiao Jiang,Yuefei Wang,Xiaoxuan Tian,Hong‐Hua Wu
标识
DOI:10.1016/j.microc.2024.110895
摘要
Curcumae pieces prepared in ready-to-use forms, consumed extensively for their significant medicinal and culinary benefits, present a challenge in discrimination due to their heterogeneous origins, analogous morphologies, and varied components. This study explored the metabolites of nine Curcumae pieces (NCPs) by Gas Chromatography-Mass Spectrometry (GC–MS) and Ultra-Performance Liquid Chromatography/Ultra High-Performance Liquid Chromatography–Quadrupole Time of Flight–Mass Spectrometry (UPLC/UHPLC–QTOF–MS) technologies, augmented by multivariate statistical analysis (Principal Component Analysis, PCA; Orthogonal Partial Least Squares Discriminant Analysis, OPLS–DA) and machine learning algorithms (Random Forest, RF). Twenty-six key differential markers in volatile and non-volatile metabolites each were found subsequently. Additionally, DNA metabarcoding was employed to develop specific Single Nucleotide Polymorphism (SNP) markers for the precise identification of Curcuma species in single and mixed samples, and for CRa even contained in Chinese patent prescriptions. The integration of these three methodologies facilitates a more reliable authentication of NCPs, enhancing the overall strategy for their global discrimination. This research contributes to the advancement of quality control and the development of functional foods or products derived from Curcumae pieces, tailored to meet specific health and dietary requirements.
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