Abstract
Objectives The determination of chemical components is usually used in the quality control of propolis. However, chemical components from different types of propolis are similar. The objective of this investigation was to establish a method based on a specific chemical fingerprint profile and a multivariate mixed model statistical analysis which could easily distinguish propolis of different origins and promote the quality control of propolis. Methods A novel approach using high performance liquid chromatography (HPLC) coupled with multivariate statistical analysis was established for profiling and distinguishing Chinese and Brazilian green propolis. A batch of 22 propolis samples was analyzed, and the datasets on retention time, peak area and sample codes were subjected to mixed multivariate statistical analysis consisting of principal component analysis (PCA) and a self-organization mapping net (SOM). Results The fingerprints were profiled. PCA score plots showed Chinese and Brazilian green propolis clearly classified into two groups. The visualized SOM results showed data from the two groups projected to the adjacent neurons clearly separated from each other. Artepillin C, which contributed greatly to the differentiation, was screened out and identified as the reference compound. Artepillin C is the characteristic component in Brazilian propolis which can be used as chemical marker to distinguish propolis of different origins. Conclusions In this study, fingerprints coupled with multivariate statistical analysis have been successfully applied to distinguish Chinese from Brazilian green propolis. The research identified a chemical marker, and thus helps to investigate and promote the quality control of propolis.
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