Discrimination of Citri reticulatae pericarpium (CP) and Citrus reticulata 'chachi' (GCP): Focus on HPTLC, UHPLC techniques combined with machine learning and content differences of three specific flavonoids.

The global consumption of Citri reticulatae pericarpium (Chenpi, CP) and Citrus reticulata 'chachi' (Guangchenpi, GCP) has been experiencing a steady increase, driven by its extensive applications in healthcare, flavoring, and therapeutic fields. While the two varieties exhibit analogous macroscopic characteristics and therapeutic efficacies, rigorous market differentiation and identification are imperative owing to their distinct botanical origins, qualitative specifications, and especially significant price discrepancy. A comprehensive strategy integrating HPTLC and UHPLC analyses with machine learning was implemented in this research to discriminate between CP and GCP and to conduct a comparative content analysis of three characteristic flavonoids in the two materials. The discrimination results of eight models upon HPTLC data reveal that all models except Tree achieved 100% discrimination accuracy. (2S)-Narirutin, Nobiletin, and Tangeretin, three compounds contributed to the differentiation were quantified and exhibited distinct content differences in CP and GCP samples. This study could provide simple and scientific guidance for the authentication of the two varieties.