Comprehensive HPLC fingerprint analysis based on a two-step extraction method for quality evaluation of (L.) Britt

• Published in: Analytical methods Vol. 15; no. 15; pp. 1888 - 1895
• Main Authors: Zhou, Guanghao, Dai, Yingping, Ge, Dandan, Yang, Jie, Fu, Qing, Jin, Yu, Liang, Xinmiao
• Format: Journal Article
• Published: 13.04.2023
• DOI: 10.1039/d3ay00172e
• ISSN: 1759-9660

Abundant chemical components are key to ensure the evaluation accuracy of fingerprint analysis of traditional Chinese medicines (TCMs). A two-step extraction method combining supercritical fluid extraction (SFE) and water ultrasonic extraction was established for the quality evaluation of Perilla frutescens (L.) Britt. Weakly polar components were extracted under optimal SFE conditions (15% co-solvent (EtOH :  n -hexane = 1 : 14, (v/v)), 40 °C, 250 bar, and 30 min), and polar components were subsequently extracted by an ultrasonic step (100% water as solvent, 40 °C, and 45 min). Then, HPLC methods were established, which were validated to be accurate, stable, and reliable. In this work, 25 batches of samples were evaluated and the data were analysed by similarity analysis (SA) and hierarchical cluster analysis (HCA). The similarity values of SFE extracts and aqueous extracts were respectively 0.616-0.999, and 0.252-0.997, proving the importance of the extraction method for the accuracy of the subsequent fingerprint analysis results. For the HCA, 25 samples were divided into two categories (leaves and stems), among which four batches of leaves with less similarity were considered as stems, indicating that quality differences of P. frutescens depending on medicinal parts and origin exist. The two-step extraction method developed in this work has been proved to be suitable for the quality evaluation of TCMs with complex compositions. A two-step extraction method combining supercritical fluid extraction and water ultrasonic extraction was developed, and the quality evaluation of P. frutescens was completed by HPLC fingerprint analysis and hierarchical cluster analysis.