“UHPLC‐Q‐TOF/MS‐chemometrics‐network pharmacology” integrated strategy to discover quality markers of raw and stir‐fried Fructus Tribuli and process optimization of stir‐fried Fructus Tribuli

Abstract

IntroductionFructus Tribuli, the dried ripe fruit of Tribulus terrestris L., has various beneficial effects, including liver‐calming and depression‐relieving effects. Raw Fructus Tribuli (RFT) and stir‐fried Fructus Tribuli (SFT) are included in the Chinese Pharmacopoeia 2020 edition (Ch. P 2020). However, owing to the lack of specific regulations on SFT‐processing parameters in Ch. P 2020, it is difficult to ensure the quality of commercially available SFT.ObjectiveThe present study aimed to screen the quality markers (Q‐markers) of RFT and SFT and optimize the processing technology of SFT based on the identified Q‐markers.MethodsFirst, the ultra‐high‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry (UHPLC‐Q‐TOF/MS) technology as well as multiple statistical analysis along with network pharmacology was used to comprehensively identify the Q‐markers of RFT and SFT. Then, based on single‐factor experiments, the Box–Behnken design (BBD) response surface methodology (RSM) was used to optimize the processing technology of SFT and perform process validation.ResultsA total of 63 components were identified in RFT and SFT extracts. Terrestrosin D and Terrestrosin K were initially considered the Q‐markers of RFT and SFT, respectively. The optimum processing technology conditions were 208°C, 14 min, and 60 r·min−1. Three batches of process validation were performed, and the mean composite score was 56.87, with a relative standard deviation (RSD) value of 1.13%.ConclusionThe content of steroidal saponin components in RFT was significantly different before and after stir‐frying. Terrestrosin D and Terrestrosin K were validated as the Q‐markers of RFT and SFT, respectively. The identification of Q‐markers for RFT and SFT offered a clear index for optimizing the SFT‐processing technology and provided a basis for the quality control of RFT and SFT decoction pieces.

A multidimensional strategy for uncovering comprehensive quality markers of Scutellariae Radix based on UPLC-Q-TOF-MS analysis, artificial neural network, network pharmacology analysis, and molecular simulation

“…In order to further explore the differences among its internal components, a variety of artificial intelligence discrimination models have been constructed to objectively analyze the data obtained from the analysis, quantify the entire chromatogram information, and make it recognizable and processable by computers ( Wang et al., 2024 ). The results show that 48 batches of SR can be grouped into two major categories: KQ and ZQ, which have great differences in chemical compositions and are the primary factor affecting the quality of SR. Further analysis shows that the samples of KQ and ZQ can be further distinguished based on their origins, indicating that the origin is a secondary factor affecting their chemical compositions.…”