An Integrated Approach to Identify the Q‐Markers of Banxia‐Houpo Decoction Based on Nontargeted Multicomponent Profiling, Network Pharmacology, and Chemometrics

ABSTRACT Introduction The inherent complexity of traditional Chinese medicine (TCM) poses significant challenges in directly correlating quality evaluation with clinical efficacy. Banxia‐Houpo Decoction (BHD), a classical TCM formula, has demonstrated efficacy in treating globus hystericus. However, the intricate composition of BHD, which contains both volatile and non‐volatile active components, complicates efforts to ensure its consistent quality and clinical effectiveness. Objective The aim of this study was to introduce an integrated approach that combines non‐targeted multicomponent analysis, network pharmacology, and multivariate chemometrics to identify quality markers for the effective quality control of BHD. Materials and Methods First, a nontargeted high‐definition MSE method based on ultraperformance liquid chromatography–quadrupole time‐of‐flight–mass spectrometry (UHPLC‐QTOF‐MS) was developed for the comprehensive multi‐component characterization of BHD. Next, the quality markers of nonvolatile compounds in BHD were identified through network pharmacology analysis. Subsequently, volatile organic compounds (VOCs) in BHD samples were analyzed via headspace solid‐phase microextraction–gas chromatography–mass spectrometry (HS‐SPME‐GC‐MS) and headspace gas chromatography–ion mobility spectrometry (HS‐GC‐IMS). Finally, the orthogonal partial least squares discriminant analysis (OPLS‐DA) model was applied to screen for potential markers. Results Based on in‐house library‐driven automated peak annotation and comparison with 25 reference compounds, 128 components were identified for the first time. Additionally, honokiol, magnolol, magnoflorine, 6‐gingerol, rosmarinic acid, and adenosine were preliminarily identified as potential quality markers for BHD through network pharmacology analysis. By employing two complementary techniques, HS‐SPME‐GC‐MS and HS‐GC‐IMS, a total of 145 volatile compounds was identified in the BHD samples. Four potential differential VOCs in the BHD samples were further identified based on the variable importance in projection (VIP ≥ 1.5) using HS‐GC‐IMS combined with chemometric analysis. Conclusion In conclusion, this study not only contributes to establishing quality standards for BHD but also offers new insights into quality assessment and identification in the development of classical formulations enriched with volatile components.