LC–MS-Based Metabolomics and Multivariate Analysis for Detecting Herbal Adulteration: A Global Literature Review

Abstrak

Herbal medicines are widely used worldwide, yet the risk of adulteration poses a serious challenge to their quality, safety, and efficacy. This review aims to summarize the state-of-the-art applications of liquid chromatography–mass spectrometry (LC–MS)-based metabolomics combined with multivariate analysis for detecting adulteration in medicinal plants. Relevant studies published between 2015 and 2025 were retrieved from Scopus and Google Scholar using keywords such as “LC–MS,” “metabolomics,” “herbal adulteration,” and “multivariate analysis.” The review highlights the workflow of untargeted and targeted metabolomics, data preprocessing pipelines, and commonly used multivariate statistical models such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Case studies on Panax ginseng, Echinacea spp., Curcuma longa, and other herbal species illustrate the capability of LC–MS metabolomics to differentiate authentic from adulterated samples. Challenges, including data standardization, metabolite annotation, and overfitting in chemometrics, are also discussed. This review underscores the critical role of LC–MS metabolomics as a robust and reproducible tool for herbal authentication and provides perspectives for improving regulatory frameworks and quality control in the global herbal medicine industry.