Abstract
Owing to the high pharmacological relevance and multidimensional quality attributes of Panax spp., accurate authentication and quality evaluation of Panax-derived herbal materials remain challenging within traditional Chinese medicine (TCM) quality control systems. Conventional approaches often face trade-offs among analysis speed and throughput, non-destructive measurement, and analytical accuracy, which can limit their suitability for modern, large-scale quality control. This review summarizes recent advances in vibrational and related analytical techniques-infrared (IR) and near-infrared (NIR) spectroscopy, Raman spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging (HSI), and nuclear magnetic resonance (NMR)-for authentication and quality evaluation of Panax materials. We compare the capabilities of each modality in supporting key tasks, including species authentication, geographical origin tracing, age/cultivation-stage discrimination, and quantitative assessment of major chemical markers, with emphasis on the underlying measurement principles. In general, NIR and HSI are well suited to rapid, high-throughput screening of bulk samples, whereas Raman and NMR provide higher chemical specificity for molecular and structural characterization. To mitigate limitations of single-modality analysis, this review discusses a methodological shift from conventional spectral fingerprinting and chemometric approaches toward model-driven, data-enabled sensing strategies for robust quality evaluation. Specifically, we highlight multimodal data fusion frameworks combined with interpretable machine-learning/deep-learning methods to build robust classification and regression models for quality assessment. This perspective aims to support standardized and scalable authentication and quality evaluation of Panax herbal materials and to facilitate the digitization of quality control workflows for Chinese herbal medicines.