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Quality control of traditional Chinese medicine solid preparation Zhizi Jinhua Pills via FT-IR, UV and THz fingerprints assisted with dissolution.

PubMed
Authors: Wang X, Zou K, Li P, Zhu W, Cui H, Xiao L, Lan L, Sun G

Year

2026

Paper ID

69103

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

225

Citations

N/A

Abstract

The chemical complexity and compositional diversity of traditional Chinese Medicine (TCM) and its proprietary preparations present substantial challenges to their quality control. Spectroscopic detection techniques represent a powerful tool for TCM quality assessment owing to their rapidity, non-destructiveness, and environmental friendliness. Nevertheless, spectral information overlap has long impeded effective qualitative and quantitative analysis. In this study, Fourier transform infrared (FT-IR), ultraviolet (UV), and terahertz (THz) spectroscopic techniques were employed to establish multi-dimensional spectral fingerprints for Zhizi Jinhua Pills (ZZJHPs). Original spectra were processed using the quantum fingerprinting method, and qualitative similarity (S) and quantitative similarity (P) were further evaluated via the systematically quantified fingerprint method. Based on the integrated tri-spectral fingerprint analysis, 32 batches of ZZJHP samples were categorized into three distinct quality grades. In addition, the overall dissolution behavior of ZZJHPs was systematically investigated using a full ultraviolet fingerprint (FUV-FP) strategy. The established FUV-FP method enabled rapid and accurate characterization of in vitro dissolution profiles. Among all samples, two batches exhibited relatively slow dissolution rates, whereas four batches displayed faster release behavior, which was sensitively distinguished by the quantitative similarity parameter P. This work provides an effective strategy for resolving spectral information overlap in TCM spectroscopic analysis. It also promotes the application of UV fingerprinting for holistic dissolution evaluation, thereby offering a reliable and practical technical approach for the quality control of TCM solid preparations.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • The chemical complexity and compositional diversity of traditional Chinese Medicine (TCM) and its proprietary preparations present substantial challenges to their quality control.

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