Analytical Strategies for Quality Control of Herbal Medicines

Herbal medicines encompass complex mixtures of phytochemicals whose composition can vary with species, geography and processing. Ensuring consistent quality, safety and efficacy requires integrated analytical strategies. Chromatographic fingerprinting—using techniques such as high-performance liquid chromatography (HPLC), ultra-performance liquid chromatography (UPLC) or thin-layer chromatography (TLC)—provides reproducible chemical profiles that serve as barcodes for batch comparison. Hyphenated methods, notably liquid chromatography–mass spectrometry (LC-MS), enable the detection and structural elucidation of key markers. Chemometric tools, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), extract meaningful patterns from multidimensional data, facilitating batch classification and the identification of characteristic peaks. Coupling chemical profiles with bioactivity assays (for example antioxidant or antimicrobial tests) links composition to pharmacological function. Emerging machine learning algorithms further advance species authentication and adulterant detection by modelling high-dimensional spectral data. Stability studies under controlled temperature and pH conditions elucidate degradation pathways, guiding formulation and storage. Together, these approaches underpin international regulatory frameworks and support the reliable supply of herbal therapies in global healthcare.

Research from Nature Portfolio

Recent studies have combined chromatographic fingerprinting with functional assays to pinpoint active constituents and to authenticate raw materials. In one seminal work, high-performance liquid chromatography with diode array detection was used to generate fingerprints of multiple batches of Salvia miltiorrhizae, while microcalorimetric measurements of Pseudomonas aeruginosa growth provided an antibacterial activity profile. Multivariate clustering and regression analyses identified protocatechualdehyde and salvianolic acid B as major contributors to inhibitory effects, offering concrete markers for quality assessment. In another development, liquid chromatography–mass spectrometry datasets from over seventy medicinal plant extracts were analysed using advanced machine learning techniques, including Bayesian networks and autoencoder-based dimensionality reduction, to classify species with high accuracy. This approach demonstrated robustness against variations in extraction methods and instrumentation, highlighting the potential for rapid, data-driven authentication of herbal materials.

Analytical Strategies for Quality Control of Herbal Medicines publication trend

The graph below shows the total number of articles in analytical strategies for quality control of herbal medicines across all publications each year (not limited to Nature Index journals).

Technical terms

Chromatographic fingerprint: A characteristic chromatogram representing the profile of chemical constituents in an herbal sample, used as a benchmark for quality comparison.

Chemometrics: The application of statistical and mathematical methods to interpret complex chemical data and reveal underlying patterns.

Principal component analysis (PCA): A dimensionality-reduction technique that transforms correlated variables into a set of uncorrelated principal components to highlight variation in datasets.

Orthogonal partial least squares-discriminant analysis (OPLS-DA): A supervised multivariate method that separates predictive variation from orthogonal noise to improve classification of sample groups.

High-performance liquid chromatography (HPLC): A separation technique that passes a liquid sample through a column to resolve compounds based on their interactions with the stationary phase.

Liquid chromatography–mass spectrometry (LC-MS): A hyphenated method combining chromatographic separation with mass analysis for the detection and identification of molecular species.

Microcalorimetry: A technique measuring heat flow associated with biological or chemical processes, used to quantify activities such as microbial growth or enzyme inhibition.

References

1. HPLC Fingerprint Analysis of Cibotii rhizoma from Different Regions and Identification of Common Peaks by LC-MS. Pharmaceuticals (2024).
2. Holistic Evaluation of Quality Consistency of Ixeris sonchifolia (Bunge) Hance Injectables by Quantitative Fingerprinting in Combination with Antioxidant Activity and Chemometric Methods. PLOS ONE (2016).
3. Combination of chemical fingerprint and bioactivity evaluation to explore the antibacterial components of Salvia miltiorrhizae. Scientific Reports (2017).
4. Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task. Scientific Reports (2018).
5. The Potential Use of Herbal Fingerprints by Means of HPLC and TLC for Characterization and Identification of Herbal Extracts and the Distinction of Latvian Native Medicinal Plants. Molecules (2022).