Feasibility of UV–Vis spectroscopy combined with pattern recognition techniques to authenticate the medicinal plant material from different geographical areas

• Published in: Journal of analytical science and technology 2024-12, Vol.15 (1), p.17-10, Article 17
• Main Authors: Casoni, Dorina, Cobzac, Simona Codruța Aurora, Simion, Ileana Maria
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Authentication; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Cluster analysis; Clustering; Derivative spectra; Discriminant analysis; Distance measurement; Herbal medicine; Medicinal plants; Monitoring/Environmental Analysis; Pattern recognition; Principal component analysis; Principal components analysis; Research Article; Spectra; Spectroscopy; Spectrum analysis; Ultraviolet radiation; UV–Vis spectroscopy
• ISSN: 2093-3371; 2093-3134; 2093-3371
• DOI: 10.1186/s40543-024-00428-2

The correct identification and authentication of medicinal plants material is a crucial task that ensures quality and prevent adulteration. The use of UV–Vis spectroscopy with principal component analysis (PCA) and discriminant analysis (DA) was proposed for identification/authentication of plant material form different genus and different geographical areas provenience. Hydroalcoholic extracts of samples from twelve genus collected from seven countries (Romania, North Macedonia, Germany, Italy, Serbia, Russia and Kazakhstan) were used. The UV–Vis spectra of the extracts were acquired in the 200–800 nm spectral range, and signal smoothing was used for pre-processing the spectral data. Hierarchical clustering analysis (HCA) with 1-Pearson r distance measurement was used to classify the samples based on the original spectra and different-order derivative spectra, respectively. Data from original spectra and from different-order derivative spectra were evaluated by PCA method. Using the PCA with varimax rotation approach, the spectral ranges with significant contribution for samples classification were revealed for the first time. When the PCA method coupled with DA was applied to the data obtained from the original spectra and the fourth-order derivative spectra, the samples were correctly classified to the respective groups with a 98.04% accuracy. The proposed method can be a useful tool for rapid authentication of plant material derived from different countries.