Scholarlink

<p>Medicinal plants represent a key reservoir of bioactive compounds relevant to pharmaceutical and nutraceutical development. In this study, differential pulse voltammetry (DPV) coupled with chemometric analysis was employed for the classification of 89 samples belonging to 34 Algerian medicinal plant species. Electrochemical fingerprints were recorded within −0.1–1.5&#xa0;V versus Ag/AgCl and processed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). The first two principal components (PC1 = 34.1%, PC2 = 28.6%) explained 62.7% of the total variance, clearly discriminating plant families such as Fabaceae, Asteraceae, and Chenopodiaceae. Outliers identified through Hotelling’s T<sup>2</sup> ellipse were consistent with minor variations in extract composition. The clustering agreement between PCA and HCA exceeded 85%, confirming the robustness of the chemometric model. The method enabled the identification of characteristic electroactive compounds, mainly flavonoids and phenolic acids-responsible for distinctive voltammetric responses. This DPV-chemometrics approach provides a rapid (&lt; 10&#xa0;min per sample), low-cost, and environmentally friendly tool for the authentication and quality control of medicinal plants and offers a reliable framework for future machine-learning-based phytochemical screening.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of chemometric techniques to differential pulse voltammetry for Algerian medicinal plants classification

  • 期刊论文

  • 发表日期：

    2026年2月14日

查看全文
  • Siham Tei,
  • Abdelkrim Rebiai,
  • Bachir Ben Seghir,
  • Hadia Hemmami,
  • Hafidha Terea,
  • Soumeia Zeghoud

摘要

Medicinal plants represent a key reservoir of bioactive compounds relevant to pharmaceutical and nutraceutical development. In this study, differential pulse voltammetry (DPV) coupled with chemometric analysis was employed for the classification of 89 samples belonging to 34 Algerian medicinal plant species. Electrochemical fingerprints were recorded within −0.1–1.5 V versus Ag/AgCl and processed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). The first two principal components (PC1 = 34.1%, PC2 = 28.6%) explained 62.7% of the total variance, clearly discriminating plant families such as Fabaceae, Asteraceae, and Chenopodiaceae. Outliers identified through Hotelling’s T2 ellipse were consistent with minor variations in extract composition. The clustering agreement between PCA and HCA exceeded 85%, confirming the robustness of the chemometric model. The method enabled the identification of characteristic electroactive compounds, mainly flavonoids and phenolic acids-responsible for distinctive voltammetric responses. This DPV-chemometrics approach provides a rapid (< 10 min per sample), low-cost, and environmentally friendly tool for the authentication and quality control of medicinal plants and offers a reliable framework for future machine-learning-based phytochemical screening.

Graphical abstract