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

IF 2.5 4区 化学 Q3 CHEMISTRY, ANALYTICAL
Dorina Casoni, Simona Codruța Aurora Cobzac, Ileana Maria Simion
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引用次数: 0

Abstract

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.
紫外可见光谱与模式识别技术相结合鉴定不同地理区域药用植物材料的可行性
正确识别和鉴定药用植物材料是确保质量和防止掺假的关键任务。建议使用紫外可见光谱与主成分分析(PCA)和判别分析(DA)对不同属和不同产地的植物材料进行鉴定/认证。研究使用了从 7 个国家(罗马尼亚、北马其顿、德国、意大利、塞尔维亚、俄罗斯和哈萨克斯坦)采集的 12 种属植物样品的水醇提取物。提取物的紫外可见光谱在 200-800 纳米光谱范围内采集,并使用信号平滑法对光谱数据进行预处理。利用 1-Pearson r 距离测量法进行层次聚类分析(HCA),分别根据原始光谱和不同阶导数光谱对样品进行分类。用 PCA 方法对原始光谱和不同阶导数光谱的数据进行评估。利用带有方差旋转的 PCA 方法,首次发现了对样品分类有重要贡献的光谱范围。将 PCA 方法与 DA 结合应用于从原始光谱和四阶导数光谱中获得的数据时,样品被正确归入相应组别的准确率为 98.04%。所提出的方法可作为一种有用的工具,用于快速鉴定来自不同国家的植物材料。
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来源期刊
Journal of Analytical Science and Technology
Journal of Analytical Science and Technology Environmental Science-General Environmental Science
CiteScore
4.00
自引率
4.20%
发文量
39
审稿时长
13 weeks
期刊介绍: The Journal of Analytical Science and Technology (JAST) is a fully open access peer-reviewed scientific journal published under the brand SpringerOpen. JAST was launched by Korea Basic Science Institute in 2010. JAST publishes original research and review articles on all aspects of analytical principles, techniques, methods, procedures, and equipment. JAST’s vision is to be an internationally influential and widely read analytical science journal. Our mission is to inform and stimulate researchers to make significant professional achievements in science. We aim to provide scientists, researchers, and students worldwide with unlimited access to the latest advances of the analytical sciences.
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