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首页> 外文期刊>International journal of analytical chemistry >Ultraviolet-Visible Spectroscopy and Chemometric Strategy Enable the Classification and Detection of Expired Antimalarial Herbal Medicinal Product in Ghana
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Ultraviolet-Visible Spectroscopy and Chemometric Strategy Enable the Classification and Detection of Expired Antimalarial Herbal Medicinal Product in Ghana

机译:紫外线可见光谱和化学计量策略使加纳的过期的抗疟药药品进行分类和检测

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To meet the growing demand for complementary and alternative treatment for malaria, manufacturers produce several antimalarial herbal medicinal products. Herbal medicinal products regulation is difficult due to their complex chemical nature, requiring cumbersome, expensive, and time-consuming methods of analysis. The aim of this study was to develop a simple spectroscopic method together with a chemometric model for the classification and the identification of expired liquid antimalarial herbal medicinal products. Principal component analysis model was successfully used to distinguish between different herbal medicinal products and identify expired products. Principal component analysis showed a clear class separation between all five herbal medicinal products (HMP) studied, with explained variance for first and second principal components as 37.51% and 26.38%, respectively, while the third principal component had 18.74%. Support vector machine classification gave specificity and accuracy of 1.00 (100%) for training set data for all the products. The validation set HMP1, HMP2, and HMP3 had sensitivity, specificity, and accuracy of 1.00. HMP4 and HMP5 had sensitivity and specificity of 0.90 and 1.00, respectively, and an accuracy of 0.98. The support vector machine classification and principal component analysis models were successfully used to identify expired herbal medicinal products. This strategy can be used for rapid field detection of expired liquid antimalarial herbal medicinal products.
机译:为了满足对疟疾互补和替代治疗的需求不断增长,制造商生产几种抗疟药草药产品。由于其复杂的化学性质,草药药品调节难以困难,需要麻烦,昂贵,耗时的分析方法。该研究的目的是与用于分类的化学计量模型和过期的液体抗疟药草药产品的化学计量模型一起开发一种简单的光谱方法。主要成分分析模型已成功地用于区分不同的草药产品并鉴定过期产品。主成分分析显示,所有五种草药产品(HMP)之间的透明类别分离,第一个和第二主成分的解释差异分别为37.51%和26.38%,而第三个主要成分具有18.74%。支持向量机分类对所有产品的培训设置数据提供1.00(100%)的特异性和准确性。验证集HMP1,HMP2和HMP3具有灵敏度,特异性和精度为1.00。 HMP4和HMP5分别具有0.90和1.00的灵敏度和特异性,精度为0.98。支持向量机分类和主要成分分析模型已成功用于识别过期的草药产品。该策略可用于快速现场检测过期的液体抗疟药草药药品。

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