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Chemometrics and chromatographic fingerprints to discriminate and classify counterfeit medicines containing PDE-5 inhibitors

机译:化学计量学和色谱指纹图谱,用于鉴别和分类含有PDE-5抑制剂的假药

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摘要

Chromatographic fingerprints recorded for a set of genuine and counterfeit samples of Viagra ? and Cialis ? were evaluated for their use in the detection and classification of counterfeit samples of these groups of medicines. Therefore several exploratory chemometric techniques were applied to reveal structures in the data sets as well as differences among the samples. The focus was on the differentiation between genuine and counterfeit samples and on the differences between the samples of the different classes of counterfeits as defined by the Dutch National Institute for Public Health and the Environment (RIVM). In a second part the revealed differences between the samples were modelled to obtain a predictive model for both the differentiation between genuine and counterfeit samples as well as the classification of the counterfeit samples. The exploratory analysis clearly revealed differences in the data for the genuine and the counterfeit samples and with projection pursuit and hierarchical clustering differences among the different groups of counterfeits could be revealed, especially for the Viagra ? data set. For both data sets predictive models were obtained with 100 correct classification rates for the differentiation between genuine and counterfeit medicines and high correct classification rates for the classification in the different classes of counterfeit medicines. For both data sets the best performing models were obtained with Least Square-Support Vector Machines (LS-SVM) and Soft Independent Modelling by Class Analogy (SIMCA).
机译:一套真实和假冒伟哥样品的色谱指纹图谱?和Cialis?评估了它们在检测和分类这些药物假冒样品中的用途。因此,采用了几种探索性化学计量技术来揭示数据集中的结构以及样品之间的差异。重点是真伪样品之间的区别,以及荷兰国家公共卫生与环境研究所(RIVM)定义的不同类别假冒样品之间的差异。在第二部分中,对样本之间揭示的差异进行建模,以获得真实和假冒样本之间的区分以及假冒样本分类的预测模型。探索性分析清楚地揭示了真实和假冒样品数据的差异,以及通过投影追踪和等级聚类可以发现不同伪造品组之间的差异,特别是对于伟哥?数据集。对于这两个数据集,获得的预测模型具有100种正确的分类率,可以区分真假药品,不同分类的假药具有很高的正确分类率。对于这两个数据集,使用最小二乘支持向量机(LS-SVM)和类比法软独立建模(SIMCA)获得了性能最佳的模型。

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