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Detection of counterfeit Viagra(R) by Raman microspectroscopy imaging and multivariate analysis.

机译:通过拉曼光谱成像和多元分析检测伪造的伟哥。

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摘要

During the past years, pharmaceutical counterfeiting was mainly a problem of developing countries with weak enforcement and inspection programs. However, Europe and North America are more and more confronted with the counterfeiting problem. During this study, 26 counterfeits and imitations of Viagra(R) tablets and 8 genuine tablets of Viagra(R) were analysed by Raman microspectroscopy imaging. After unfolding the data, three maps are combined per sample and a first PCA is realised on these data. Then, the first principal components of each sample are assembled. The exploratory and classification analysis are performed on that matrix. PCA was applied as exploratory analysis tool on different spectral ranges to detect counterfeit medicines based on the full spectra (200-1800 cm(1)), the presence of lactose (830-880 cm(1)) and the spatial distribution of sildenafil (1200-1290 cm(1)) inside the tablet. After the exploratory analysis, three different classification algorithms were applied on the full spectra dataset: linear discriminant analysis, k-nearest neighbour and soft independent modelling of class analogy. PCA analysis of the 830-880 cm(1) spectral region discriminated genuine samples while the multivariate analysis of the spectral region between 1200 cm(1) and 1290 cm(1) returns no satisfactory results. A good discrimination of genuine samples was obtained with multivariate analysis of the full spectra region (200-1800 cm(1)). Application of the k-NN and SIMCA algorithm returned 100% correct classification during both internal and external validation.
机译:在过去的几年中,药品假冒主要是执法和检查计划薄弱的发展中国家的问题。但是,欧洲和北美越来越面临假冒问题。在这项研究中,通过拉曼光谱成像分析了26种仿冒品和仿制的Viagra®片剂和8种真正的Viagra®片剂。展开数据后,每个样本将合并三个映射,并在这些数据上实现第一个PCA。然后,组装每个样品的第一主要成分。探索性和分类分析在该矩阵上执行。 PCA作为探索性分析工具在不同光谱范围上应用,可根据全光谱(200-1800 cm(1)),乳糖的存在(830-880 cm(1))和西地那非(数位板内部1200-1290 cm(1))。在探索性分析之后,对全光谱数据集应用了三种不同的分类算法:线性判别分析,k最近邻和类比的软独立建模。 830-880 cm(1)光谱区域的PCA分析可区分真实样品,而1200 cm(1)和1290 cm(1)之间的光谱区域的多元分析未获得令人满意的结果。通过对整个光谱区域(200-1800 cm(1))进行多变量分析,可以很好地区分真实样品。在内部和外部验证期间,k-NN和SIMCA算法的应用返回了100%正确的分类。

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