F. Hoffmann-La Roche Ltd., Basel, Switzerland; Institute of Forensic Science, School of Criminal Sciences, University of Lausanne, Switzerland.
Forensic Sci Int. 2011 Sep 10;211(1-3):83-100. doi: 10.1016/j.forsciint.2011.04.023. Epub 2011 May 26.
Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results. The present study offers a methodology allowing to provide more valuable information for organisations engaged in the fight against counterfeiting of medicines. A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers. The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.
在过去的十年中，假冒药品已成为一个普遍存在的问题。已经应用了各种分析技术来区分正品和假冒产品。其中，近红外（NIR）和拉曼光谱提供了有希望的结果。本研究提供了一种方法，允许为参与打击药品伪造的组织提供更有价值的信息。通过使用近红外（NIR）和拉曼光谱分析特定药品的假冒产品来建立数据库。实施了无监督化学计量技术（即主成分分析（PCA）和层次聚类分析（HCA））以识别数据集中的类别。气相色谱法与质谱法（GC-MS）和傅里叶变换红外光谱法（FT-IR）用于确定假冒产品中不同化学图谱的数量。与 NIR 和拉曼光谱建立的类别进行比较，评估了这些技术的区分能力。监督分类器（即 k-最近邻，偏最小二乘判别分析，概率神经网络和对传人工神经网络）应用于获得的 NIR 和拉曼光谱，将结果与无监督分类器提供的结果进行比较。基于 NIR 和拉曼光谱识别的类别的保留策略用于常规应用，使用基于距离度量和接收器操作特性（ROC）曲线的分类算法。该模型能够将新假冒产品的光谱与先前分析产品的光谱进行比较，并确定新样本是否属于现有类别之一，从而可以与数据库中的其他假冒产品建立联系。
