F. Hoffmann-La Roche Ltd., Basel, Switzerland.
Anal Chim Acta. 2011 Oct 31;705(1-2):334-41. doi: 10.1016/j.aca.2011.07.043. Epub 2011 Aug 5.
Raman spectroscopy combined with chemometrics has recently become a widespread technique for the analysis of pharmaceutical solid forms. The application presented in this paper is the investigation of counterfeit medicines. This increasingly serious issue involves networks that are an integral part of industrialized organized crime. Efficient analytical tools are consequently required to fight against it. Quick and reliable authentication means are needed to allow the deployment of measures from the company and the authorities. For this purpose a method in two steps has been implemented here. The first step enables the identification of pharmaceutical tablets and capsules and the detection of their counterfeits. A nonlinear classification method, the Support Vector Machines (SVM), is computed together with a correlation with the database and the detection of Active Pharmaceutical Ingredient (API) peaks in the suspect product. If a counterfeit is detected, the second step allows its chemical profiling among former counterfeits in a forensic intelligence perspective. For this second step a classification based on Principal Component Analysis (PCA) and correlation distance measurements is applied to the Raman spectra of the counterfeits.
拉曼光谱结合化学计量学最近已成为分析药物固态形式的广泛应用技术。本文介绍的应用是对假药的研究。这个日益严重的问题涉及到作为工业化有组织犯罪一部分的网络。因此，需要有效的分析工具来与之斗争。需要快速可靠的认证手段，以便公司和当局采取措施。为此，这里实施了两步法。第一步能够识别药物片剂和胶囊，并检测其假冒产品。计算非线性分类方法支持向量机（SVM），并与数据库相关联，以及可疑产品中活性药物成分（API）峰的检测。如果检测到假冒产品，则第二步允许从取证情报的角度对其进行化学分析。对于第二步，将基于主成分分析（PCA）和相关距离测量的分类应用于假冒产品的拉曼光谱。
