作者: Deborah Custers , Barbara Krakowska , Jacques O. De Beer , Patricia Courselle , Michal Daszykowski
DOI: 10.1007/S00216-015-9275-0
关键词: k-nearest neighbors algorithm 、 Counterfeit Drugs 、 Principal component analysis 、 European market 、 Detector 、 Chemometrics 、 Computer science 、 Counterfeit 、 Chromatography
摘要: Counterfeit medicines are a global threat to public health. High amounts enter the European market, which is why characterization of these products very important issue. In this study, high-performance liquid chromatography-photodiode array (HPLC-PDA) and chromatography-mass spectrometry (HPLC-MS) method were developed for analysis genuine Viagra®, generic counterfeit samples in order obtain different types fingerprints. These data included chemometric analysis, aiming test whether PDA MS complementary detection techniques. The comprise both MS1 MS2 fingerprints; consist fingerprints measured at three wavelengths, i.e., 254, 270, 290 nm, all possible combinations wavelengths. First, it was verified if groups can discriminate between genuine, generic, separately; next, studied obtained results could be ameliorated by combining fingerprint types. This showed that does not provide suitable classification models since several genuines generics classified as counterfeits vice versa. However, when analyzing MS1_MS2 combination with partial least squares-discriminant (PLS-DA), perfect discrimination obtained. When only using 254 good k nearest neighbors (kNN) soft independent modelling class analogy (SIMCA), might interesting drugs developing countries. general, (254 nm_MS1) preferred due less errors genuines/generics compared separately.