Abstract

This paper proposes a novel method for selecting subsets of wavenumbers provided by attenuated total reflectance by Fourier transform infrared (ATR-FTIR) spectroscopy able to improve the clustering of medicine samples into two groups; i.e., authentic or fraudulent. For that matter, we apply principal components analysis (PCA) to ATR-FTIR data, and derive two variable importance indices from the PCA parameters. Next, an iterative variable (i.e. wavenumbers) elimination procedure and sample clustering through k-means and Fuzzy C-means techniques are carried out; clustering performance is assessed by the Silhouette Index (SI). The performance of the proposed method is compared with a greedy variable selection method, the "leave one variable out at a time" approach, in terms of clustering quality, percent of retained variables, and computational time. When applied to Viagra ATR-FTIR data, our propositions increased the average SI from 0.5307 to 0.8603 using 0.61% of the original 661 wavenumbers; as for Cialis ATR-FTIR data, clustering quality increased from 0.7548 to 0.8681 when 1.21% of the original wavenumbers were retained in the procedure. The retained wavenumbers, located in the 1091-1046 cm(-1) region, comprise the lactose typically hailed as key substance to discriminate between authentic and counterfeit samples. (C) 2014 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.