Counterfeit medicines pose a huge threat to public health worldwide. High amounts of counterfeit pharmaceuticals enter the European market and therefore detection of these products is essential. Attenuated Total Reflection Fourier-Transform infrared spectroscopy (ATR-FTIR) might be useful for the screening of counterfeit medicines since it is easy to use and little sample preparation is required. Furthermore, this approach might be helpful to customs to obtain a first evaluation of suspected samples. This study proposes a combination of ATR-FTIR and chemometrics to discriminate and classify counterfeit medicines. A sample set, containing 209 samples in total, was analyzed using ATR-FTIR and the obtained spectra were used as fingerprints in the chemometric data-analysis which included Principal Component Analysis (PCA), k-Nearest Neighbours (k-NN), Classification and Regression Trees (CART) and Soft Independent Modelling of Class Analogy (SIMCA). First it was verified whether the mentioned techniques are capable to distinguish samples containing different active pharmaceutical ingredients (APIs). PCA showed a clear tendency of discrimination based on the API present; k-NN, CART and SIMCA were capable to create suitable prediction models based on the presence of different APIs. However k-NN performs the least while SIMCA performs the best. Secondly, it was tested whether these three models could be expanded to discriminate between genuine and counterfeit samples as well. k-NN was not able to make the desired discrimination and therefore it was not useful. CART performed better but also this model was less suited. SIMCA, on the other hand, resulted in a model with a 100% correct discrimination between genuine and counterfeit drugs. This study shows that chemometric analysis of ATR-FTIR fingerprints is a valuable tool to discriminate genuine from counterfeit samples and to classify counterfeit medicines.

More and more events, such as the summer music festivals, are considering the possibilities for implementing on‐site testing of psychoactive drugs in the context of prevention and harm reduction. Although the on‐site identification is already implemented by plenty of drug checking services, the required rapid quantitative dosing of the composition of illicit substances is still a missing aspect for a successful harm reduction strategy at events. In this paper, an approach is presented to identify white powders as amphetamine, cocaine, ketamine or others and to estimate the purity of the amphetamine, cocaine and ketamine samples using spectroscopic techniques hyphenated with partial least squares (PLS) modelling. For identification purposes, it was observed that mid‐infrared spectroscopy hyphenated with PLS‐discriminant analysis allowed the distinction between amphetamine, cocaine, ketamine and other samples and this with a correct classification rate of 93.1% for an external test set. For quantitative estimation, near‐infrared spectroscopy was more performant and allowed the estimation of the dosage/purity of the amphetamine, cocaine and ketamine samples with an error of more or less 10% w/w. An easily applicable, practical and cost‐effective approach for on‐site characterisation of the majority of the psychoactive samples encountered in Belgian nightlife settings based on IR spectroscopy was proposed.

Counterfeit medicines are a global threat to public health. High amounts enter the European market, enforcing the need for simple techniques to help customs detect these pharmaceuticals. This study focused on physical profiling and IR spectroscopy to obtain a prime discrimination between genuine and illegal Viagra® and Cialis® medicines. Five post-tableting characteristics were explored: colour, mass, long length, short length, and thickness. Hypothesis testing showed that most illegal samples (between 60 and 100%) significantly differ from the genuine medicines, in particular for mass and long length. Classification and Regression Trees (CART) analysis resulted in a good discrimination between genuine and illegal medicines (98.93% correct classification rate for Viagra®, 99.42% for Cialis®). Moreover, CART confirmed the observation that mass and long length are the key physical characteristics which determine the observed discrimination. IR analysis was performed on tablets without blister and on tablets in intact blister. These data were analyzed using Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares - Discriminant Analysis (PLS-DA). Supervised techniques needed to be applied since Principal Component Analysis (PCA) was not able to generate the desired discrimination. Our study shows that a perfect discrimination between genuine and illegal medicines can be made by both SIMCA and PLS-DA without removing the tablets from the blister. This approach has the advantage of keeping the blister intact. Our study demonstrates that these user friendly techniques are reliable methods to aid customs to obtain a prime distinction between genuine and illegal samples on the spot. Copyright © 2015 John Wiley & Sons, Ltd.

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