Counterfeit Detection

Agata Sroka/ August 13, 2020/ Uncategorized

aikemy provides patients with a convenient way of ensuring their medicines are genuine. 

 According to WHO, even 1 in 10 medicines is falsified. Counterfeit products often look identical to their authentic versions, but contain too little or too much of the active ingredient, or are made with toxic substances. Patients who receive the falsified medicines believe that they on therapy, but this is not true. Even worse, they are at risk of severe illness or death. Counterfeiting is illegal, but difficult to recognize, particularly for patients. The spread of the problem is the largest in online pharmacies. 

The revolutionary Pocket Lab can confirm medicines authenticity by looking at their unique chemical make-up. A simple scan using the device supplied by AMS and the pocket lab can tell what it is, the composition, and detect potential safety hazards. The answer to your question pops up on your mobile phone.

True Blue Project Success

 We have successfully been able to detect falsified medicines with a backed-by-data close to 100% success rate of correct answers in a peer-reviewed publication. The technology behind the Pocket Lab is the miniaturized Near-Infrared Spectroscopy. 

Abstract

The miniaturized Near-Infrared Spectroscopy (NIRS) enables convenient, non-destructive, and real-time testing of medicines circulating on the field or throughout the supply chain. We have successfully been able to detect falsified Viagra using a new low-cost, consumer-type NIRS. In like manner, we have distinguished the original Viagra from its generic versions with the same dose. This innovation is a promising way of ensuring medicines authenticity by looking at their chemical ‘fingerprints’. Portable screening technologies for medicine quality assurance on the field or throughout the supply chain are currently growing and complementing the current approach for combating the falsified products, i.e., safety features on the packaging and laboratory analysis of suspect samples. In total, 78 spectra of Sildenafil-based tablets were recorded and classified using the K-Nearest Neighbors algorithm that relies on the Euclidean distances between measured values. The accuracy of our model was assessed using cross-validation and bootstrapping techniques. As a result, the miniaturized NIRS correctly ordered all tablets according to their manufacturer and indicated the falsified tablets. The spectra differed in shape from the authentic versions and show the high spread in chemo-physical characteristics, which suggest poor manufacturing practices of the falsified products.   

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