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United States Department of Agriculture

Agricultural Research Service

Title: Detecting Counterfeit Antimalarial Tablets by Near-Infrared Spectroscopy

Authors
item Dowell, Floyd
item Maghirang, Elizabeth
item Fernandez, Facundo - GEORGIA INSTITUTE OF TECH
item Newton, Paul - UNIV OF OXFORD, UK
item Green, Michael - CNTRS FOR DISEASE CONTROL

Submitted to: Journal of Pharmaceutical and Biomedical Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 27, 2008
Publication Date: July 6, 2008
Repository URL: http://www.ars.usda.gov/SP2UserFiles/Place/54300520/405Antimalarialtabletnirpaper.pdf
Citation: Dowell, F.E., Maghirang, E.B., Fernandez, F.M., Newton, P.N., Green, M.D. 2008. Detecting Counterfeit Antimalarial Tablets by Near-Infrared Spectroscopy. Journal of Pharmaceutical and Biomedical Analysis. 48:1011-1014. DOI: 10.1016/j.jpba.2008.06.024.

Interpretive Summary: Counterfeit antimalarial drugs are found in as many as 50% of tablets in some developing countries. However, it is difficult to differentiate between genuine and fakes due to their increasing sophistication. We tested the application of near-infrared spectroscopy (NIRS) for discriminating between counterfeit and genuine artesunate antimalarial tablets. Using this rapid technique we found that antimalarial tablets could be identified as genuine or counterfeit with 100% accuracy. This NIR technique can be field-portable and requires little training after calibrations are developed, thus showing great promise for rapid and accurate fake detection.

Technical Abstract: Counterfeit antimalarial drugs are found in many developing countries, but it is challenging to differentiate between genuine and fakes due to their increasing sophistication. Near-infrared spectroscopy (NIRS) is a powerful tool in pharmaceutical forensics, and we tested this technique for discriminating between counterfeit and genuine artesunate antimalarial tablets. Using NIRS, we found that artesunate tablets could be identified as genuine or counterfeit with 100% accuracy. Multivariate classification models indicated that this discriminatory ability was based, at least partly, on the presence or absence of spectral signatures related to artesunate. This technique can be field-portable and requires little training after calibrations are developed, thus showing great promise for rapid and accurate fake detection.

Last Modified: 8/21/2014
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