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Tracing the origin of paracetamol tablets by near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy using principal component analysis and linear discriminant analysis

  • Most drugs are no longer produced in their own countries by the pharmaceutical companies, but by contract manufacturers or at manufacturing sites in countries that can produce more cheaply. This not only makes it difficult to trace them back but also leaves room for criminal organizations to fake them unnoticed. For these reasons, it is becoming increasingly difficult to determine the exact origin of drugs. The goal of this work was to investigate how exactly this is possible by using different spectroscopic methods like nuclear magnetic resonance and near- and mid-infrared spectroscopy in combination with multivariate data analysis. As an example, 56 out of 64 different paracetamol preparations, collected from 19 countries around the world, were chosen to investigate whether it is possible to determine the pharmaceutical company, manufacturing site, or country of origin. By means of suitable pre-processing of the spectra and the different information contained in each method, principal component analysis was able to evaluate manufacturing relationships between individual companies and to differentiate between production sites or formulations. Linear discriminant analysis showed different results depending on the spectral method and purpose. For all spectroscopic methods, it was found that the classification of the preparations to their manufacturer achieves better results than the classification to their pharmaceutical company. The best results were obtained with nuclear magnetic resonance and near-infrared data, with 94.6%/99.6% and 98.7/100% of the spectra of the preparations correctly assigned to their pharmaceutical company or manufacturer.

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Metadaten
Author:Alexander Becht, Curd Schollmayer, Yulia MonakhovaORCiD, Ulrike Holzgrabe
DOI:https://doi.org/10.1007/s00216-021-03249-z
ISSN:1618-2650
Parent Title (English):Analytical and Bioanalytical Chemistry
Publisher:Springer Nature
Document Type:Article
Language:English
Year of Completion:2021
Date of the Publication (Server):2021/04/15
Tag:IR; Linear discriminant analysis; Manufacturer; Principal component analysis
Volume:413
First Page:3107
Last Page:3118
Link:https://doi.org/10.1007/s00216-021-03249-z
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Chemie und Biotechnologie
collections:Verlag / Springer Nature
Open Access / Hybrid
Licence (German):License LogoCreative Commons - Namensnennung