Article
Refine
Year of publication
- 2021 (69) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (29)
- IfB - Institut für Bioengineering (23)
- Fachbereich Luft- und Raumfahrttechnik (11)
- INB - Institut für Nano- und Biotechnologien (10)
- Fachbereich Chemie und Biotechnologie (8)
- Fachbereich Elektrotechnik und Informationstechnik (6)
- Fachbereich Wirtschaftswissenschaften (4)
- Fachbereich Bauingenieurwesen (3)
- ECSM European Center for Sustainable Mobility (2)
- IMP - Institut für Mikrowellen- und Plasmatechnik (2)
Has Fulltext
- no (69) (remove)
Language
- English (69) (remove)
Document Type
- Article (69) (remove)
Keywords
- Principal component analysis (2)
- capacitive field-effect sensor (2)
- constructive alignment (2)
- examination (2)
- harmonic radar (2)
- long-term retention (2)
- multimodal (2)
- practical learning (2)
- AlterG (1)
- Authenticity (1)
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.