Thesis - Open Access
Master of Science (MS)
Department / School
Chemistry and Biochemistry
Brian A. Logue
Aspirin, Atypicality, Counterfeit pharmaceuticals, Linear discriminant analysis
Counterfeit pharmaceuticals pose a threat to society that can include inaccurate amounts of the active pharmaceutical ingredient (API), no API, or containing off-target compounds. For example, there are many recent examples of counterfeit pharmaceuticals containing potentially lethal doses (> 2 mg) of fentanyl (i.e., a synthetic opioid). Current measures to combat illicit pharmaceuticals (e.g., unique packaging and product serialization) have merit, however with evolved technologies, counterfeiters can relatively easily simulate these measures and continue to distribute illicit pharmaceuticals. The only accurate way to definitively determine that a suspected counterfeit is, in fact, counterfeit is advanced chemical analysis. However, current methods of authentication via chemical analysis have disadvantages. Therefore, a general drug authentication method was developed to authenticate and correctly classify pharmaceuticals, specifically Bayer®, Walgreens©, and Premier Value® aspirin. Gaschromatography mass-spectrometry (GC-MS) and liquid-chromatography tandem mass spectrometry (LC-MS/MS) were evaluated for analysis of aspirin. LC-MS/MS produced the most consistent analysis results. Additionally, three statistical techniques, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and atypicality analysis, were evaluated for their usefulness in source attribution. LDA outperformed the other statistical treatments, with perfect classification of the training data set using LDA. However, when applying the method to a set of double-blinded pills, all statistical treatments failed to correctly classify over 25% of the pills. Because this method of source attribution was inconsistent, further optimization of the method is needed before introducing unknown sources.
Library of Congress Subject Headings
Number of Pages
South Dakota State University
Guetzloff, Megan, "Development of a Chromatographic Method to Authenticate Aspirin Brands" (2022). Electronic Theses and Dissertations. 338.